teaching strategies a research paper

• Tutorial Review
• Open access
• Published: 24 January 2018

Teaching the science of learning

• Yana Weinstein   ORCID: orcid.org/0000-0002-5144-968X 1 ,
• Christopher R. Madan 2 , 3 &
• Megan A. Sumeracki 4  

Cognitive Research: Principles and Implications volume  3 , Article number:  2 ( 2018 ) Cite this article

273k Accesses

101 Citations

757 Altmetric

Metrics details

The science of learning has made a considerable contribution to our understanding of effective teaching and learning strategies. However, few instructors outside of the field are privy to this research. In this tutorial review, we focus on six specific cognitive strategies that have received robust support from decades of research: spaced practice, interleaving, retrieval practice, elaboration, concrete examples, and dual coding. We describe the basic research behind each strategy and relevant applied research, present examples of existing and suggested implementation, and make recommendations for further research that would broaden the reach of these strategies.

Significance

Education does not currently adhere to the medical model of evidence-based practice (Roediger, 2013 ). However, over the past few decades, our field has made significant advances in applying cognitive processes to education. From this work, specific recommendations can be made for students to maximize their learning efficiency (Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013 ; Roediger, Finn, & Weinstein, 2012 ). In particular, a review published 10 years ago identified a limited number of study techniques that have received solid evidence from multiple replications testing their effectiveness in and out of the classroom (Pashler et al., 2007 ). A recent textbook analysis (Pomerance, Greenberg, & Walsh, 2016 ) took the six key learning strategies from this report by Pashler and colleagues, and found that very few teacher-training textbooks cover any of these six principles – and none cover them all, suggesting that these strategies are not systematically making their way into the classroom. This is the case in spite of multiple recent academic (e.g., Dunlosky et al., 2013 ) and general audience (e.g., Dunlosky, 2013 ) publications about these strategies. In this tutorial review, we present the basic science behind each of these six key principles, along with more recent research on their effectiveness in live classrooms, and suggest ideas for pedagogical implementation. The target audience of this review is (a) educators who might be interested in integrating the strategies into their teaching practice, (b) science of learning researchers who are looking for open questions to help determine future research priorities, and (c) researchers in other subfields who are interested in the ways that principles from cognitive psychology have been applied to education.

While the typical teacher may not be exposed to this research during teacher training, a small cohort of teachers intensely interested in cognitive psychology has recently emerged. These teachers are mainly based in the UK, and, anecdotally (e.g., Dennis (2016), personal communication), appear to have taken an interest in the science of learning after reading Make it Stick (Brown, Roediger, & McDaniel, 2014 ; see Clark ( 2016 ) for an enthusiastic review of this book on a teacher’s blog, and “Learning Scientists” ( 2016c ) for a collection). In addition, a grassroots teacher movement has led to the creation of “researchED” – a series of conferences on evidence-based education (researchED, 2013 ). The teachers who form part of this network frequently discuss cognitive psychology techniques and their applications to education on social media (mainly Twitter; e.g., Fordham, 2016 ; Penfound, 2016 ) and on their blogs, such as Evidence Into Practice ( https://evidenceintopractice.wordpress.com/ ), My Learning Journey ( http://reflectionsofmyteaching.blogspot.com/ ), and The Effortful Educator ( https://theeffortfuleducator.com/ ). In general, the teachers who write about these issues pay careful attention to the relevant literature, often citing some of the work described in this review.

These informal writings, while allowing teachers to explore their approach to teaching practice (Luehmann, 2008 ), give us a unique window into the application of the science of learning to the classroom. By examining these blogs, we can not only observe how basic cognitive research is being applied in the classroom by teachers who are reading it, but also how it is being misapplied, and what questions teachers may be posing that have gone unaddressed in the scientific literature. Throughout this review, we illustrate each strategy with examples of how it can be implemented (see Table  1 and Figs.  1 , 2 , 3 , 4 , 5 , 6 and 7 ), as well as with relevant teacher blog posts that reflect on its application, and draw upon this work to pin-point fruitful avenues for further basic and applied research.

Spaced practice schedule for one week. This schedule is designed to represent a typical timetable of a high-school student. The schedule includes four one-hour study sessions, one longer study session on the weekend, and one rest day. Notice that each subject is studied one day after it is covered in school, to create spacing between classes and study sessions. Copyright note: this image was produced by the authors

a Blocked practice and interleaved practice with fraction problems. In the blocked version, students answer four multiplication problems consecutively. In the interleaved version, students answer a multiplication problem followed by a division problem and then an addition problem, before returning to multiplication. For an experiment with a similar setup, see Patel et al. ( 2016 ). Copyright note: this image was produced by the authors. b Illustration of interleaving and spacing. Each color represents a different homework topic. Interleaving involves alternating between topics, rather than blocking. Spacing involves distributing practice over time, rather than massing. Interleaving inherently involves spacing as other tasks naturally “fill” the spaces between interleaved sessions. Copyright note: this image was produced by the authors, adapted from Rohrer ( 2012 )

Concept map illustrating the process and resulting benefits of retrieval practice. Retrieval practice involves the process of withdrawing learned information from long-term memory into working memory, which requires effort. This produces direct benefits via the consolidation of learned information, making it easier to remember later and causing improvements in memory, transfer, and inferences. Retrieval practice also produces indirect benefits of feedback to students and teachers, which in turn can lead to more effective study and teaching practices, with a focus on information that was not accurately retrieved. Copyright note: this figure originally appeared in a blog post by the first and third authors ( http://www.learningscientists.org/blog/2016/4/1-1 )

Illustration of “how” and “why” questions (i.e., elaborative interrogation questions) students might ask while studying the physics of flight. To help figure out how physics explains flight, students might ask themselves the following questions: “How does a plane take off?”; “Why does a plane need an engine?”; “How does the upward force (lift) work?”; “Why do the wings have a curved upper surface and a flat lower surface?”; and “Why is there a downwash behind the wings?”. Copyright note: the image of the plane was downloaded from Pixabay.com and is free to use, modify, and share

Three examples of physics problems that would be categorized differently by novices and experts. The problems in ( a ) and ( c ) look similar on the surface, so novices would group them together into one category. Experts, however, will recognize that the problems in ( b ) and ( c ) both relate to the principle of energy conservation, and so will group those two problems into one category instead. Copyright note: the figure was produced by the authors, based on figures in Chi et al. ( 1981 )

Example of how to enhance learning through use of a visual example. Students might view this visual representation of neural communications with the words provided, or they could draw a similar visual representation themselves. Copyright note: this figure was produced by the authors

Example of word properties associated with visual, verbal, and motor coding for the word “SPOON”. A word can evoke multiple types of representation (“codes” in dual coding theory). Viewing a word will automatically evoke verbal representations related to its component letters and phonemes. Words representing objects (i.e., concrete nouns) will also evoke visual representations, including information about similar objects, component parts of the object, and information about where the object is typically found. In some cases, additional codes can also be evoked, such as motor-related properties of the represented object, where contextual information related to the object’s functional intention and manipulation action may also be processed automatically when reading the word. Copyright note: this figure was produced by the authors and is based on Aylwin ( 1990 ; Fig.  2 ) and Madan and Singhal ( 2012a , Fig.  3 )

Spaced practice

The benefits of spaced (or distributed) practice to learning are arguably one of the strongest contributions that cognitive psychology has made to education (Kang, 2016 ). The effect is simple: the same amount of repeated studying of the same information spaced out over time will lead to greater retention of that information in the long run, compared with repeated studying of the same information for the same amount of time in one study session. The benefits of distributed practice were first empirically demonstrated in the 19 th century. As part of his extensive investigation into his own memory, Ebbinghaus ( 1885/1913 ) found that when he spaced out repetitions across 3 days, he could almost halve the number of repetitions necessary to relearn a series of 12 syllables in one day (Chapter 8). He thus concluded that “a suitable distribution of [repetitions] over a space of time is decidedly more advantageous than the massing of them at a single time” (Section 34). For those who want to read more about Ebbinghaus’s contribution to memory research, Roediger ( 1985 ) provides an excellent summary.

Since then, hundreds of studies have examined spacing effects both in the laboratory and in the classroom (Kang, 2016 ). Spaced practice appears to be particularly useful at large retention intervals: in the meta-analysis by Cepeda, Pashler, Vul, Wixted, and Rohrer ( 2006 ), all studies with a retention interval longer than a month showed a clear benefit of distributed practice. The “new theory of disuse” (Bjork & Bjork, 1992 ) provides a helpful mechanistic explanation for the benefits of spacing to learning. This theory posits that memories have both retrieval strength and storage strength. Whereas retrieval strength is thought to measure the ease with which a memory can be recalled at a given moment, storage strength (which cannot be measured directly) represents the extent to which a memory is truly embedded in the mind. When studying is taking place, both retrieval strength and storage strength receive a boost. However, the extent to which storage strength is boosted depends upon retrieval strength, and the relationship is negative: the greater the current retrieval strength, the smaller the gains in storage strength. Thus, the information learned through “cramming” will be rapidly forgotten due to high retrieval strength and low storage strength (Bjork & Bjork, 2011 ), whereas spacing out learning increases storage strength by allowing retrieval strength to wane before restudy.

Teachers can introduce spacing to their students in two broad ways. One involves creating opportunities to revisit information throughout the semester, or even in future semesters. This does involve some up-front planning, and can be difficult to achieve, given time constraints and the need to cover a set curriculum. However, spacing can be achieved with no great costs if teachers set aside a few minutes per class to review information from previous lessons. The second method involves putting the onus to space on the students themselves. Of course, this would work best with older students – high school and above. Because spacing requires advance planning, it is crucial that the teacher helps students plan their studying. For example, teachers could suggest that students schedule study sessions on days that alternate with the days on which a particular class meets (e.g., schedule review sessions for Tuesday and Thursday when the class meets Monday and Wednesday; see Fig.  1 for a more complete weekly spaced practice schedule). It important to note that the spacing effect refers to information that is repeated multiple times, rather than the idea of studying different material in one long session versus spaced out in small study sessions over time. However, for teachers and particularly for students planning a study schedule, the subtle difference between the two situations (spacing out restudy opportunities, versus spacing out studying of different information over time) may be lost. Future research should address the effects of spacing out studying of different information over time, whether the same considerations apply in this situation as compared to spacing out restudy opportunities, and how important it is for teachers and students to understand the difference between these two types of spaced practice.

It is important to note that students may feel less confident when they space their learning (Bjork, 1999 ) than when they cram. This is because spaced learning is harder – but it is this “desirable difficulty” that helps learning in the long term (Bjork, 1994 ). Students tend to cram for exams rather than space out their learning. One explanation for this is that cramming does “work”, if the goal is only to pass an exam. In order to change students’ minds about how they schedule their studying, it might be important to emphasize the value of retaining information beyond a final exam in one course.

Ideas for how to apply spaced practice in teaching have appeared in numerous teacher blogs (e.g., Fawcett, 2013 ; Kraft, 2015 ; Picciotto, 2009 ). In England in particular, as of 2013, high-school students need to be able to remember content from up to 3 years back on cumulative exams (General Certificate of Secondary Education (GCSE) and A-level exams; see CIFE, 2012 ). A-levels in particular determine what subject students study in university and which programs they are accepted into, and thus shape the path of their academic career. A common approach for dealing with these exams has been to include a “revision” (i.e., studying or cramming) period of a few weeks leading up to the high-stakes cumulative exams. Now, teachers who follow cognitive psychology are advocating a shift of priorities to spacing learning over time across the 3 years, rather than teaching a topic once and then intensely reviewing it weeks before the exam (Cox, 2016a ; Wood, 2017 ). For example, some teachers have suggested using homework assignments as an opportunity for spaced practice by giving students homework on previous topics (Rose, 2014 ). However, questions remain, such as whether spaced practice can ever be effective enough to completely alleviate the need or utility of a cramming period (Cox, 2016b ), and how one can possibly figure out the optimal lag for spacing (Benney, 2016 ; Firth, 2016 ).

There has been considerable research on the question of optimal lag, and much of it is quite complex; two sessions neither too close together (i.e., cramming) nor too far apart are ideal for retention. In a large-scale study, Cepeda, Vul, Rohrer, Wixted, and Pashler ( 2008 ) examined the effects of the gap between study sessions and the interval between study and test across long periods, and found that the optimal gap between study sessions was contingent on the retention interval. Thus, it is not clear how teachers can apply the complex findings on lag to their own classrooms.

A useful avenue of research would be to simplify the research paradigms that are used to study optimal lag, with the goal of creating a flexible, spaced-practice framework that teachers could apply and tailor to their own teaching needs. For example, an Excel macro spreadsheet was recently produced to help teachers plan for lagged lessons (Weinstein-Jones & Weinstein, 2017 ; see Weinstein & Weinstein-Jones ( 2017 ) for a description of the algorithm used in the spreadsheet), and has been used by teachers to plan their lessons (Penfound, 2017 ). However, one teacher who found this tool helpful also wondered whether the more sophisticated plan was any better than his own method of manually selecting poorly understood material from previous classes for later review (Lovell, 2017 ). This direction is being actively explored within personalized online learning environments (Kornell & Finn, 2016 ; Lindsey, Shroyer, Pashler, & Mozer, 2014 ), but teachers in physical classrooms might need less technologically-driven solutions to teach cohorts of students.

It seems teachers would greatly appreciate a set of guidelines for how to implement spacing in the curriculum in the most effective, but also the most efficient manner. While the cognitive field has made great advances in terms of understanding the mechanisms behind spacing, what teachers need more of are concrete evidence-based tools and guidelines for direct implementation in the classroom. These could include more sophisticated and experimentally tested versions of the software described above (Weinstein-Jones & Weinstein, 2017 ), or adaptable templates of spaced curricula. Moreover, researchers need to evaluate the effectiveness of these tools in a real classroom environment, over a semester or academic year, in order to give pedagogically relevant evidence-based recommendations to teachers.

Interleaving

Another scheduling technique that has been shown to increase learning is interleaving. Interleaving occurs when different ideas or problem types are tackled in a sequence, as opposed to the more common method of attempting multiple versions of the same problem in a given study session (known as blocking). Interleaving as a principle can be applied in many different ways. One such way involves interleaving different types of problems during learning, which is particularly applicable to subjects such as math and physics (see Fig.  2 a for an example with fractions, based on a study by Patel, Liu, & Koedinger, 2016 ). For example, in a study with college students, Rohrer and Taylor ( 2007 ) found that shuffling math problems that involved calculating the volume of different shapes resulted in better test performance 1 week later than when students answered multiple problems about the same type of shape in a row. This pattern of results has also been replicated with younger students, for example 7 th grade students learning to solve graph and slope problems (Rohrer, Dedrick, & Stershic, 2015 ). The proposed explanation for the benefit of interleaving is that switching between different problem types allows students to acquire the ability to choose the right method for solving different types of problems rather than learning only the method itself, and not when to apply it.

Do the benefits of interleaving extend beyond problem solving? The answer appears to be yes. Interleaving can be helpful in other situations that require discrimination, such as inductive learning. Kornell and Bjork ( 2008 ) examined the effects of interleaving in a task that might be pertinent to a student of the history of art: the ability to match paintings to their respective painters. Students who studied different painters’ paintings interleaved at study were more successful on a later identification test than were participants who studied the paintings blocked by painter. Birnbaum, Kornell, Bjork, and Bjork ( 2013 ) proposed the discriminative-contrast hypothesis to explain that interleaving enhances learning by allowing the comparison between exemplars of different categories. They found support for this hypothesis in a set of experiments with bird categorization: participants benefited from interleaving and also from spacing, but not when the spacing interrupted side-by-side comparisons of birds from different categories.

Another type of interleaving involves the interleaving of study and test opportunities. This type of interleaving has been applied, once again, to problem solving, whereby students alternate between attempting a problem and viewing a worked example (Trafton & Reiser, 1993 ); this pattern appears to be superior to answering a string of problems in a row, at least with respect to the amount of time it takes to achieve mastery of a procedure (Corbett, Reed, Hoffmann, MacLaren, & Wagner, 2010 ). The benefits of interleaving study and test opportunities – rather than blocking study followed by attempting to answer problems or questions – might arise due to a process known as “test-potentiated learning”. That is, a study opportunity that immediately follows a retrieval attempt may be more fruitful than when that same studying was not preceded by retrieval (Arnold & McDermott, 2013 ).

For problem-based subjects, the interleaving technique is straightforward: simply mix questions on homework and quizzes with previous materials (which takes care of spacing as well); for languages, mix vocabulary themes rather than blocking by theme (Thomson & Mehring, 2016 ). But interleaving as an educational strategy ought to be presented to teachers with some caveats. Research has focused on interleaving material that is somewhat related (e.g., solving different mathematical equations, Rohrer et al., 2015 ), whereas students sometimes ask whether they should interleave material from different subjects – a practice that has not received empirical support (Hausman & Kornell, 2014 ). When advising students how to study independently, teachers should thus proceed with caution. Since it is easy for younger students to confuse this type of unhelpful interleaving with the more helpful interleaving of related information, it may be best for teachers of younger grades to create opportunities for interleaving in homework and quiz assignments rather than putting the onus on the students themselves to make use of the technique. Technology can be very helpful here, with apps such as Quizlet, Memrise, Anki, Synap, Quiz Champ, and many others (see also “Learning Scientists”, 2017 ) that not only allow instructor-created quizzes to be taken by students, but also provide built-in interleaving algorithms so that the burden does not fall on the teacher or the student to carefully plan which items are interleaved when.

An important point to consider is that in educational practice, the distinction between spacing and interleaving can be difficult to delineate. The gap between the scientific and classroom definitions of interleaving is demonstrated by teachers’ own writings about this technique. When they write about interleaving, teachers often extend the term to connote a curriculum that involves returning to topics multiple times throughout the year (e.g., Kirby, 2014 ; see “Learning Scientists” ( 2016a ) for a collection of similar blog posts by several other teachers). The “interleaving” of topics throughout the curriculum produces an effect that is more akin to what cognitive psychologists call “spacing” (see Fig.  2 b for a visual representation of the difference between interleaving and spacing). However, cognitive psychologists have not examined the effects of structuring the curriculum in this way, and open questions remain: does repeatedly circling back to previous topics throughout the semester interrupt the learning of new information? What are some effective techniques for interleaving old and new information within one class? And how does one determine the balance between old and new information?

Retrieval practice

While tests are most often used in educational settings for assessment, a lesser-known benefit of tests is that they actually improve memory of the tested information. If we think of our memories as libraries of information, then it may seem surprising that retrieval (which happens when we take a test) improves memory; however, we know from a century of research that retrieving knowledge actually strengthens it (see Karpicke, Lehman, & Aue, 2014 ). Testing was shown to strengthen memory as early as 100 years ago (Gates, 1917 ), and there has been a surge of research in the last decade on the mnemonic benefits of testing, or retrieval practice . Most of the research on the effectiveness of retrieval practice has been done with college students (see Roediger & Karpicke, 2006 ; Roediger, Putnam, & Smith, 2011 ), but retrieval-based learning has been shown to be effective at producing learning for a wide range of ages, including preschoolers (Fritz, Morris, Nolan, & Singleton, 2007 ), elementary-aged children (e.g., Karpicke, Blunt, & Smith, 2016 ; Karpicke, Blunt, Smith, & Karpicke, 2014 ; Lipko-Speed, Dunlosky, & Rawson, 2014 ; Marsh, Fazio, & Goswick, 2012 ; Ritchie, Della Sala, & McIntosh, 2013 ), middle-school students (e.g., McDaniel, Thomas, Agarwal, McDermott, & Roediger, 2013 ; McDermott, Agarwal, D’Antonio, Roediger, & McDaniel, 2014 ), and high-school students (e.g., McDermott et al., 2014 ). In addition, the effectiveness of retrieval-based learning has been extended beyond simple testing to other activities in which retrieval practice can be integrated, such as concept mapping (Blunt & Karpicke, 2014 ; Karpicke, Blunt, et al., 2014 ; Ritchie et al., 2013 ).

A debate is currently ongoing as to the effectiveness of retrieval practice for more complex materials (Karpicke & Aue, 2015 ; Roelle & Berthold, 2017 ; Van Gog & Sweller, 2015 ). Practicing retrieval has been shown to improve the application of knowledge to new situations (e.g., Butler, 2010 ; Dirkx, Kester, & Kirschner, 2014 ); McDaniel et al., 2013 ; Smith, Blunt, Whiffen, & Karpicke, 2016 ); but see Tran, Rohrer, and Pashler ( 2015 ) and Wooldridge, Bugg, McDaniel, and Liu ( 2014 ), for retrieval practice studies that showed limited or no increased transfer compared to restudy. Retrieval practice effects on higher-order learning may be more sensitive than fact learning to encoding factors, such as the way material is presented during study (Eglington & Kang, 2016 ). In addition, retrieval practice may be more beneficial for higher-order learning if it includes more scaffolding (Fiechter & Benjamin, 2017 ; but see Smith, Blunt, et al., 2016 ) and targeted practice with application questions (Son & Rivas, 2016 ).

How does retrieval practice help memory? Figure  3 illustrates both the direct and indirect benefits of retrieval practice identified by the literature. The act of retrieval itself is thought to strengthen memory (Karpicke, Blunt, et al., 2014 ; Roediger & Karpicke, 2006 ; Smith, Roediger, & Karpicke, 2013 ). For example, Smith et al. ( 2013 ) showed that if students brought information to mind without actually producing it (covert retrieval), they remembered the information just as well as if they overtly produced the retrieved information (overt retrieval). Importantly, both overt and covert retrieval practice improved memory over control groups without retrieval practice, even when feedback was not provided. The fact that bringing information to mind in the absence of feedback or restudy opportunities improves memory leads researchers to conclude that it is the act of retrieval – thinking back to bring information to mind – that improves memory of that information.

The benefit of retrieval practice depends to a certain extent on successful retrieval (see Karpicke, Lehman, et al., 2014 ). For example, in Experiment 4 of Smith et al. ( 2013 ), students successfully retrieved 72% of the information during retrieval practice. Of course, retrieving 72% of the information was compared to a restudy control group, during which students were re-exposed to 100% of the information, creating a bias in favor of the restudy condition. Yet retrieval led to superior memory later compared to the restudy control. However, if retrieval success is extremely low, then it is unlikely to improve memory (e.g., Karpicke, Blunt, et al., 2014 ), particularly in the absence of feedback. On the other hand, if retrieval-based learning situations are constructed in such a way that ensures high levels of success, the act of bringing the information to mind may be undermined, thus making it less beneficial. For example, if a student reads a sentence and then immediately covers the sentence and recites it out loud, they are likely not retrieving the information but rather just keeping the information in their working memory long enough to recite it again (see Smith, Blunt, et al., 2016 for a discussion of this point). Thus, it is important to balance success of retrieval with overall difficulty in retrieving the information (Smith & Karpicke, 2014 ; Weinstein, Nunes, & Karpicke, 2016 ). If initial retrieval success is low, then feedback can help improve the overall benefit of practicing retrieval (Kang, McDermott, & Roediger, 2007 ; Smith & Karpicke, 2014 ). Kornell, Klein, and Rawson ( 2015 ), however, found that it was the retrieval attempt and not the correct production of information that produced the retrieval practice benefit – as long as the correct answer was provided after an unsuccessful attempt, the benefit was the same as for a successful retrieval attempt in this set of studies. From a practical perspective, it would be helpful for teachers to know when retrieval attempts in the absence of success are helpful, and when they are not. There may also be additional reasons beyond retrieval benefits that would push teachers towards retrieval practice activities that produce some success amongst students; for example, teachers may hesitate to give students retrieval practice exercises that are too difficult, as this may negatively affect self-efficacy and confidence.

In addition to the fact that bringing information to mind directly improves memory for that information, engaging in retrieval practice can produce indirect benefits as well (see Roediger et al., 2011 ). For example, research by Weinstein, Gilmore, Szpunar, and McDermott ( 2014 ) demonstrated that when students expected to be tested, the increased test expectancy led to better-quality encoding of new information. Frequent testing can also serve to decrease mind-wandering – that is, thoughts that are unrelated to the material that students are supposed to be studying (Szpunar, Khan, & Schacter, 2013 ).

Practicing retrieval is a powerful way to improve meaningful learning of information, and it is relatively easy to implement in the classroom. For example, requiring students to practice retrieval can be as simple as asking students to put their class materials away and try to write out everything they know about a topic. Retrieval-based learning strategies are also flexible. Instructors can give students practice tests (e.g., short-answer or multiple-choice, see Smith & Karpicke, 2014 ), provide open-ended prompts for the students to recall information (e.g., Smith, Blunt, et al., 2016 ) or ask their students to create concept maps from memory (e.g., Blunt & Karpicke, 2014 ). In one study, Weinstein et al. ( 2016 ) looked at the effectiveness of inserting simple short-answer questions into online learning modules to see whether they improved student performance. Weinstein and colleagues also manipulated the placement of the questions. For some students, the questions were interspersed throughout the module, and for other students the questions were all presented at the end of the module. Initial success on the short-answer questions was higher when the questions were interspersed throughout the module. However, on a later test of learning from that module, the original placement of the questions in the module did not matter for performance. As with spaced practice, where the optimal gap between study sessions is contingent on the retention interval, the optimum difficulty and level of success during retrieval practice may also depend on the retention interval. Both groups of students who answered questions performed better on the delayed test compared to a control group without question opportunities during the module. Thus, the important thing is for instructors to provide opportunities for retrieval practice during learning. Based on previous research, any activity that promotes the successful retrieval of information should improve learning.

Retrieval practice has received a lot of attention in teacher blogs (see “Learning Scientists” ( 2016b ) for a collection). A common theme seems to be an emphasis on low-stakes (Young, 2016 ) and even no-stakes (Cox, 2015 ) testing, the goal of which is to increase learning rather than assess performance. In fact, one well-known charter school in the UK has an official homework policy grounded in retrieval practice: students are to test themselves on subject knowledge for 30 minutes every day in lieu of standard homework (Michaela Community School, 2014 ). The utility of homework, particularly for younger children, is often a hotly debated topic outside of academia (e.g., Shumaker, 2016 ; but see Jones ( 2016 ) for an opposing viewpoint and Cooper ( 1989 ) for the original research the blog posts were based on). Whereas some research shows clear links between homework and academic achievement (Valle et al., 2016 ), other researchers have questioned the effectiveness of homework (Dettmers, Trautwein, & Lüdtke, 2009 ). Perhaps amending homework to involve retrieval practice might make it more effective; this remains an open empirical question.

One final consideration is that of test anxiety. While retrieval practice can be very powerful at improving memory, some research shows that pressure during retrieval can undermine some of the learning benefit. For example, Hinze and Rapp ( 2014 ) manipulated pressure during quizzing to create high-pressure and low-pressure conditions. On the quizzes themselves, students performed equally well. However, those in the high-pressure condition did not perform as well on a criterion test later compared to the low-pressure group. Thus, test anxiety may reduce the learning benefit of retrieval practice. Eliminating all high-pressure tests is probably not possible, but instructors can provide a number of low-stakes retrieval opportunities for students to help increase learning. The use of low-stakes testing can serve to decrease test anxiety (Khanna, 2015 ), and has recently been shown to negate the detrimental impact of stress on learning (Smith, Floerke, & Thomas, 2016 ). This is a particularly important line of inquiry to pursue for future research, because many teachers who are not familiar with the effectiveness of retrieval practice may be put off by the implied pressure of “testing”, which evokes the much maligned high-stakes standardized tests (e.g., McHugh, 2013 ).

Elaboration

Elaboration involves connecting new information to pre-existing knowledge. Anderson ( 1983 , p.285) made the following claim about elaboration: “One of the most potent manipulations that can be performed in terms of increasing a subject’s memory for material is to have the subject elaborate on the to-be-remembered material.” Postman ( 1976 , p. 28) defined elaboration most parsimoniously as “additions to nominal input”, and Hirshman ( 2001 , p. 4369) provided an elaboration on this definition (pun intended!), defining elaboration as “A conscious, intentional process that associates to-be-remembered information with other information in memory.” However, in practice, elaboration could mean many different things. The common thread in all the definitions is that elaboration involves adding features to an existing memory.

One possible instantiation of elaboration is thinking about information on a deeper level. The levels (or “depth”) of processing framework, proposed by Craik and Lockhart ( 1972 ), predicts that information will be remembered better if it is processed more deeply in terms of meaning, rather than shallowly in terms of form. The leves of processing framework has, however, received a number of criticisms (Craik, 2002 ). One major problem with this framework is that it is difficult to measure “depth”. And if we are not able to actually measure depth, then the argument can become circular: is it that something was remembered better because it was studied more deeply, or do we conclude that it must have been studied more deeply because it is remembered better? (See Lockhart & Craik, 1990 , for further discussion of this issue).

Another mechanism by which elaboration can confer a benefit to learning is via improvement in organization (Bellezza, Cheesman, & Reddy, 1977 ; Mandler, 1979 ). By this view, elaboration involves making information more integrated and organized with existing knowledge structures. By connecting and integrating the to-be-learned information with other concepts in memory, students can increase the extent to which the ideas are organized in their minds, and this increased organization presumably facilitates the reconstruction of the past at the time of retrieval.

Elaboration is such a broad term and can include so many different techniques that it is hard to claim that elaboration will always help learning. There is, however, a specific technique under the umbrella of elaboration for which there is relatively strong evidence in terms of effectiveness (Dunlosky et al., 2013 ; Pashler et al., 2007 ). This technique is called elaborative interrogation, and involves students questioning the materials that they are studying (Pressley, McDaniel, Turnure, Wood, & Ahmad, 1987 ). More specifically, students using this technique would ask “how” and “why” questions about the concepts they are studying (see Fig.  4 for an example on the physics of flight). Then, crucially, students would try to answer these questions – either from their materials or, eventually, from memory (McDaniel & Donnelly, 1996 ). The process of figuring out the answer to the questions – with some amount of uncertainty (Overoye & Storm, 2015 ) – can help learning. When using this technique, however, it is important that students check their answers with their materials or with the teacher; when the content generated through elaborative interrogation is poor, it can actually hurt learning (Clinton, Alibali, & Nathan, 2016 ).

Students can also be encouraged to self-explain concepts to themselves while learning (Chi, De Leeuw, Chiu, & LaVancher, 1994 ). This might involve students simply saying out loud what steps they need to perform to solve an equation. Aleven and Koedinger ( 2002 ) conducted two classroom studies in which students were either prompted by a “cognitive tutor” to provide self-explanations during a problem-solving task or not, and found that the self-explanations led to improved performance. According to the authors, this approach could scale well to real classrooms. If possible and relevant, students could even perform actions alongside their self-explanations (Cohen, 1981 ; see also the enactment effect, Hainselin, Picard, Manolli, Vankerkore-Candas, & Bourdin, 2017 ). Instructors can scaffold students in these types of activities by providing self-explanation prompts throughout to-be-learned material (O’Neil et al., 2014 ). Ultimately, the greatest potential benefit of accurate self-explanation or elaboration is that the student will be able to transfer their knowledge to a new situation (Rittle-Johnson, 2006 ).

The technical term “elaborative interrogation” has not made it into the vernacular of educational bloggers (a search on https://educationechochamberuncut.wordpress.com , which consolidates over 3,000 UK-based teacher blogs, yielded zero results for that term). However, a few teachers have blogged about elaboration more generally (e.g., Hobbiss, 2016 ) and deep questioning specifically (e.g., Class Teaching, 2013 ), just without using the specific terminology. This strategy in particular may benefit from a more open dialog between researchers and teachers to facilitate the use of elaborative interrogation in the classroom and to address possible barriers to implementation. In terms of advancing the scientific understanding of elaborative interrogation in a classroom setting, it would be informative to conduct a larger-scale intervention to see whether having students elaborate during reading actually helps their understanding. It would also be useful to know whether the students really need to generate their own elaborative interrogation (“how” and “why”) questions, versus answering questions provided by others. How long should students persist to find the answers? When is the right time to have students engage in this task, given the levels of expertise required to do it well (Clinton et al., 2016 )? Without knowing the answers to these questions, it may be too early for us to instruct teachers to use this technique in their classes. Finally, elaborative interrogation takes a long time. Is this time efficiently spent? Or, would it be better to have the students try to answer a few questions, pool their information as a class, and then move to practicing retrieval of the information?

Concrete examples

Providing supporting information can improve the learning of key ideas and concepts. Specifically, using concrete examples to supplement content that is more conceptual in nature can make the ideas easier to understand and remember. Concrete examples can provide several advantages to the learning process: (a) they can concisely convey information, (b) they can provide students with more concrete information that is easier to remember, and (c) they can take advantage of the superior memorability of pictures relative to words (see “Dual Coding”).

Words that are more concrete are both recognized and recalled better than abstract words (Gorman, 1961 ; e.g., “button” and “bound,” respectively). Furthermore, it has been demonstrated that information that is more concrete and imageable enhances the learning of associations, even with abstract content (Caplan & Madan, 2016 ; Madan, Glaholt, & Caplan, 2010 ; Paivio, 1971 ). Following from this, providing concrete examples during instruction should improve retention of related abstract concepts, rather than the concrete examples alone being remembered better. Concrete examples can be useful both during instruction and during practice problems. Having students actively explain how two examples are similar and encouraging them to extract the underlying structure on their own can also help with transfer. In a laboratory study, Berry ( 1983 ) demonstrated that students performed well when given concrete practice problems, regardless of the use of verbalization (akin to elaborative interrogation), but that verbalization helped students transfer understanding from concrete to abstract problems. One particularly important area of future research is determining how students can best make the link between concrete examples and abstract ideas.

Since abstract concepts are harder to grasp than concrete information (Paivio, Walsh, & Bons, 1994 ), it follows that teachers ought to illustrate abstract ideas with concrete examples. However, care must be taken when selecting the examples. LeFevre and Dixon ( 1986 ) provided students with both concrete examples and abstract instructions and found that when these were inconsistent, students followed the concrete examples rather than the abstract instructions, potentially constraining the application of the abstract concept being taught. Lew, Fukawa-Connelly, Mejí-Ramos, and Weber ( 2016 ) used an interview approach to examine why students may have difficulty understanding a lecture. Responses indicated that some issues were related to understanding the overarching topic rather than the component parts, and to the use of informal colloquialisms that did not clearly follow from the material being taught. Both of these issues could have potentially been addressed through the inclusion of a greater number of relevant concrete examples.

One concern with using concrete examples is that students might only remember the examples – especially if they are particularly memorable, such as fun or gimmicky examples – and will not be able to transfer their understanding from one example to another, or more broadly to the abstract concept. However, there does not seem to be any evidence that fun relevant examples actually hurt learning by harming memory for important information. Instead, fun examples and jokes tend to be more memorable, but this boost in memory for the joke does not seem to come at a cost to memory for the underlying concept (Baldassari & Kelley, 2012 ). However, two important caveats need to be highlighted. First, to the extent that the more memorable content is not relevant to the concepts of interest, learning of the target information can be compromised (Harp & Mayer, 1998 ). Thus, care must be taken to ensure that all examples and gimmicks are, in fact, related to the core concepts that the students need to acquire, and do not contain irrelevant perceptual features (Kaminski & Sloutsky, 2013 ).

The second issue is that novices often notice and remember the surface details of an example rather than the underlying structure. Experts, on the other hand, can extract the underlying structure from examples that have divergent surface features (Chi, Feltovich, & Glaser, 1981 ; see Fig.  5 for an example from physics). Gick and Holyoak ( 1983 ) tried to get students to apply a rule from one problem to another problem that appeared different on the surface, but was structurally similar. They found that providing multiple examples helped with this transfer process compared to only using one example – especially when the examples provided had different surface details. More work is also needed to determine how many examples are sufficient for generalization to occur (and this, of course, will vary with contextual factors and individual differences). Further research on the continuum between concrete/specific examples and more abstract concepts would also be informative. That is, if an example is not concrete enough, it may be too difficult to understand. On the other hand, if the example is too concrete, that could be detrimental to generalization to the more abstract concept (although a diverse set of very concrete examples may be able to help with this). In fact, in a controversial article, Kaminski, Sloutsky, and Heckler ( 2008 ) claimed that abstract examples were more effective than concrete examples. Later rebuttals of this paper contested whether the abstract versus concrete distinction was clearly defined in the original study (see Reed, 2008 , for a collection of letters on the subject). This ideal point along the concrete-abstract continuum might also interact with development.

Finding teacher blog posts on concrete examples proved to be more difficult than for the other strategies in this review. One optimistic possibility is that teachers frequently use concrete examples in their teaching, and thus do not think of this as a specific contribution from cognitive psychology; the one blog post we were able to find that discussed concrete examples suggests that this might be the case (Boulton, 2016 ). The idea of “linking abstract concepts with concrete examples” is also covered in 25% of teacher-training textbooks used in the US, according to the report by Pomerance et al. ( 2016 ); this is the second most frequently covered of the six strategies, after “posing probing questions” (i.e., elaborative interrogation). A useful direction for future research would be to establish how teachers are using concrete examples in their practice, and whether we can make any suggestions for improvement based on research into the science of learning. For example, if two examples are better than one (Bauernschmidt, 2017 ), are additional examples also needed, or are there diminishing returns from providing more examples? And, how can teachers best ensure that concrete examples are consistent with prior knowledge (Reed, 2008 )?

Dual coding

Both the memory literature and folk psychology support the notion of visual examples being beneficial—the adage of “a picture is worth a thousand words” (traced back to an advertising slogan from the 1920s; Meider, 1990 ). Indeed, it is well-understood that more information can be conveyed through a simple illustration than through several paragraphs of text (e.g., Barker & Manji, 1989 ; Mayer & Gallini, 1990 ). Illustrations can be particularly helpful when the described concept involves several parts or steps and is intended for individuals with low prior knowledge (Eitel & Scheiter, 2015 ; Mayer & Gallini, 1990 ). Figure  6 provides a concrete example of this, illustrating how information can flow through neurons and synapses.

In addition to being able to convey information more succinctly, pictures are also more memorable than words (Paivio & Csapo, 1969 , 1973 ). In the memory literature, this is referred to as the picture superiority effect , and dual coding theory was developed in part to explain this effect. Dual coding follows from the notion of text being accompanied by complementary visual information to enhance learning. Paivio ( 1971 , 1986 ) proposed dual coding theory as a mechanistic account for the integration of multiple information “codes” to process information. In this theory, a code corresponds to a modal or otherwise distinct representation of a concept—e.g., “mental images for ‘book’ have visual, tactual, and other perceptual qualities similar to those evoked by the referent objects on which the images are based” (Clark & Paivio, 1991 , p. 152). Aylwin ( 1990 ) provides a clear example of how the word “dog” can evoke verbal, visual, and enactive representations (see Fig.  7 for a similar example for the word “SPOON”, based on Aylwin, 1990 (Fig.  2 ) and Madan & Singhal, 2012a (Fig.  3 )). Codes can also correspond to emotional properties (Clark & Paivio, 1991 ; Paivio, 2013 ). Clark and Paivio ( 1991 ) provide a thorough review of dual coding theory and its relation to education, while Paivio ( 2007 ) provides a comprehensive treatise on dual coding theory. Broadly, dual coding theory suggests that providing multiple representations of the same information enhances learning and memory, and that information that more readily evokes additional representations (through automatic imagery processes) receives a similar benefit.

Paivio and Csapo ( 1973 ) suggest that verbal and imaginal codes have independent and additive effects on memory recall. Using visuals to improve learning and memory has been particularly applied to vocabulary learning (Danan, 1992 ; Sadoski, 2005 ), but has also shown success in other domains such as in health care (Hartland, Biddle, & Fallacaro, 2008 ). To take advantage of dual coding, verbal information should be accompanied by a visual representation when possible. However, while the studies discussed all indicate that the use of multiple representations of information is favorable, it is important to acknowledge that each representation also increases cognitive load and can lead to over-saturation (Mayer & Moreno, 2003 ).

Given that pictures are generally remembered better than words, it is important to ensure that the pictures students are provided with are helpful and relevant to the content they are expected to learn. McNeill, Uttal, Jarvin, and Sternberg ( 2009 ) found that providing visual examples decreased conceptual errors. However, McNeill et al. also found that when students were given visually rich examples, they performed more poorly than students who were not given any visual example, suggesting that the visual details can at times become a distraction and hinder performance. Thus, it is important to consider that images used in teaching are clear and not ambiguous in their meaning (Schwartz, 2007 ).

Further broadening the scope of dual coding theory, Engelkamp and Zimmer ( 1984 ) suggest that motor movements, such as “turning the handle,” can provide an additional motor code that can improve memory, linking studies of motor actions (enactment) with dual coding theory (Clark & Paivio, 1991 ; Engelkamp & Cohen, 1991 ; Madan & Singhal, 2012c ). Indeed, enactment effects appear to primarily occur during learning, rather than during retrieval (Peterson & Mulligan, 2010 ). Along similar lines, Wammes, Meade, and Fernandes ( 2016 ) demonstrated that generating drawings can provide memory benefits beyond what could otherwise be explained by visual imagery, picture superiority, and other memory enhancing effects. Providing convergent evidence, even when overt motor actions are not critical in themselves, words representing functional objects have been shown to enhance later memory (Madan & Singhal, 2012b ; Montefinese, Ambrosini, Fairfield, & Mammarella, 2013 ). This indicates that motoric processes can improve memory similarly to visual imagery, similar to memory differences for concrete vs. abstract words. Further research suggests that automatic motor simulation for functional objects is likely responsible for this memory benefit (Madan, Chen, & Singhal, 2016 ).

When teachers combine visuals and words in their educational practice, however, they may not always be taking advantage of dual coding – at least, not in the optimal manner. For example, a recent discussion on Twitter centered around one teacher’s decision to have 7 th Grade students replace certain words in their science laboratory report with a picture of that word (e.g., the instructions read “using a syringe …” and a picture of a syringe replaced the word; Turner, 2016a ). Other teachers argued that this was not dual coding (Beaven, 2016 ; Williams, 2016 ), because there were no longer two different representations of the information. The first teacher maintained that dual coding was preserved, because this laboratory report with pictures was to be used alongside the original, fully verbal report (Turner, 2016b ). This particular implementation – having students replace individual words with pictures – has not been examined in the cognitive literature, presumably because no benefit would be expected. In any case, we need to be clearer about implementations for dual coding, and more research is needed to clarify how teachers can make use of the benefits conferred by multiple representations and picture superiority.

Critically, dual coding theory is distinct from the notion of “learning styles,” which describe the idea that individuals benefit from instruction that matches their modality preference. While this idea is pervasive and individuals often subjectively feel that they have a preference, evidence indicates that the learning styles theory is not supported by empirical findings (e.g., Kavale, Hirshoren, & Forness, 1998 ; Pashler, McDaniel, Rohrer, & Bjork, 2008 ; Rohrer & Pashler, 2012 ). That is, there is no evidence that instructing students in their preferred learning style leads to an overall improvement in learning (the “meshing” hypothesis). Moreover, learning styles have come to be described as a myth or urban legend within psychology (Coffield, Moseley, Hall, & Ecclestone, 2004 ; Hattie & Yates, 2014 ; Kirschner & van Merriënboer, 2013 ; Kirschner, 2017 ); skepticism about learning styles is a common stance amongst evidence-informed teachers (e.g., Saunders, 2016 ). Providing evidence against the notion of learning styles, Kraemer, Rosenberg, and Thompson-Schill ( 2009 ) found that individuals who scored as “verbalizers” and “visualizers” did not perform any better on experimental trials matching their preference. Instead, it has recently been shown that learning through one’s preferred learning style is associated with elevated subjective judgements of learning, but not objective performance (Knoll, Otani, Skeel, & Van Horn, 2017 ). In contrast to learning styles, dual coding is based on providing additional, complementary forms of information to enhance learning, rather than tailoring instruction to individuals’ preferences.

Genuine educational environments present many opportunities for combining the strategies outlined above. Spacing can be particularly potent for learning if it is combined with retrieval practice. The additive benefits of retrieval practice and spacing can be gained by engaging in retrieval practice multiple times (also known as distributed practice; see Cepeda et al., 2006 ). Interleaving naturally entails spacing if students interleave old and new material. Concrete examples can be both verbal and visual, making use of dual coding. In addition, the strategies of elaboration, concrete examples, and dual coding all work best when used as part of retrieval practice. For example, in the concept-mapping studies mentioned above (Blunt & Karpicke, 2014 ; Karpicke, Blunt, et al., 2014 ), creating concept maps while looking at course materials (e.g., a textbook) was not as effective for later memory as creating concept maps from memory. When practicing elaborative interrogation, students can start off answering the “how” and “why” questions they pose for themselves using class materials, and work their way up to answering them from memory. And when interleaving different problem types, students should be practicing answering them rather than just looking over worked examples.

But while these ideas for strategy combinations have empirical bases, it has not yet been established whether the benefits of the strategies to learning are additive, super-additive, or, in some cases, incompatible. Thus, future research needs to (a) better formalize the definition of each strategy (particularly critical for elaboration and dual coding), (b) identify best practices for implementation in the classroom, (c) delineate the boundary conditions of each strategy, and (d) strategically investigate interactions between the six strategies we outlined in this manuscript.

Aleven, V. A., & Koedinger, K. R. (2002). An effective metacognitive strategy: learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26 , 147–179.

Article   Google Scholar  

Anderson, J. R. (1983). A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior, 22 , 261–295.

Arnold, K. M., & McDermott, K. B. (2013). Test-potentiated learning: distinguishing between direct and indirect effects of tests. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39 , 940–945.

PubMed   Google Scholar  

Aylwin, S. (1990). Imagery and affect: big questions, little answers. In P. J. Thompson, D. E. Marks, & J. T. E. Richardson (Eds.), Imagery: Current developments . New York: International Library of Psychology.

Google Scholar  

Baldassari, M. J., & Kelley, M. (2012). Make’em laugh? The mnemonic effect of humor in a speech. Psi Chi Journal of Psychological Research, 17 , 2–9.

Barker, P. G., & Manji, K. A. (1989). Pictorial dialogue methods. International Journal of Man-Machine Studies, 31 , 323–347.

Bauernschmidt, A. (2017). GUEST POST: two examples are better than one. [Blog post]. The Learning Scientists Blog . Retrieved from http://www.learningscientists.org/blog/2017/5/30-1 . Accessed 25 Dec 2017.

Beaven, T. (2016). @doctorwhy @FurtherEdagogy @doc_kristy Right, I thought the whole point of dual coding was to use TWO codes: pics + words of the SAME info? [Tweet]. Retrieved from https://twitter.com/TitaBeaven/status/807504041341308929 . Accessed 25 Dec 2017.

Bellezza, F. S., Cheesman, F. L., & Reddy, B. G. (1977). Organization and semantic elaboration in free recall. Journal of Experimental Psychology: Human Learning and Memory, 3 , 539–550.

Benney, D. (2016). (Trying to apply) spacing in a content heavy subject [Blog post]. Retrieved from https://mrbenney.wordpress.com/2016/10/16/trying-to-apply-spacing-in-science/ . Accessed 25 Dec 2017.

Berry, D. C. (1983). Metacognitive experience and transfer of logical reasoning. Quarterly Journal of Experimental Psychology, 35A , 39–49.

Birnbaum, M. S., Kornell, N., Bjork, E. L., & Bjork, R. A. (2013). Why interleaving enhances inductive learning: the roles of discrimination and retrieval. Memory & Cognition, 41 , 392–402.

Bjork, R. A. (1999). Assessing our own competence: heuristics and illusions. In D. Gopher & A. Koriat (Eds.), Attention and peformance XVII. Cognitive regulation of performance: Interaction of theory and application (pp. 435–459). Cambridge, MA: MIT Press.

Bjork, R. A. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe & A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 185–205). Cambridge, MA: MIT Press.

Bjork, R. A., & Bjork, E. L. (1992). A new theory of disuse and an old theory of stimulus fluctuation. From learning processes to cognitive processes: Essays in honor of William K. Estes, 2 , 35–67.

Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: creating desirable difficulties to enhance learning. Psychology and the real world: Essays illustrating fundamental contributions to society , 56–64.

Blunt, J. R., & Karpicke, J. D. (2014). Learning with retrieval-based concept mapping. Journal of Educational Psychology, 106 , 849–858.

Boulton, K. (2016). What does cognitive overload look like in the humanities? [Blog post]. Retrieved from https://educationechochamberuncut.wordpress.com/2016/03/05/what-does-cognitive-overload-look-like-in-the-humanities-kris-boulton-2/ . Accessed 25 Dec 2017.

Brown, P. C., Roediger, H. L., & McDaniel, M. A. (2014). Make it stick . Cambridge, MA: Harvard University Press.

Book   Google Scholar  

Butler, A. C. (2010). Repeated testing produces superior transfer of learning relative to repeated studying. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36 , 1118–1133.

Caplan, J. B., & Madan, C. R. (2016). Word-imageability enhances association-memory by recruiting hippocampal activity. Journal of Cognitive Neuroscience, 28 , 1522–1538.

Article   PubMed   Google Scholar  

Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: a review and quantitative synthesis. Psychological Bulletin, 132 , 354–380.

Cepeda, N. J., Vul, E., Rohrer, D., Wixted, J. T., & Pashler, H. (2008). Spacing effects in learning a temporal ridgeline of optimal retention. Psychological Science, 19 , 1095–1102.

Chi, M. T., De Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18 , 439–477.

Chi, M. T., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5 , 121–152.

CIFE. (2012). No January A level and other changes. Retrieved from http://www.cife.org.uk/cife-general-news/no-january-a-level-and-other-changes/ . Accessed 25 Dec 2017.

Clark, D. (2016). One book on learning that every teacher, lecturer & trainer should read (7 reasons) [Blog post]. Retrieved from http://donaldclarkplanb.blogspot.com/2016/03/one-book-on-learning-that-every-teacher.html . Accessed 25 Dec 2017.

Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3 , 149–210.

Class Teaching. (2013). Deep questioning [Blog post]. Retrieved from https://classteaching.wordpress.com/2013/07/12/deep-questioning/ . Accessed 25 Dec 2017.

Clinton, V., Alibali, M. W., & Nathan, M. J. (2016). Learning about posterior probability: do diagrams and elaborative interrogation help? The Journal of Experimental Education, 84 , 579–599.

Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: a systematic and critical review . London: Learning & Skills Research Centre.

Cohen, R. L. (1981). On the generality of some memory laws. Scandinavian Journal of Psychology, 22 , 267–281.

Cooper, H. (1989). Synthesis of research on homework. Educational Leadership, 47 , 85–91.

Corbett, A. T., Reed, S. K., Hoffmann, R., MacLaren, B., & Wagner, A. (2010). Interleaving worked examples and cognitive tutor support for algebraic modeling of problem situations. In Proceedings of the Thirty-Second Annual Meeting of the Cognitive Science Society (pp. 2882–2887).

Cox, D. (2015). No stakes testing – not telling students their results [Blog post]. Retrieved from https://missdcoxblog.wordpress.com/2015/06/06/no-stakes-testing-not-telling-students-their-results/ . Accessed 25 Dec 2017.

Cox, D. (2016a). Ditch revision. Teach it well [Blog post]. Retrieved from https://missdcoxblog.wordpress.com/2016/01/09/ditch-revision-teach-it-well/ . Accessed 25 Dec 2017.

Cox, D. (2016b). ‘They need to remember this in three years time’: spacing & interleaving for the new GCSEs [Blog post]. Retrieved from https://missdcoxblog.wordpress.com/2016/03/25/they-need-to-remember-this-in-three-years-time-spacing-interleaving-for-the-new-gcses/ . Accessed 25 Dec 2017.

Craik, F. I. (2002). Levels of processing: past, present… future? Memory, 10 , 305–318.

Craik, F. I., & Lockhart, R. S. (1972). Levels of processing: a framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11 , 671–684.

Danan, M. (1992). Reversed subtitling and dual coding theory: new directions for foreign language instruction. Language Learning, 42 , 497–527.

Dettmers, S., Trautwein, U., & Lüdtke, O. (2009). The relationship between homework time and achievement is not universal: evidence from multilevel analyses in 40 countries. School Effectiveness and School Improvement, 20 , 375–405.

Dirkx, K. J., Kester, L., & Kirschner, P. A. (2014). The testing effect for learning principles and procedures from texts. The Journal of Educational Research, 107 , 357–364.

Dunlosky, J. (2013). Strengthening the student toolbox: study strategies to boost learning. American Educator, 37 (3), 12–21.

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14 , 4–58.

Ebbinghaus, H. (1913). Memory (HA Ruger & CE Bussenius, Trans.). New York: Columbia University, Teachers College. (Original work published 1885) . Retrieved from http://psychclassics.yorku.ca/Ebbinghaus/memory8.htm . Accessed 25 Dec 2017.

Eglington, L. G., & Kang, S. H. (2016). Retrieval practice benefits deductive inference. Educational Psychology Review , 1–14.

Eitel, A., & Scheiter, K. (2015). Picture or text first? Explaining sequential effects when learning with pictures and text. Educational Psychology Review, 27 , 153–180.

Engelkamp, J., & Cohen, R. L. (1991). Current issues in memory of action events. Psychological Research, 53 , 175–182.

Engelkamp, J., & Zimmer, H. D. (1984). Motor programme information as a separable memory unit. Psychological Research, 46 , 283–299.

Fawcett, D. (2013). Can I be that little better at……using cognitive science/psychology/neurology to plan learning? [Blog post]. Retrieved from http://reflectionsofmyteaching.blogspot.com/2013/09/can-i-be-that-little-better-atusing.html . Accessed 25 Dec 2017.

Fiechter, J. L., & Benjamin, A. S. (2017). Diminishing-cues retrieval practice: a memory-enhancing technique that works when regular testing doesn’t. Psychonomic Bulletin & Review , 1–9.

Firth, J. (2016). Spacing in teaching practice [Blog post]. Retrieved from http://www.learningscientists.org/blog/2016/4/12-1 . Accessed 25 Dec 2017.

Fordham, M. [mfordhamhistory]. (2016). Is there a meaningful distinction in psychology between ‘thinking’ & ‘critical thinking’? [Tweet]. Retrieved from https://twitter.com/mfordhamhistory/status/809525713623781377 . Accessed 25 Dec 2017.

Fritz, C. O., Morris, P. E., Nolan, D., & Singleton, J. (2007). Expanding retrieval practice: an effective aid to preschool children’s learning. The Quarterly Journal of Experimental Psychology, 60 , 991–1004.

Gates, A. I. (1917). Recitation as a factory in memorizing. Archives of Psychology, 6.

Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15 , 1–38.

Gorman, A. M. (1961). Recognition memory for nouns as a function of abstractedness and frequency. Journal of Experimental Psychology, 61 , 23–39.

Hainselin, M., Picard, L., Manolli, P., Vankerkore-Candas, S., & Bourdin, B. (2017). Hey teacher, don’t leave them kids alone: action is better for memory than reading. Frontiers in Psychology , 8 .

Harp, S. F., & Mayer, R. E. (1998). How seductive details do their damage. Journal of Educational Psychology, 90 , 414–434.

Hartland, W., Biddle, C., & Fallacaro, M. (2008). Audiovisual facilitation of clinical knowledge: A paradigm for dispersed student education based on Paivio’s dual coding theory. AANA Journal, 76 , 194–198.

Hattie, J., & Yates, G. (2014). Visible learning and the science of how we learn . New York: Routledge.

Hausman, H., & Kornell, N. (2014). Mixing topics while studying does not enhance learning. Journal of Applied Research in Memory and Cognition, 3 , 153–160.

Hinze, S. R., & Rapp, D. N. (2014). Retrieval (sometimes) enhances learning: performance pressure reduces the benefits of retrieval practice. Applied Cognitive Psychology, 28 , 597–606.

Hirshman, E. (2001). Elaboration in memory. In N. J. Smelser & P. B. Baltes (Eds.), International encyclopedia of the social & behavioral sciences (pp. 4369–4374). Oxford: Pergamon.

Chapter   Google Scholar  

Hobbiss, M. (2016). Make it meaningful! Elaboration [Blog post]. Retrieved from https://hobbolog.wordpress.com/2016/06/09/make-it-meaningful-elaboration/ . Accessed 25 Dec 2017.

Jones, F. (2016). Homework – is it really that useless? [Blog post]. Retrieved from http://www.learningscientists.org/blog/2016/4/5-1 . Accessed 25 Dec 2017.

Kaminski, J. A., & Sloutsky, V. M. (2013). Extraneous perceptual information interferes with children’s acquisition of mathematical knowledge. Journal of Educational Psychology, 105 (2), 351–363.

Kaminski, J. A., Sloutsky, V. M., & Heckler, A. F. (2008). The advantage of abstract examples in learning math. Science, 320 , 454–455.

Kang, S. H. (2016). Spaced repetition promotes efficient and effective learning policy implications for instruction. Policy Insights from the Behavioral and Brain Sciences, 3 , 12–19.

Kang, S. H. K., McDermott, K. B., & Roediger, H. L. (2007). Test format and corrective feedback modify the effects of testing on long-term retention. European Journal of Cognitive Psychology, 19 , 528–558.

Karpicke, J. D., & Aue, W. R. (2015). The testing effect is alive and well with complex materials. Educational Psychology Review, 27 , 317–326.

Karpicke, J. D., Blunt, J. R., Smith, M. A., & Karpicke, S. S. (2014). Retrieval-based learning: The need for guided retrieval in elementary school children. Journal of Applied Research in Memory and Cognition, 3 , 198–206.

Karpicke, J. D., Lehman, M., & Aue, W. R. (2014). Retrieval-based learning: an episodic context account. In B. H. Ross (Ed.), Psychology of Learning and Motivation (Vol. 61, pp. 237–284). San Diego, CA: Elsevier Academic Press.

Karpicke, J. D., Blunt, J. R., & Smith, M. A. (2016). Retrieval-based learning: positive effects of retrieval practice in elementary school children. Frontiers in Psychology, 7 .

Kavale, K. A., Hirshoren, A., & Forness, S. R. (1998). Meta-analytic validation of the Dunn and Dunn model of learning-style preferences: a critique of what was Dunn. Learning Disabilities Research & Practice, 13 , 75–80.

Khanna, M. M. (2015). Ungraded pop quizzes: test-enhanced learning without all the anxiety. Teaching of Psychology, 42 , 174–178.

Kirby, J. (2014). One scientific insight for curriculum design [Blog post]. Retrieved from https://pragmaticreform.wordpress.com/2014/05/05/scientificcurriculumdesign/ . Accessed 25 Dec 2017.

Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106 , 166–171.

Kirschner, P. A., & van Merriënboer, J. J. G. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48 , 169–183.

Knoll, A. R., Otani, H., Skeel, R. L., & Van Horn, K. R. (2017). Learning style, judgments of learning, and learning of verbal and visual information. British Journal of Psychology, 108 , 544-563.

Kornell, N., & Bjork, R. A. (2008). Learning concepts and categories is spacing the “enemy of induction”? Psychological Science, 19 , 585–592.

Kornell, N., & Finn, B. (2016). Self-regulated learning: an overview of theory and data. In J. Dunlosky & S. Tauber (Eds.), The Oxford Handbook of Metamemory (pp. 325–340). New York: Oxford University Press.

Kornell, N., Klein, P. J., & Rawson, K. A. (2015). Retrieval attempts enhance learning, but retrieval success (versus failure) does not matter. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41 , 283–294.

Kraemer, D. J. M., Rosenberg, L. M., & Thompson-Schill, S. L. (2009). The neural correlates of visual and verbal cognitive styles. Journal of Neuroscience, 29 , 3792–3798.

Article   PubMed   PubMed Central   Google Scholar  

Kraft, N. (2015). Spaced practice and repercussions for teaching. Retrieved from http://nathankraft.blogspot.com/2015/08/spaced-practice-and-repercussions-for.html . Accessed 25 Dec 2017.

Learning Scientists. (2016a). Weekly Digest #3: How teachers implement interleaving in their curriculum [Blog post]. Retrieved from http://www.learningscientists.org/blog/2016/3/28/weekly-digest-3 . Accessed 25 Dec 2017.

Learning Scientists. (2016b). Weekly Digest #13: how teachers implement retrieval in their classrooms [Blog post]. Retrieved from http://www.learningscientists.org/blog/2016/6/5/weekly-digest-13 . Accessed 25 Dec 2017.

Learning Scientists. (2016c). Weekly Digest #40: teachers’ implementation of principles from “Make It Stick” [Blog post]. Retrieved from http://www.learningscientists.org/blog/2016/12/18-1 . Accessed 25 Dec 2017.

Learning Scientists. (2017). Weekly Digest #54: is there an app for that? Studying 2.0 [Blog post]. Retrieved from http://www.learningscientists.org/blog/2017/4/9/weekly-digest-54 . Accessed 25 Dec 2017.

LeFevre, J.-A., & Dixon, P. (1986). Do written instructions need examples? Cognition and Instruction, 3 , 1–30.

Lew, K., Fukawa-Connelly, T., Mejí-Ramos, J. P., & Weber, K. (2016). Lectures in advanced mathematics: Why students might not understand what the mathematics professor is trying to convey. Journal of Research in Mathematics Education, 47 , 162–198.

Lindsey, R. V., Shroyer, J. D., Pashler, H., & Mozer, M. C. (2014). Improving students’ long-term knowledge retention through personalized review. Psychological Science, 25 , 639–647.

Lipko-Speed, A., Dunlosky, J., & Rawson, K. A. (2014). Does testing with feedback help grade-school children learn key concepts in science? Journal of Applied Research in Memory and Cognition, 3 , 171–176.

Lockhart, R. S., & Craik, F. I. (1990). Levels of processing: a retrospective commentary on a framework for memory research. Canadian Journal of Psychology, 44 , 87–112.

Lovell, O. (2017). How do we know what to put on the quiz? [Blog Post]. Retrieved from http://www.ollielovell.com/olliesclassroom/know-put-quiz/ . Accessed 25 Dec 2017.

Luehmann, A. L. (2008). Using blogging in support of teacher professional identity development: a case study. The Journal of the Learning Sciences, 17 , 287–337.

Madan, C. R., Glaholt, M. G., & Caplan, J. B. (2010). The influence of item properties on association-memory. Journal of Memory and Language, 63 , 46–63.

Madan, C. R., & Singhal, A. (2012a). Motor imagery and higher-level cognition: four hurdles before research can sprint forward. Cognitive Processing, 13 , 211–229.

Madan, C. R., & Singhal, A. (2012b). Encoding the world around us: motor-related processing influences verbal memory. Consciousness and Cognition, 21 , 1563–1570.

Madan, C. R., & Singhal, A. (2012c). Using actions to enhance memory: effects of enactment, gestures, and exercise on human memory. Frontiers in Psychology, 3 .

Madan, C. R., Chen, Y. Y., & Singhal, A. (2016). ERPs differentially reflect automatic and deliberate processing of the functional manipulability of objects. Frontiers in Human Neuroscience, 10 .

Mandler, G. (1979). Organization and repetition: organizational principles with special reference to rote learning. In L. G. Nilsson (Ed.), Perspectives on Memory Research (pp. 293–327). New York: Academic Press.

Marsh, E. J., Fazio, L. K., & Goswick, A. E. (2012). Memorial consequences of testing school-aged children. Memory, 20 , 899–906.

Mayer, R. E., & Gallini, J. K. (1990). When is an illustration worth ten thousand words? Journal of Educational Psychology, 82 , 715–726.

Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38 , 43–52.

McDaniel, M. A., & Donnelly, C. M. (1996). Learning with analogy and elaborative interrogation. Journal of Educational Psychology, 88 , 508–519.

McDaniel, M. A., Thomas, R. C., Agarwal, P. K., McDermott, K. B., & Roediger, H. L. (2013). Quizzing in middle-school science: successful transfer performance on classroom exams. Applied Cognitive Psychology, 27 , 360–372.

McDermott, K. B., Agarwal, P. K., D’Antonio, L., Roediger, H. L., & McDaniel, M. A. (2014). Both multiple-choice and short-answer quizzes enhance later exam performance in middle and high school classes. Journal of Experimental Psychology: Applied, 20 , 3–21.

McHugh, A. (2013). High-stakes tests: bad for students, teachers, and education in general [Blog post]. Retrieved from https://teacherbiz.wordpress.com/2013/07/01/high-stakes-tests-bad-for-students-teachers-and-education-in-general/ . Accessed 25 Dec 2017.

McNeill, N. M., Uttal, D. H., Jarvin, L., & Sternberg, R. J. (2009). Should you show me the money? Concrete objects both hurt and help performance on mathematics problems. Learning and Instruction, 19 , 171–184.

Meider, W. (1990). “A picture is worth a thousand words”: from advertising slogan to American proverb. Southern Folklore, 47 , 207–225.

Michaela Community School. (2014). Homework. Retrieved from http://mcsbrent.co.uk/homework-2/ . Accessed 25 Dec 2017.

Montefinese, M., Ambrosini, E., Fairfield, B., & Mammarella, N. (2013). The “subjective” pupil old/new effect: is the truth plain to see? International Journal of Psychophysiology, 89 , 48–56.

O’Neil, H. F., Chung, G. K., Kerr, D., Vendlinski, T. P., Buschang, R. E., & Mayer, R. E. (2014). Adding self-explanation prompts to an educational computer game. Computers In Human Behavior, 30 , 23–28.

Overoye, A. L., & Storm, B. C. (2015). Harnessing the power of uncertainty to enhance learning. Translational Issues in Psychological Science, 1 , 140–148.

Paivio, A. (1971). Imagery and verbal processes . New York: Holt, Rinehart and Winston.

Paivio, A. (1986). Mental representations: a dual coding approach . New York: Oxford University Press.

Paivio, A. (2007). Mind and its evolution: a dual coding theoretical approach . Mahwah: Erlbaum.

Paivio, A. (2013). Dual coding theory, word abstractness, and emotion: a critical review of Kousta et al. (2011). Journal of Experimental Psychology: General, 142 , 282–287.

Paivio, A., & Csapo, K. (1969). Concrete image and verbal memory codes. Journal of Experimental Psychology, 80 , 279–285.

Paivio, A., & Csapo, K. (1973). Picture superiority in free recall: imagery or dual coding? Cognitive Psychology, 5 , 176–206.

Paivio, A., Walsh, M., & Bons, T. (1994). Concreteness effects on memory: when and why? Journal of Experimental Psychology: Learning, Memory, and Cognition, 20 , 1196–1204.

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: concepts and evidence. Psychological Science in the Public Interest, 9 , 105–119.

Pashler, H., Bain, P. M., Bottge, B. A., Graesser, A., Koedinger, K., McDaniel, M., & Metcalfe, J. (2007). Organizing instruction and study to improve student learning. IES practice guide. NCER 2007–2004. National Center for Education Research .

Patel, R., Liu, R., & Koedinger, K. (2016). When to block versus interleave practice? Evidence against teaching fraction addition before fraction multiplication. In Proceedings of the 38th Annual Meeting of the Cognitive Science Society, Philadelphia, PA .

Penfound, B. (2017). Journey to interleaved practice #2 [Blog Post]. Retrieved from https://fullstackcalculus.com/2017/02/03/journey-to-interleaved-practice-2/ . Accessed 25 Dec 2017.

Penfound, B. [BryanPenfound]. (2016). Does blocked practice/learning lessen cognitive load? Does interleaved practice/learning provide productive struggle? [Tweet]. Retrieved from https://twitter.com/BryanPenfound/status/808759362244087808 . Accessed 25 Dec 2017.

Peterson, D. J., & Mulligan, N. W. (2010). Enactment and retrieval. Memory & Cognition, 38 , 233–243.

Picciotto, H. (2009). Lagging homework [Blog post]. Retrieved from http://blog.mathedpage.org/2013/06/lagging-homework.html . Accessed 25 Dec 2017.

Pomerance, L., Greenberg, J., & Walsh, K. (2016). Learning about learning: what every teacher needs to know. Retrieved from http://www.nctq.org/dmsView/Learning_About_Learning_Report . Accessed 25 Dec 2017.

Postman, L. (1976). Methodology of human learning. In W. K. Estes (Ed.), Handbook of learning and cognitive processes (Vol. 3). Hillsdale: Erlbaum.

Pressley, M., McDaniel, M. A., Turnure, J. E., Wood, E., & Ahmad, M. (1987). Generation and precision of elaboration: effects on intentional and incidental learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13 , 291–300.

Reed, S. K. (2008). Concrete examples must jibe with experience. Science, 322 , 1632–1633.

researchED. (2013). How it all began. Retrieved from http://www.researched.org.uk/about/our-story/ . Accessed 25 Dec 2017.

Ritchie, S. J., Della Sala, S., & McIntosh, R. D. (2013). Retrieval practice, with or without mind mapping, boosts fact learning in primary school children. PLoS One, 8 (11), e78976.

Rittle-Johnson, B. (2006). Promoting transfer: effects of self-explanation and direct instruction. Child Development, 77 , 1–15.

Roediger, H. L. (1985). Remembering Ebbinghaus. [Retrospective review of the book On Memory , by H. Ebbinghaus]. Contemporary Psychology, 30 , 519–523.

Roediger, H. L. (2013). Applying cognitive psychology to education translational educational science. Psychological Science in the Public Interest, 14 , 1–3.

Roediger, H. L., & Karpicke, J. D. (2006). The power of testing memory: basic research and implications for educational practice. Perspectives on Psychological Science, 1 , 181–210.

Roediger, H. L., Putnam, A. L., & Smith, M. A. (2011). Ten benefits of testing and their applications to educational practice. In J. Mester & B. Ross (Eds.), The psychology of learning and motivation: cognition in education (pp. 1–36). Oxford: Elsevier.

Roediger, H. L., Finn, B., & Weinstein, Y. (2012). Applications of cognitive science to education. In Della Sala, S., & Anderson, M. (Eds.), Neuroscience in education: the good, the bad, and the ugly . Oxford, UK: Oxford University Press.

Roelle, J., & Berthold, K. (2017). Effects of incorporating retrieval into learning tasks: the complexity of the tasks matters. Learning and Instruction, 49 , 142–156.

Rohrer, D. (2012). Interleaving helps students distinguish among similar concepts. Educational Psychology Review, 24(3), 355–367.

Rohrer, D., Dedrick, R. F., & Stershic, S. (2015). Interleaved practice improves mathematics learning. Journal of Educational Psychology, 107 , 900–908.

Rohrer, D., & Pashler, H. (2012). Learning styles: Where’s the evidence? Medical Education, 46 , 34–35.

Rohrer, D., & Taylor, K. (2007). The shuffling of mathematics problems improves learning. Instructional Science, 35 , 481–498.

Rose, N. (2014). Improving the effectiveness of homework [Blog post]. Retrieved from https://evidenceintopractice.wordpress.com/2014/03/20/improving-the-effectiveness-of-homework/ . Accessed 25 Dec 2017.

Sadoski, M. (2005). A dual coding view of vocabulary learning. Reading & Writing Quarterly, 21 , 221–238.

Saunders, K. (2016). It really is time we stopped talking about learning styles [Blog post]. Retrieved from http://martingsaunders.com/2016/10/it-really-is-time-we-stopped-talking-about-learning-styles/ . Accessed 25 Dec 2017.

Schwartz, D. (2007). If a picture is worth a thousand words, why are you reading this essay? Social Psychology Quarterly, 70 , 319–321.

Shumaker, H. (2016). Homework is wrecking our kids: the research is clear, let’s ban elementary homework. Salon. Retrieved from http://www.salon.com/2016/03/05/homework_is_wrecking_our_kids_the_research_is_clear_lets_ban_elementary_homework . Accessed 25 Dec 2017.

Smith, A. M., Floerke, V. A., & Thomas, A. K. (2016). Retrieval practice protects memory against acute stress. Science, 354 , 1046–1048.

Smith, M. A., Blunt, J. R., Whiffen, J. W., & Karpicke, J. D. (2016). Does providing prompts during retrieval practice improve learning? Applied Cognitive Psychology, 30 , 784–802.

Smith, M. A., & Karpicke, J. D. (2014). Retrieval practice with short-answer, multiple-choice, and hybrid formats. Memory, 22 , 784–802.

Smith, M. A., Roediger, H. L., & Karpicke, J. D. (2013). Covert retrieval practice benefits retention as much as overt retrieval practice. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39 , 1712–1725.

Son, J. Y., & Rivas, M. J. (2016). Designing clicker questions to stimulate transfer. Scholarship of Teaching and Learning in Psychology, 2 , 193–207.

Szpunar, K. K., Khan, N. Y., & Schacter, D. L. (2013). Interpolated memory tests reduce mind wandering and improve learning of online lectures. Proceedings of the National Academy of Sciences, 110 , 6313–6317.

Thomson, R., & Mehring, J. (2016). Better vocabulary study strategies for long-term learning. Kwansei Gakuin University Humanities Review, 20 , 133–141.

Trafton, J. G., & Reiser, B. J. (1993). Studying examples and solving problems: contributions to skill acquisition . Technical report, Naval HCI Research Lab, Washington, DC, USA.

Tran, R., Rohrer, D., & Pashler, H. (2015). Retrieval practice: the lack of transfer to deductive inferences. Psychonomic Bulletin & Review, 22 , 135–140.

Turner, K. [doc_kristy]. (2016a). My dual coding (in red) and some y8 work @AceThatTest they really enjoyed practising the technique [Tweet]. Retrieved from https://twitter.com/doc_kristy/status/807220355395977216 . Accessed 25 Dec 2017.

Turner, K. [doc_kristy]. (2016b). @FurtherEdagogy @doctorwhy their work is revision work, they already have the words on a different page, to compliment not replace [Tweet]. Retrieved from https://twitter.com/doc_kristy/status/807360265100599301 . Accessed 25 Dec 2017.

Valle, A., Regueiro, B., Núñez, J. C., Rodríguez, S., Piñeiro, I., & Rosário, P. (2016). Academic goals, student homework engagement, and academic achievement in elementary school. Frontiers in Psychology, 7 .

Van Gog, T., & Sweller, J. (2015). Not new, but nearly forgotten: the testing effect decreases or even disappears as the complexity of learning materials increases. Educational Psychology Review, 27 , 247–264.

Wammes, J. D., Meade, M. E., & Fernandes, M. A. (2016). The drawing effect: evidence for reliable and robust memory benefits in free recall. Quarterly Journal of Experimental Psychology, 69 , 1752–1776.

Weinstein, Y., Gilmore, A. W., Szpunar, K. K., & McDermott, K. B. (2014). The role of test expectancy in the build-up of proactive interference in long-term memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40 , 1039–1048.

Weinstein, Y., Nunes, L. D., & Karpicke, J. D. (2016). On the placement of practice questions during study. Journal of Experimental Psychology: Applied, 22 , 72–84.

Weinstein, Y., & Weinstein-Jones, F. (2017). Topic and quiz spacing spreadsheet: a planning tool for teachers [Blog Post]. Retrieved from http://www.learningscientists.org/blog/2017/5/11-1 . Accessed 25 Dec 2017.

Weinstein-Jones, F., & Weinstein, Y. (2017). Topic spacing spreadsheet for teachers [Excel macro]. Zenodo. http://doi.org/10.5281/zenodo.573764 . Accessed 25 Dec 2017.

Williams, D. [FurtherEdagogy]. (2016). @doctorwhy @doc_kristy word accompanying the visual? I’m unclear how removing words benefit? Would a flow chart better suit a scientific exp? [Tweet]. Retrieved from https://twitter.com/FurtherEdagogy/status/807356800509104128 . Accessed 25 Dec 2017.

Wood, B. (2017). And now for something a little bit different….[Blog post]. Retrieved from https://justateacherstandinginfrontofaclass.wordpress.com/2017/04/20/and-now-for-something-a-little-bit-different/ . Accessed 25 Dec 2017.

Wooldridge, C. L., Bugg, J. M., McDaniel, M. A., & Liu, Y. (2014). The testing effect with authentic educational materials: a cautionary note. Journal of Applied Research in Memory and Cognition, 3 , 214–221.

Young, C. (2016). Mini-tests. Retrieved from https://colleenyoung.wordpress.com/revision-activities/mini-tests/ . Accessed 25 Dec 2017.

Download references

Acknowledgements

Not applicable.

YW and MAS were partially supported by a grant from The IDEA Center.

Availability of data and materials

Author information, authors and affiliations.

Department of Psychology, University of Massachusetts Lowell, Lowell, MA, USA

Yana Weinstein

Department of Psychology, Boston College, Chestnut Hill, MA, USA

Christopher R. Madan

School of Psychology, University of Nottingham, Nottingham, UK

Department of Psychology, Rhode Island College, Providence, RI, USA

Megan A. Sumeracki

You can also search for this author in PubMed   Google Scholar

Contributions

YW took the lead on writing the “Spaced practice”, “Interleaving”, and “Elaboration” sections. CRM took the lead on writing the “Concrete examples” and “Dual coding” sections. MAS took the lead on writing the “Retrieval practice” section. All authors edited each others’ sections. All authors were involved in the conception and writing of the manuscript. All authors gave approval of the final version.

Corresponding author

Correspondence to Yana Weinstein .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

YW and MAS run a blog, “The Learning Scientists Blog”, which is cited in the tutorial review. The blog does not make money. Free resources on the strategies described in this tutorial review are provided on the blog. Occasionally, YW and MAS are invited by schools/school districts to present research findings from cognitive psychology applied to education.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Cite this article.

Weinstein, Y., Madan, C.R. & Sumeracki, M.A. Teaching the science of learning. Cogn. Research 3 , 2 (2018). https://doi.org/10.1186/s41235-017-0087-y

Download citation

Received : 20 December 2016

Accepted : 02 December 2017

Published : 24 January 2018

DOI : https://doi.org/10.1186/s41235-017-0087-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Adv Med Educ Prof
• v.4(4); 2016 Oct

Effective Teaching Methods in Higher Education: Requirements and Barriers

Nahid shirani bidabadi.

1 Psychology and Educational Sciences School, University of Isfahan, Isfahan, Iran;

AHMMADREZA NASR ISFAHANI

Amir rouhollahi.

2 Department of English, Management and Information School, Isfahan University of Medical Science, Isfahan, Iran;

ROYA KHALILI

3 Quality Improvement in Clinical Education Research Center, Education Development Center, Shiraz University of Medical Sciences, Shiraz, Iran

Introduction:

Teaching is one of the main components in educational planning which is a key factor in conducting educational plans. Despite the importance of good teaching, the outcomes are far from ideal. The present qualitative study aimed to investigate effective teaching in higher education in Iran based on the experiences of best professors in the country and the best local professors of Isfahan University of Technology.

This qualitative content analysis study was conducted through purposeful sampling. Semi-structured interviews were conducted with ten faculty members (3 of them from the best professors in the country and 7 from the best local professors). Content analysis was performed by MAXQDA software. The codes, categories and themes were explored through an inductive process that began from semantic units or direct quotations to general themes.

According to the results of this study, the best teaching approach is the mixed method (student-centered together with teacher-centered) plus educational planning and previous readiness. But whenever the teachers can teach using this method confront with some barriers and requirements; some of these requirements are prerequisite in professors' behavior and some of these are prerequisite in professors’ outlook. Also, there are some major barriers, some of which are associated with the professors’ operation and others are related to laws and regulations. Implications of these findings for teachers’ preparation in education are discussed.

Conclusion:

In the present study, it was illustrated that a good teaching method helps the students to question their preconceptions, and motivates them to learn, by putting them in a situation in which they come to see themselves as the authors of answers, as the agents of responsibility for change. But training through this method has some barriers and requirements. To have an effective teaching; the faculty members of the universities should be awarded of these barriers and requirements as a way to improve teaching quality. The nationally and locally recognized professors are good leaders in providing ideas, insight, and the best strategies to educators who are passionate for effective teaching in the higher education. Finally, it is supposed that there is an important role for nationally and locally recognized professors in higher education to become more involved in the regulation of teaching rules.

Introduction

Rapid changes of modern world have caused the Higher Education System to face a great variety of challenges. Therefore, training more eager, thoughtful individuals in interdisciplinary fields is required ( 1 ). Thus, research and exploration to figure out useful and effective teaching and learning methods are one of the most important necessities of educational systems ( 2 ); Professors have a determining role in training such people in the mentioned field ( 3 ). A university is a place where new ideas germinate; roots strike and grow tall and sturdy. It is a unique space, which covers the entire universe of knowledge. It is a place where creative minds converge, interact with each other and construct visions of new realities. Established notions of truth are challenged in the pursuit of knowledge. To be able to do all this, getting help from experienced teachers can be very useful and effective.

Given the education quality, attention to students’ education as a main product that is expected from education quality system is of much greater demand in comparison to the past. There has always been emphasis on equal attention to research and teaching quality and establishing a bond between these two before making any decision; however, studies show that the already given attention to research in universities does not meet the educational quality requirements.

Attention to this task in higher education is considered as a major one, so in their instruction, educators must pay attention to learners and learning approach; along with these two factors, the educators should move forward to attain new teaching approaches. In the traditional system, instruction was teacher-centered and the students’ needs and interests were not considered. This is when students’ instruction must change into a method in which their needs are considered and as a result of the mentioned method active behavior change occurs in them ( 4 ). Moreover, a large number of graduated students especially bachelor holders do not feel ready enough to work in their related fields ( 5 ). Being dissatisfied with the status quo at any academic institution and then making decision to improve it require much research and assistance from the experts and pioneers of that institute. Giving the aforementioned are necessary, especially in present community of Iran; it seems that no qualitative study has ever been carried out in this area drawing on in-depth reports of recognized university faculties; therefore, in the present study the new global student-centered methods are firstly studied and to explore the ideas of experienced university faculties, some class observations and interviews were done. Then, efficient teaching method and its barriers and requirements were investigated because the faculty ideas about teaching method could be itemized just through a qualitative study.

The study was conducted with a qualitative method using content analysis approach. The design is appropriate for this study because it allows the participants to describe their experiences focusing on factors that may improve the quality of teaching in their own words. Key participants in purposeful sampling consist of three nationally recognized professors introduced based on the criteria of Ministry of Science, Research and Technology (based on education, research, executive and cultural qualifications) and seven other locally recognized professors according to Isfahan University of Technology standards and students votes. The purposive sampling continued until the saturation was reached, i.e. no further information was obtained for the given concept. All the participants had a teaching experience of above 10 years ( Table 1 ). They were first identified and after making appointments, they were briefed about the purpose of the study and they expressed their consent for the interview to be performed. The lack of female nationally recognized professors among respondents (due to lack of them) are restrictions of this research.

The participants’ characteristics

The data were collected using semi-structured in-depth interviews. Interviews began with general topics, such as “Talk about your experiences in effective teaching” and then the participants were asked to describe their perceptions of their expertise. Probing questions were also used to deeply explore conditions, processes, and other factors that the participants recognized as significant. The interview process was largely dependent on the questions that arose in the interaction between the interviewer and interviewees.

In the process of the study, informed consent was obtained from all the participants and they were ensured of the anonymity of their responses and that the audio files will be removed after use; then, after obtaining permission from the participants, the interview was recorded and transcribed verbatim immediately. The interviews were conducted in a private and quiet place and in convenient time. Then, verification of documents and coordination for subsequent interviews were done. The interviews lasted for one hour on average and each interview was conducted in one session with the interviewer’s notes or memos and field notes. Another method of data collection in this study was an unstructured observation in the educational setting. The investigator observed the method of interactions among faculty members and students. The interviews were conducted from November 2014 to April 2015. Each participant was interviewed for one or two sessions. The mean duration of the interviews was 60 minutes. To analyze the data, we used MAXQDA software (version 10, package series) for indexing and charting. Also, we used qualitative content analysis with a conventional approach to analyze the data. The data of the study were directly collected from the experiences of the study participants. The codes, categories and themes were explored through an inductive process, in which the researchers moved from specific to general. The consequently formulated concepts or categories were representative of the participants’ experiences. In content analysis at first, semantic units should be specified, and then the related codes should be extracted and categorized based on their similarities. Finally, in the case of having a high degree of abstraction, the themes can be determined. In the conventional approach, the use of predetermined classes is avoided and classes and their names are allowed to directly come out of the data. To do so, we read the manuscripts and listened to the recorded data for several times until an overall sense was attained. Then, the manuscript was read word by word and the codes were extracted. At the same time, the interviews were continued with other participants and coding of the texts was continued and sub-codes were categorized within the general topics. Then, the codes were classified in categories based on their similarities ( 6 ). Finally, by providing a comprehensive description about the topics, participants, data collection and analysis procedures and limitations of the study, we intend to create transferability so that other researchers clearly follow the research process taken by the researchers.

To improve the accuracy and the rigor of the findings, Lincoln and Cuba’s criteria, including credibility, dependability, conformability, and transferability, were used ( 7 ). To ensure the accuracy of the data, peer review, the researchers’ acceptability, and the long and continuing evaluation through in-depth, prolonged, and repeated interviews and the colleague’s comments must be used ( 8 ). In addition, the findings were repeatedly assessed and checked by supervisors (expert checking) ( 9 ). In this research, the researcher tried to increase the credibility of the data by keeping prolonged engagement in the process of data collection. Then, the accuracy of data analysis was confirmed by one specialist in the field of qualitative research and original codes were checked by some participants to compare the findings with the participants’ experiences. To increase the dependability and conformability of data, maximum variation was observed in the sampling. In addition, to increase the power of data transferability, adequate description of the data was provided in the study for critical review of the findings by other researchers.

Ethical considerations

The aim of the research and interview method was explained to the participants and in the process of the study, informed consent was obtained from all the participants and they were ensured of the anonymity of their responses and that audio files were removed after use. Informed consent for interview and its recording was obtained.

The mean age of faculty members in this study was 54.8 years and all of them were married. According to the results of the study, the best teaching approach was the mixed method one (student-centered with teacher-centered) plus educational planning and previous readiness. Meaning units expressed by professors were divided into 19 codes, 4 categories and 2 themes. In the present study, regarding the Effective Teaching Method in Higher Education, Requirements and Barriers, the experiences and perceptions of general practitioners were explored. As presented in Table 2 , according to data analysis, two themes containing several major categories and codes were extracted. Each code and category is described in more details below.

Examples of extracting codes, categories and themes from raw data

New teaching methods and barriers to the use of these methods

Teachers participating in this study believed that teaching and learning in higher education is a shared process, with responsibilities on both student and teacher to contribute to their success. Within this shared process, higher education must engage the students in questioning their preconceived ideas and their models of how the world works, so that they can reach a higher level of understanding. But students are not always equipped with this challenge, nor are all of them driven by a desire to understand and apply knowledge, but all too often aspire merely to survive the course, or to learn only procedurally in order to get the highest possible marks before rapidly moving on to the next subject. The best teaching helps the students to question their preconceptions, and motivates them to learn, by putting them in a situation in which their existing model does not work and in which they come to see themselves as authors of answers, as agents of responsibility for change. That means, the students need to be faced with problems which they think are important. Also, they believed that most of the developed countries are attempting to use new teaching methods, such as student-centered active methods, problem-based and project-based approaches in education. For example, the faculty number 3 said:

“In a project called EPS (European Project Semester), students come together and work on interdisciplinary issues in international teams. It is a very interesting technique to arouse interest, motivate students, and enhance their skills (Faculty member No. 3).”

The faculty number 8 noted another project-based teaching method that is used nowadays especially to promote education in software engineering and informatics is FLOSS (Free/Liber Open Source Software). In recent years, this project was used to empower the students. They will be allowed to accept the roles in a project and, therefore, deeply engage in the process of software development.

In Iran, many studies have been conducted about new teaching methods. For example, studies by Momeni Danaie ( 10 ), Noroozi ( 11 ), and Zarshenas ( 12 ), have shown various required methods of teaching. They have also concluded that pure lecture, regardless of any feedback ensuring the students learning, have lost their effectiveness. The problem-oriented approach in addition to improving communication skills among students not only increased development of critical thinking but also promoted study skills and an interest in their learning ( 12 ).

In this study, the professors noted that there are some barriers to effective teaching that are mentioned below:

As to the use of new methods of training such as problem-based methods or project-based approach, faculty members No. 4 and 9 remarked that "The need for student-centered teaching is obvious but for some reasons, such as the requirement in the teaching curriculum and the large volume of materials and resources, using these methods is not feasible completely" (Faculty member No. 9).

"If at least in the form of teacher evaluation, some questions were allocated to the use of project-based and problem-based approaches, teachers would try to use them further" (Faculty member No. 2).

The faculty members No. 6 and 7 believed that the lack of motivation in students and the lack of access to educational assistants are considered the reasons for neglecting these methods.

"I think one of the ways that can make student-centered education possible is employing educational assistants (Faculty member No. 6).”

"If each professor could attend crowded classes with two or three assistants, they could divide the class into some groups and assign more practical teamwork while they were carefully supervised (Faculty member No. 7).”

Requirements related to faculty outlook in an effective teaching

Having a successful and effective teaching that creates long-term learning on the part of the students will require certain feelings and attitudes of the teachers. These attitudes and emotions strongly influence their behavior and teaching. In this section, the attitudes of successful teachers are discussed.

Coordination with the overall organizational strategies will allow the educational system to move toward special opportunities for innovation based on the guidelines ( 13 ). The participants, 4, 3, 5 and 8 know that teaching effectively makes sense if the efforts of the professors are aligned with the goals of university.

"If faculty members know themselves as an inseparable part of the university, and proud of their employment in the university and try to promote the aim of training educated people with a high level of scientific expertise of university, it will become their goal, too. Thus, they will try as much as possible to attain this goal" (Faculty member No.9).

When a person begins to learn, according to the value of hope theory, he must feel this is an important learning and believe that he will succeed. Since the feeling of being successful will encourage individuals to learn, you should know that teachers have an important role in this sense ( 14 ). The interviewees’ number 1, 2, 3 and 10 considered factors like interest in youth, trust in ability and respect, as motivating factors for students.

Masters 7 and 8 signified that a master had a holistic and systematic view, determined the position of the teaching subject in a field or in the entire course, know general application of issues and determines them for students, and try to teach interdisciplinary topics. Interviewee No. 5 believed that: "Masters should be aware of the fact that these students are the future of the country and in addition to knowledge, they should provide them with the right attitude and vision" (Faculty member No.5).

Participants No. 2, 4 and 8 considered the faculty members’ passion to teach a lesson as responsible and believed that: "If the a teacher is interested in his field, he/she devotes more time to study the scriptures of his field and regularly updates his information; this awareness in his teaching and its influence on students is also very effective" (Faculty member No. 8).

Requirements related to the behavior and performance of faculty members in effective teaching

Teachers have to focus on mental differences, interest, and sense of belonging, emotional stability, practical experience and scientific level of students in training. Class curriculum planning includes preparation, effective transition of content, and the use of learning and evaluating teaching ( 15 ).

Given the current study subjects’ ideas, the following functional requirements for successful teaching in higher education can be proposed.

According to Choi and Pucker, the most important role of teachers is planning and controlling the educational process for students to be able to achieve a comprehensive learning ( 16 ).

"The fact that many teachers don’t have a predetermined plan on how to teach, and just collect what they should teach in a meeting is one reason for the lack of creativity in teaching" Faculty member No.4).

Klug and colleagues in an article entitled “teaching and learning in education” raise some questions and want the faculty members to ask themselves these questions regularly.

1- How to increase the students' motivation.

2- How to help students feel confident in solving problems.

3- How to teach students to plan their learning activities.

4- How to help them to carry out self-assessment at the end of each lesson.

5- How to encourage the students to motivate them for future work.

6- How I can give feedback to the students and inform them about their individual learning ( 14 ).

Every five faculty members who were interviewed cited the need to explain the lessons in plain language, give feedback to students, and explain the causes and reasons of issues.

"I always pay attention to my role as a model with regular self-assessment; I'm trying to teach this main issue to my students" (Faculty member No. 9).

Improving the quality of learning through the promotion of education, using pre-organizers and conceptual map, emphasizing the student-centered learning and developing the skills needed for employment are the strategies outlined in lifelong learning, particularly in higher education ( 17 ).

"I always give a five to ten-minute summary of the last topic to students at first; if possible, I build up the new lesson upon the previous one" (Faculty member No. 4).

The belief that creative talent is universal and it will be strengthened with appropriate programs is a piece of evidence to prove that innovative features of the programs should be attended to continually ( 18 ). Certainly, in addition to the enumerated powers, appropriate fields should be provided to design new ideas with confidence and purposeful orientation. Otherwise, in the absence of favorable conditions and lack of proper motivations, it will be difficult to apply new ideas ( 19 ). Teacher’s No. 3, 5 and 7 emphasized encouraging the students for creativity: "I always encourage the students to be creative when I teach a topic; for example, after teaching, I express some vague hints and undiscovered issues and ask them what the second move is to improve that process" (Faculty member No.3).

Senior instructors try to engage in self-management and consultation, tracking their usage of classroom management skills and developing action plans to modify their practices based on data. Through consultation, instructors work with their colleagues to collect and implement data to gauge the students’ strengths and weaknesses, and then use protocols to turn the weaknesses into strengths. The most effective teachers monitor progress and assess how their changed practices have impacted the students’ outcomes ( 20 ).

"It is important that what is taught be relevant to the students' career; however, in the future with the same information they have learned in university, they want to work in the industry of their country" (Faculty member No.1).

Skills in documenting the results of the process of teaching-learning cannot only facilitate management in terms of studying the records, but also provides easier access to up to date information ( 21 ). Faculty members No. 7 and 3 stressed the need for documenting learning experiences by faculty.

"I have a notebook in my office that I usually refer to after each class. Then, I write down every successful strategy that was highly regarded by students that day" (Faculty member No.3).

Developing a satisfactory interaction with students

To connect with students and impact their lives personally and professionally, teachers must be student-centered and demonstrate respect for their background, ideologies, beliefs, and learning styles. The best instructors use differentiated instruction, display cultural sensitivity, accentuate open communication, offer positive feedback on the students’ academic performance ( 20 ), and foster student growth by allowing them to resubmit assignments prior to assigning a grade ( 22 ).

"I pay attention to every single student in my class and every time when I see a student in class is not focused on a few consecutive sessions, I ask about his lack of focus and I help him solve his problem" (Faculty member No. 5).

The limitation in this research was little access to other nationally recognized university faculty members; also their tight schedule was among other limitations in this study that kept us several times from interviewing such faculties. To overcome such a problem, they were briefed about the importance of this study and then some appointments were set with them.

This study revealed the effective teaching methods, requirements and barriers in Iranian Higher Education. Teachers participating in this study believed that teaching and learning in higher education is a shared process, with responsibilities on both student and teacher to contribute to their success. Within this shared process, higher education must engage the students in questioning their preconceived ideas and their models of how the world works, so that they can reach a higher level of understanding. They believed that to grow successful people to deal with the challenges in evolving the society, most developed countries are attempting to use new teaching methods in higher education. All these methods are student-centered and are the result of pivotal projects. Research conducted by Momeni Danaei and colleagues also showed that using a combination of various teaching methods together will lead to more effective learning while implementing just one teaching model cannot effectively promote learning ( 10 ). However, based on the faculty member’s experiences, effective teaching methods in higher education have some requirements and barriers.

In this study, barriers according to codes were divided two major categories: professor-related barriers and regulation-related ones; for these reasons, the complete use of these methods is not possible. However, teachers who are aware of the necessity of engaging the student for a better understanding of their content try to use this method as a combination that is class speech presentation and involving students in teaching and learning. This result is consistent with the research findings of Momeni Danaei and colleagues ( 10 ), Zarshenas et al. ( 12 ) and Noroozi ( 11 ).

Using student-centered methods in higher education needs some requirements that according to faculty members who were interviewed, and according to the codes, such requirements for effective teaching can be divided into two categories: First, things to exist in the outlook of faculties about the students and faculties' responsibility towards them, to guide them towards effective teaching methods, the most important of which are adaptation to the organizational strategies, interest in the students and trust in their abilities, systemic approach in higher education, and interest in their discipline.

Second, the necessary requirements should exist in the faculties’ behavior to make their teaching methods more effective. This category emerged from some codes, including having lesson plan; using appropriate educational strategies and metacognition training and self-assessment of students during teaching; using concept and pre-organizer maps in training, knowledge; and explaining how to resolve problems in professional career through teaching discussion, documenting of experience and having satisfactory interaction with the students. This result is consistent with the findings of Klug et al., Byun et al., and Khanyfr et al. ( 14 , 17 , 18 ).

In addition and according to the results, we can conclude that a major challenge for universities, especially at a time of resource constraints, is to organize teaching so as to maximize learning effectiveness. As mentioned earlier, a major barrier to change is the fact that most faculty members are not trained for their teaching role and are largely ignorant of the research literature on effective pedagogy. These findings are in agreement with the research of Knapper, indicating that the best ideas for effective teaching include: Teaching methods that focus on the students’ activity and task performance rather than just acquisition of facts; Opportunities for meaningful personal interaction between the students and teachers; Opportunities for collaborative team learning; More authentic methods of assessment that stress task performance in naturalistic situations, preferably including elements of peer and self-assessment; Making learning processes more explicit, and encouraging the students to reflect on the way they learn; Learning tasks that encourage integration of information and skills from different fields ( 23 ).

In the present study, it was illustrated that a good teaching method helps the students to question their preconceptions, and motivates them to learn, by putting them in a situation in which they come to see themselves as the authors of answers and the agents of responsibility for change. But whenever the teachers can teach by this method, they are faced with some barriers and requirements. Some of these requirements are prerequisite of the professors' behavior and some of these are prerequisite of the professors’ outlook. Also, there are some major barriers some of which are associated with the professors’ behavior and others are related to laws and regulations. Therefore, to have an effective teaching, the faculty members of universities should be aware of these barriers and requirements as a way to improve the teaching quality.

Effective teaching also requires structural changes that can only be brought about by academic leaders. These changes include hiring practices reward structures that recognize the importance of teaching expertise, quality assurance approaches that measure learning processes, outcomes in a much more sophisticated way than routine methods, and changing the way of attaining university accreditation.

The nationally and locally recognized professors are good leaders in providing ideas, insight, and the best strategies to educators who are passionate for effective teaching in the higher education. Finally, it is supposed that there is an important role for nationally and locally recognized professors in higher education to become more involved in the regulation of teaching rules. This will help other university teachers to be familiar with effective teaching and learning procedures. Therefore, curriculum planners and faculty members can improve their teaching methods.

Acknowledgement

The authors would like to thank all research participants of Isfahan University of Technology (faculties) who contributed to this study and spent their time to share their experiences through interviews.

Conflict of Interest: None declared.

A Review of the Literature on Teacher Effectiveness and Student Outcomes

• Open Access
• First Online: 24 May 2019

Cite this chapter

You have full access to this open access chapter

• Nathan Burroughs 25 ,
• Jacqueline Gardner 26 ,
• Youngjun Lee 27 ,
• Siwen Guo 28 ,
• Israel Touitou 29 ,
• Kimberly Jansen 30 &
• William Schmidt 31  

Part of the book series: IEA Research for Education ((IEAR,volume 6))

156k Accesses

46 Citations

74 Altmetric

Researchers agree that teachers are one of the most important school-based resources in determining students’ future academic success and lifetime outcomes, yet have simultaneously had difficulties in defining what teacher characteristics make for an effective teacher. This chapter reviews the large body of literature on measures of teacher effectiveness, underscoring the diversity of methods by which the general construct of “teacher quality” has been explored, including experience, professional knowledge, and opportunity to learn. Each of these concepts comprises a number of different dimensions and methods of operationalizing. Single-country research (and particularly research from the United States) is distinguished from genuinely comparative work. Despite a voluminous research literature on the question of teacher quality, evidence for the impact of teacher characteristics (experience and professional knowledge) on student outcomes remains quite limited. There is a smaller, but more robust set of findings for the effect of teacher support on opportunity to learn. Five measures may be associated with higher student achievement: teacher experience (measured by years of teaching), teacher professional knowledge (measured by education and self-reported preparation to teach mathematics), and teacher provision of opportunity to learn (measured by time on mathematics and content coverage). These factors provide the basis for a comparative cross-country model.

You have full access to this open access chapter,  Download chapter PDF

• Opportunity to learn
• Teacher education
• Teacher experience
• Teacher quality
• Trends in International Mathematics and Science Study (TIMSS)

2.1 Defining Teacher Effectiveness

Researchers agree that teachers are one of the most important school-based resources in determining students’ future academic success and lifetime outcomes (Chetty et al. 2014 ; Rivkin et al. 2005 ; Rockoff 2004 ). As a consequence, there has been a strong emphasis on improving teacher effectiveness as a means to enhancing student learning. Goe ( 2007 ), among others, defined teacher effectiveness in terms of growth in student learning, typically measured by student standardized assessment results. Chetty et al. ( 2014 ) found that students taught by highly effective teachers, as defined by the student growth percentile (SGPs) and value-added measures (VAMs), were more likely to attend college, earn more, live in higher-income neighborhoods, save more money for retirement, and were less likely to have children during their teenage years. This potential of a highly effective teacher to significantly enhance the lives of their students makes it essential that researchers and policymakers properly understand the factors that contribute to a teacher’s effectiveness. However, as we will discuss in more detail later in this report, studies have found mixed results regarding the relationships between specific teacher characteristics and student achievement (Wayne and Youngs 2003 ). In this chapter, we explore these findings, focusing on the three main categories of teacher effectiveness identified and examined in the research literature: namely, teacher experience, teacher knowledge, and teacher behavior. Here we emphasize that much of the existing body of research is based on studies from the United States, and so the applicability of such national research to other contexts remains open to discussion.

2.2 Teacher Experience

Teacher experience refers to the number of years that a teacher has worked as a classroom teacher. Many studies show a positive relationship between teacher experiences and student achievement (Wayne and Youngs 2003 ). For example, using data from 4000 teachers in North Carolina, researchers found that teacher experience was positively related to student achievement in both reading and mathematics (Clotfelter et al. 2006 ). Rice ( 2003 ) found that the relationship between teacher experience and student achievement was most pronounced for students at the secondary level. Additional work in schools in the United States by Wiswall ( 2013 ), Papay and Kraft ( 2015 ), and Ladd and Sorenson ( 2017 ), and a Dutch twin study by Gerritsen et al. ( 2014 ), also indicated that teacher experience had a cumulative effect on student outcomes.

Meanwhile, other studies have failed to identify consistent and statistically significant associations between student achievement and teacher experience (Blomeke et al. 2016 ; Gustaffsson and Nilson 2016 ; Hanushek and Luque 2003 ; Luschei and Chudgar 2011 ; Wilson and Floden 2003 ). Some research from the United States has indicated that experience matters very much early on in a teacher’s career, but that, in later years, there were little to no additional gains (Boyd et al. 2006 ; Rivkin et al. 2005 ; Staiger and Rockoff 2010 ). In the first few years of a teacher’s career, accruing more years of experience seems to be more strongly related to student achievement (Rice 2003 ). Rockoff ( 2004 ) found that, when comparing teacher effectiveness (understood as value-added) to student test scores in reading and mathematics, teacher experience was positively related to student mathematics achievement; however, such positive relationships leveled off after teachers had gained two years of teaching experience. Drawing on data collected from teachers of grades four to eight between 2000 and 2008 within a large urban school district in the United States, Papay and Kraft ( 2015 ) confirmed previous research on the benefits experience can add to a novice teacher’s career. They found that student outcomes increased most rapidly during their teachers’ first few years of employment. They also found some further student gains due to additional years of teaching experience beyond the first five years. The research of Pil and Leana ( 2009 ) adds additional nuance; they found that acquiring teacher experience at the same grade level over a number of years, not just teacher experience in general (i.e. at multiple grades), was positively related to student achievement.

2.3 Teacher Professional Knowledge

A teacher’s professional knowledge refers to their subject-matter knowledge, curricular knowledge, and pedagogical knowledge (Collinson 1999 ). This professional knowledge is influenced by the undergraduate degrees earned by a teacher, the college attended, graduate studies undertaken, and opportunities to engage with on-the job training, commonly referred to as professional development (Collinson 1999 ; Rice 2003 ; Wayne and Youngs 2003 ). After undertaking in-depth quantitative analyses of the United States’ 1993–1994 Schools and Staffing Survey (SASS) and National Assessment of Educational Progress (NAEP) data sets, Darling-Hammond ( 2000 ) argued that measures of teacher preparation and certification were by far the strongest correlates of student achievement in reading and mathematics, after controlling for student poverty levels and language status.

As with experience, research on the impact of teacher advanced degrees, subject specializations, and certification has been inconclusive, with several studies (Aaronson et al. 2007 ; Blomeke et al. 2016 ; Hanushek and Luque 2003 ; Harris and Sass 2011 ; Luschei and Chudgar 2011 ) suggesting weak, inconsistent, or non-significant relationships with student achievement. However, several international studies comparing country means found that teacher degrees (Akiba et al. 2007 ; Gustaffsson and Nilson 2016 ; Montt 2011 ) were related to student outcomes, as did Woessman’s ( 2003 ) student-level study of multiple countries.

2.3.1 Undergraduate Education

In their meta-analysis of teacher effectiveness, Wayne and Youngs ( 2003 ) found three studies that showed some relationship between the quality of the undergraduate institution that a teacher attended and their future students’ success in standardized tests. In a thorough review of the research on teacher effectiveness attributes, Rice ( 2003 ) found that the selectivity of undergraduate institution and the teacher preparation program may be related to student achievement for students at the high school level and for high-poverty students.

In terms of teacher preparation programs, Boyd et al. ( 2009 ) found that overall these programs varied in their effectiveness. In their study of 31 teacher preparation programs designed to prepare teachers for the New York City School District, Boyd et al. ( 2009 ) drew from data based on document analyses, interviews, surveys of teacher preparation instructors, surveys of participants and graduates, and student value-added scores. They found that if a program was effective in preparing teachers to teach one subject, it tended to also have success in preparing teachers to teach other subjects as well. They also found that teacher preparation programs that focused on the practice of teaching and the classroom, and provided opportunities for teachers to study classroom practices, tended to prepare more effective teachers. Finally, they found that programs that included some sort of final project element (such as a personal research paper, or portfolio presentation) tended to prepare more effective teachers.

Beyond the institution a teacher attends, the coursework they choose to take within that program may also be related to their future students’ achievement. These associations vary by subject matter. A study by Rice ( 2003 ) indicated that, for teachers teaching at the secondary level, subject-specific coursework had a greater impact on their future students’ achievement. Similarly Goe ( 2007 ) found that, for mathematics, an increase in the amount of coursework undertaken by a trainee teacher was positively related to their future students’ achievement. By contrast, the meta-analysis completed by Wayne and Youngs ( 2003 ) found that, for history and English teachers, there was no evidence of a relationship between a teacher’s undergraduate coursework and their future students’ achievement in those subjects.

2.3.2 Graduate Education

In a review of 14 studies, Wilson and Floden ( 2003 ) were unable to identify consistent relationships between a teacher’s level of education and their students’ achievement. Similarly, in their review of data from 4000 teachers in North Carolina, Clotfelter et al. ( 2006 ) found that teachers who held a master’s degree were associated with lower student achievement. However, specifically in terms of mathematics instruction, teachers with higher degrees and who undertook more coursework during their education seem to be positively related to their students’ mathematics achievement (Goe 2007 ). Likewise, Harris and Sass ( 2011 ) found that there was a positive relationship between teachers who had obtained an advanced degree during their teaching career and their students’ achievement in middle school mathematics. They did not find any significant relationships between advanced degrees and student achievement in any other subject area. Further, using data from the United States’ Early Childhood Longitudinal Study (ECLS-K), Phillips ( 2010 ) found that subject-specific graduate degrees in elementary or early-childhood education were positively related to students’ reading achievement gains.

2.3.3 Certification Status

Another possible indicator of teacher effectiveness could be whether or not a teacher holds a teaching certificate. Much of this research has focused on the United States, which uses a variety of certification approaches, with lower grades usually having multi-subject general certifications and higher grades requiring certification in specific subjects. Wayne and Youngs ( 2003 ) found no clear relationship between US teachers’ certification status and their students’ achievement, with the exception of the subject area of mathematics, where students tended have higher test scores when their teachers had a standard mathematics certification. Rice ( 2003 ) also found that US teacher certification was related to high school mathematics achievement, and also found that there was some evidence of a relationship between certification status and student achievement in lower grades. Meanwhile, in their study of grade one students, Palardy and Rumberger ( 2008 ) also found evidence that students made greater gains in reading ability when taught by fully certified teachers.

In a longitudinal study using data from teachers teaching grades four and five and their students in the Houston School District in Texas, Darling-Hammond et al. ( 2005 ) found that those teachers who had completed training that resulted in a recognized teaching certificate were more effective that those who had no dedicated teaching qualifications. The study results suggested that teachers without recognized US certification or with non-standard certifications generally had negative effects on student achievement after controlling for student characteristics and prior achievement, as well as the teacher’s experience and degrees. The effects of teacher certification on student achievement were generally much stronger than the effects for teacher experience. Conversely, analyzing data from the ECLS-K, Phillips ( 2010 ) found that grade one students tended to have lower mathematics achievement gains when they had teachers with standard certification. In sum, the literature the influence of teacher certification remains deeply ambiguous.

2.3.4 Professional Development

Although work by Desimone et al. ( 2002 , 2013 ) suggested that professional development may influence the quality of instruction, most researchers found that teachers’ professional development experiences showed only limited associations with their effectiveness, although middle- and high-school mathematics teachers who undertook more content-focused training may be the exception (Blomeke et al. 2016 ; Harris and Sass 2011 ). In their meta-analysis of the effects of professional development on student achievement, Blank and De Las Alas ( 2009 ) found that 16 studies reported significant and positive relationships between professional development and student achievement. For mathematics, the average effect size of studies using a pre-post assessment design was 0.21 standard deviations.

Analyzing the data from six data sets, two from the Beginning Teacher Preparation Survey conducted in Connecticut and Tennessee, and four from the United States National Center for Education Statistics’ National Assessment of Educational Progress (NAEP), Wallace ( 2009 ) used structural equation modeling to find that professional development had a very small, but occasionally statistically significant effect on student achievement. She found, for example, that for NAEP mathematics data from the year 2000, 1.2 additional hours of professional development per year were related to an increase in average student scores of 0.62 points, and for reading, an additional 1.1 h of professional development were related to an average increase in student scores of 0.24 points. Overall, Wallace ( 2009 ) identified professional development had moderate effects on teacher practice and some small effects on student achievement when mediated by teacher practice.

2.3.5 Teacher Content Knowledge

Of course, characteristics like experience and education may be imperfect proxies for teacher content knowledge; unfortunately, content knowledge is difficult to assess directly. However, there is a growing body of work suggesting that teacher content knowledge may associated with student learning. It should be noted that there is an important distinction between general content knowledge about a subject (CK) and pedagogical content knowledge (PCK) specifically related to teaching that subject, each of which may be independently related to student outcomes (Baumert et al. 2010 ).

Studies from the United States (see for example, Chingos and Peterson 2011 ; Clotfelter et al. 2006 ; Constantine et al. 2009 ; Hill et al. 2005 ; Shuls and Trivitt 2015 ) have found some evidence that higher teacher cognitive skills in mathematics are associated with higher student scores. Positive associations between teacher content knowledge and student outcomes were also found in studies based in Germany (Baumert et al. 2010 ) and Peru (Metzler and Woessman 2012 ), and in a comparative study using Programme for the International Assessment of Adult Competencies (PIAAC) data undertaken by Hanushek et al. ( 2018 ). These findings are not universal, however, other studies from the United States (Blazar 2015 ; Garet et al. 2016 ; Rockoff et al. 2011 ) failed to find a statistically significant association between teacher content knowledge and student learning.

The studies we have discussed all used some direct measure of teacher content knowledge. An alternative method of assessing mathematics teacher content knowledge is self-reported teacher preparation to teach mathematics topics. Both TIMSS and IEA’s Teacher Education and Development Study in Mathematics (TEDS-M, conducted in 2007–2008) have included many questions, asking teachers to report on their preparedness to teach particular topics. Although Luschei and Chudgar ( 2011 ) and Gustafsson and Nilson ( 2016 ) found that these items had a weak direct relationship to student achievement across countries, other studies have suggested that readiness is related to instructional quality (Blomeke et al. 2016 ), as well as content knowledge and content preparation (Schmidt et al. 2017 ), suggesting that instructional quality may have an indirect effect on student learning.

2.4 Teacher Behaviors and Opportunity to Learn

Although the impact of teacher characteristics (experience, education, and preparedness to teach) on student outcomes remains an open question, there is much a much more consistent relationship between student achievement and teacher behaviors (instructional time and instructional content), especially behaviors related instructional content. Analyzing TIMSS, Schmidt et al. ( 2001 ) found an association between classroom opportunity to learn (OTL), interpreted narrowly as student exposure to instructional content, and student achievement. In a later study using student-level PISA data, Schmidt et al. ( 2015 ) identified a robust relationship between OTL and mathematics literacy across 62 different educational systems. The importance of instructional content has been recognized by national policymakers, and has helped motivate standards-based reform in an effort to improve student achievement, such as the Common Core in the United States (Common Core Standards Initiative 2018 ). However, we found that there was little research on whether teacher instructional content that aligned with national standards had improved student learning; the only study that we were able to identify found that such alignment had only very weak associations with student mathematics scores (Polikoff and Porter 2014 ). Student-reported data indicates that instructional time (understood as classroom time on a particular subject) does seem to be related to mathematics achievement (Cattaneo et al. 2016 ; Jerrim et al. 2017 ; Lavy 2015 ; Rivkin and Schiman 2015 ; Woessman 2003 ).

2.5 Conclusion

This review of the literature simply brushes the surface of the exceptional body of work on the relationship between student achievement and teacher characteristics and behaviors. Whether analyzing US-based, international, or the (limited) number of comparative studies, the associations between easily measurable teacher characteristics, like experience and education, and student outcomes in mathematics, remains debatable. In contrast, there is more evidence to support the impact of teacher behaviors, such as instructional content and time on task, on student achievement. Our goal was to incorporate all these factors into a comparative model across countries, with the aim of determining what an international cross-national study like TIMSS could reveal about the influence of teachers on student outcomes in mathematics. The analysis that follows draws on the existing body of literature on teacher effectiveness, which identified key teacher factors that may be associated with higher student achievement: teacher experience, teacher professional knowledge (measured by education and self-reported preparation to teach mathematics), and teacher provision of opportunity to learn (time on mathematics and content coverage).

Aaronson, D., Barrow, L., & Sander, W. (2007). Teachers and student achievement in the Chicago public high schools. Journal of Labor Economics, 25 (1), 95–135.

Article   Google Scholar  

Akiba, M., LeTendre, G., & Scribner, J. (2007). Teacher quality, opportunity gap, and national achievement in 46 countries. Educational Researcher, 36 (7), 369–387.

Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., et al. (2010). Teachers’ mathematical knowledge, cognitive activation in the classroom, and student progress. American Educational Research Journal, 47 (1), 133–180.

Blank, R. K., & De Las Alas, N. (2009). The effects of teacher professional development on gains in student achievement: How meta analysis provides scientific evidence useful to education leaders . Washington, DC: Council of Chief State School Officers. Retrieved from https://files.eric.ed.gov/fulltext/ED544700.pdf .

Blazar, D. (2015). Effective teaching in elementary mathematics: Identifying classroom practices that support student achievement. Economics of Education Review, 48, 16–29.

Blomeke, S., Olsen, R., & Suhl, U. (2016). Relation of student achievement to the quality of their teachers and instructional quality. In T. Nilson & J. Gustafsson (Eds.), Teacher quality, instructional quality and student outcomes . IEA Research for Education (Vol. 2, pp. 21–50). Cham, Switzerland: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-41252-8_2 .

Boyd, D., Grossman, P., Lankford, H., & Loeb, S. (2006). How changes in entry requirements alter the teacher workforce and affect student achievement. Education Finance and Policy, 1 (2), 176–216.

Boyd, D. J., Grossman, P. L., Lankford, H., Loeb, S., & Wyckoff, J. (2009). Teacher preparation and student achievement. Educational Evaluation and Policy Analysis, 31 (4), 416–440.

Cattaneo, M., Oggenfuss, C., & Wolter, S. (2016). The more, the better? The impact of instructional time on student performance. Institute for the Study of Labor (IZA) Discussion Paper No. 9797. Bonn, Germany: Forschungsinstitut zur Zukunft der Arbeit/Institute for the Study of Labor. Retrieved from ftp.iza.org/dp9797.pdf .

Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014). Measuring the impacts of teachers II: Teacher value-added and student outcomes in adulthood. American Economic Review, 104 (9), 2633–2679.

Chingos, M., & Peterson, P. (2011). It’s easier to pick a good teacher than to train one: Familiar and new results on the correlates of teacher effectiveness. Economics of Education Review, 30 (3), 449–465.

Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2006). Teacher-student matching and the assessment of teacher effectiveness. Journal of Human Resources, 41 (4), 778–820.

Collinson, V. (1999). Redefining teacher excellence. Theory Into Practice , 38 (1), 4–11. Retrieved from https://doi.org/10.1080/00405849909543824 .

Common Core Standards Initiative. (2018). Preparing America’s students for success [www document]. Retrieved from http://www.corestandards.org/ .

Constantine, J., Player, D., Silva, T., Hallgren, K., Grider, M., & Deke, J. (2009). An evaluation of teachers trained through different routes to certification. National Center for Education Evaluation and Regional Assistance paper 2009-4043. Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, US Department of Education. Retrieved from https://ies.ed.gov/ncee/pubs/20094043/ .

Darling-Hammond, L. (2000). Teacher quality and student achievement. Education Policy Analysis Archives , 8, 1. Retrieved from https://epaa.asu.edu/ojs/article/viewFile/392/515 .

Darling-Hammond, L., Holtzman, D. J., Gatlin, S. J., & Vasquez Heilig, J. (2005). Does teacher preparation matter? Evidence about teacher certification, Teach for America, and teacher effectiveness. Education Policy Analysis Archives, 13, 42. Retrieved from https://epaa.asu.edu/ojs/article/view/147 .

Desimone, L., Porter, A., Garet, M., Yoon, K., & Birman, B. (2002). Effects of professional development on teachers’ instruction: Results from a three-year longitudinal study. Education Evaluation and Policy Analysis, 24 (2), 81–112.

Desimone, L., Smith, T., & Phillips, K. (2013). Teacher and administrator responses to standards-based reform. Teachers College Record, 115 (8), 1–53.

Google Scholar  

Garet, M. S., Heppen, J. B., Walters, K., Parkinson, J., Smith, T. M., Song, M., et al. (2016). Focusing on mathematical knowledge: The impact of content-intensive teacher professional development . National Center for Education Evaluation and Regional Assistance paper 2016-4010. Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, US Department of Education. Retrieved from https://ies.ed.gov/ncee/pubs/20094010/ .

Gerritsen, S., Plug, E., & Webbink, D. (2014). Teacher quality and student achievement: Evidence from a Dutch sample of twins . CPB discussion paper 294. The Hague, The Netherlands: Central Plan Bureau/Netherlands Bureau for Economic Policy Analysis. Retrieved from https://ideas.repec.org/p/cpb/discus/294.html .

Goe, L. (2007). The link between teacher quality and student outcomes: A research synthesis . NCCTQ Report. Washington, DC: National Comprehensive Center for Teacher Quality. Retrieved from http://www.gtlcenter.org/sites/default/files/docs/LinkBetweenTQandStudentOutcomes.pdf .

Gustafsson, J., & Nilson, T. (2016). The impact of school climate and teacher quality on mathematics achievement: A difference-in-differences approach. In T. Nilson & J. Gustafsson (Eds.), Teacher quality, instructional quality and student outcomes , IEA Research for Education (Vol. 2, pp. 81–95). Cham, Switzerland: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-41252-8_4 .

Hanushek, E., & Luque, J. (2003). Efficiency and equity in schools around the world. Economics of Education Review, 22 (5), 481–502.

Hanushek, E., Piopiunik, M., & Wiederhold, S. (2018). The value of smarter teachers: International evidence on teacher cognitive skills and student performance . Journal of Human Resources (in press). https://doi.org/10.3368/jhr.55.1.0317.8619r1 .

Harris, D. N., & Sass, T. R. (2011). Teacher training, teacher quality and student achievement. Journal of Public Economics, 95 (7–8), 798–812.

Hill, H., Rowan, B., & Ball, D. (2005). Effects of teachers’ mathematical knowledge for teaching on student achievement. American Educational Research Journal, 42 (2), 371–406.

Jerrim, J., Lopez-Agudo, L., Marcenaro-Gutierrez, O., & Shure, N. (2017). What happens when econometrics and psychometrics collide? An example using the PISA data. Economics of Education Review, 61, 51–58.

Ladd, H. F., & Sorenson, L. C. (2017). Returns to teacher experience: Student achievement and motivation in middle school. Education Finance and Policy, 12 (2), 241–279. Retrieved from https://www.mitpressjournals.org/doi/10.1162/EDFP_a_00194 .

Lavy, V. (2015). Do differences in schools’ instruction time explain international achievement gaps? Evidence from developed and developing countries. The Economic Journal, 125 (11), 397–424.

Luschei, T., & Chudgar, A. (2011). Teachers, student achievement, and national income: A cross-national examination of relationships and interactions. Prospects, 41, 507–533.

Metzler, J., & Woessman, L. (2012). The impact of teacher subject knowledge on student achievement: Evidence from within-teacher within-student variation. Journal of Development Economics, 99 (2), 486–496.

Montt, G. (2011). Cross-national differences in educational achievement inequality. Sociology of Education, 84 (1), 49–68.

Palardy, G. J., & Rumberger, R. W. (2008). Teacher effectiveness in first grade: The importance of background qualifications, attitudes, and instructional practices for student learning. Educational Evaluation and Policy Analysis, 30 (2), 111–140.

Papay, J., & Kraft, M. (2015). Productivity returns to experience in the teacher labor market: Methodological challenges and new evidence on long-term career improvement. Journal of Public Economics, 130, 105–119.

Phillips, K. J. (2010). What does ‘highly qualified’ mean for student achievement? Evaluating the relationships between teacher quality indicators and at-risk students’ mathematics and reading achievement gains in first grade. The Elementary School Journal, 110 (4), 464–493.

Pil, F. K., & Leana, C. (2009). Applying organizational research to public school reform: The effects of teacher human and social capital on student performance. Academy of Management Journal , 52 (6), 1101–1124.

Polikoff, M., & Porter, A. (2014). Instructional alignment as a measure of teaching quality. Educational Evaluation and Policy Analysis, 36 (4), 399–416.

Rice, J. K. (2003). Teacher quality: Understanding the effectiveness of teacher attributes . Washington DC: Economic Policy Institute.

Rivkin, S., Hanushek, E., & Kain, J. (2005). Teachers, schools, and academic achievement. Econometrica, 73 (2), 417–458.

Rivkin, S., & Schiman, J. (2015). Instruction time, classroom quality, and academic achievement. The Economic Journal, 125 (11), 425–448.

Rockoff, J. (2004). The impact of individual teachers on student achievement: Evidence from panel data. The American Economic Review, 94 (2), 247–252.

Rockoff, J. E., Jacob, B. A., Kane, T. J., & Staiger, D. O. (2011). Can you recognize an effective teacher when you recruit one? Education Finance and Policy, 6 (1), 43–74.

Schmidt, W., Burroughs, N., Cogan, L., & Houang, R. (2017). The role of subject-matter content in teacher preparation: An international perspective for mathematics. Journal of Curriculum Studies, 49 (2), 111–131.

Schmidt, W., Burroughs, N., Zoido, P., & Houang, R. (2015). The role of schooling in perpetuating educational inequality: An international perspective. Education Researcher, 44 (4), 371–386.

Schmidt, W., McKnight, C., Houang, R., Wang, H., Wiley, D., Cogan, L., et al. (2001). Why schools matter: A cross-national comparison of curriculum and learning . San Francisco, CA: Jossey-Bass.

Shuls, J., & Trivitt, J. (2015). Teacher effectiveness: An analysis of licensure screens. Educational Policy, 29 (4), 645–675.

Staiger, D., & Rockoff, J. (2010). Searching for effective teachers with imperfect information. Journal of Economic Perspectives, 24 (3), 97–118.

Wallace, M. R. (2009). Making sense of the links: Professional development, teacher practices, and student achievement. Teachers College Record, 111 (2), 573–596.

Wayne, A. J., & Youngs, P. (2003). Teacher characteristics and student achievement gains: A review. Review of Educational Research, 73 (1), 89–122.

Wilson, S. M., & Floden, R. E. (2003). Creating effective teachers: Concise answers for hard questions. An addendum to the report “Teacher preparation research: Current knowledge, gaps, and recommendations . Washington, DC: AACTE Publications.

Wiswall, M. (2013). The dynamics of teacher quality. Journal of Public Economics, 100, 61–78.

Woessman, L. (2003). Schooling resources, educational institutions, and student performance: The international evidence. Oxford Bulletin of Economics and Statistics, 65 (2), 117–170.

Download references

Author information

Authors and affiliations.

CREATE for STEM, Michigan State University, East Lansing, MI, USA

Nathan Burroughs

Office of K-12 Outreach, Michigan State University, East Lansing, MI, USA

Jacqueline Gardner

Michigan State University, East Lansing, MI, USA

Youngjun Lee

College of Education, Michigan State University, East Lansing, MI, USA

Israel Touitou

Kimberly Jansen

Center for the Study of Curriculum Policy, Michigan State University, East Lansing, MI, USA

William Schmidt

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Nathan Burroughs .

Rights and permissions

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions

Copyright information

© 2019 International Association for the Evaluation of Educational Achievement (IEA)

About this chapter

Burroughs, N. et al. (2019). A Review of the Literature on Teacher Effectiveness and Student Outcomes. In: Teaching for Excellence and Equity. IEA Research for Education, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-030-16151-4_2

Download citation

DOI : https://doi.org/10.1007/978-3-030-16151-4_2

Published : 24 May 2019

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-16150-7

Online ISBN : 978-3-030-16151-4

eBook Packages : Education Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Search NYU Steinhardt

The effect of the teacher's teaching style on students' motivation.

SUBMITTED BY:  MARIA THERESA BARBEROS,  ARNOLD GOZALO,  EUBERTA PADAYOGDOG  SUBMITTED TO:  LEE TZONGJIN, Ed.D.  CHAPTER I  THE EFFECT OF TEACHERS' TEACHING STYLE ON STUDENTS' MOTIVATION

Introduction

The teachers, being the focal figure in education, must be competent and knowledgeable in order to impart the knowledge they could give to their students. Good teaching is a very personal manner. Effective teaching is concerned with the student as a person and with his general development. The teacher must recognize individual differences among his/her students and adjust instructions that best suit to the learners. It is always a fact that as educators, we play varied and vital roles in the classroom. Teachers are considered the light in the classroom. We are entrusted with so many responsibilities that range from the very simple to most complex and very challenging jobs. Everyday we encounter them as part of the work or mission that we are in. It is very necessary that we need to understand the need to be motivated in doing our work well, so as to have motivated learners in the classroom. When students are motivated, then learning will easily take place. However, motivating students to learn requires a very challenging role on the part of the teacher. It requires a variety of teaching styles or techniques just to capture students' interests. Above all, the teacher must himself come into possession of adequate knowledge of the objectives and standards of the curriculum, skills in teaching, interests, appreciation and ideals. He needs to exert effort to lead children or students into a life that is large, full, stimulating and satisfying. Some students seem naturally enthusiastic about learning, but many need or expect their instructors or teachers to inspire, challenge or stimulate them. "Effective learning in the classroom depends on the teacher's ability to maintain the interest that brought students to the course in the first place (Erickson, 1978). Not all students are motivated by the same values, needs, desires and wants. Some students are motivated by the approval of others or by overcoming challenges.

Teachers must recognize the diversity and complexity in the classroom, be it the ethnicity, gender, culture, language abilities and interests. Getting students to work and learn in class is largely influenced in all these areas. Classroom diversity exists not only among students and their peers but may be also exacerbated by language and cultural differences between teachers and students.

Since 2003, many foreign professional teachers, particularly from the Philippines, came to New York City to teach with little knowledge of American school settings. Filipino teachers have distinct styles and expressions of teaching. They expect that: education is interactive and spontaneous; teachers and students work together in the teaching-learning process; students learn through participation and interaction; homework is only part of the process; teaching is an active process; students are not passive learners; factual information is readily available; problem solving, creativity and critical thinking are more important; teachers should facilitate and model problem solving; students learn by being actively engaged in the process; and teachers need to be questioned and challenged. However, many Filipino teachers encountered many difficulties in teaching in NYC public schools. Some of these problems may be attributed to: students' behavior such as attention deficiency, hyperactivity disorder, and disrespect among others; and language barriers such as accent and poor understanding of languages other than English (e.g. Spanish).

As has been said, what happens in the classroom depends on the teacher's ability to maintain students' interests. Thus, teachers play a vital role in effecting classroom changes.

As stressed in the Educator's Diary published in 1995, "teaching takes place only when learning does." Considering one's teaching style and how it affects students' motivation greatly concerns the researchers. Although we might think of other factors, however, emphasis has been geared towards the effect of teacher's teaching style and student motivation.

Hypothesis:

If teacher's teaching style would fit in a class and is used consistently, then students are motivated to learn.

Purpose of the Study

The main thrust of the study was to find out the effect of the teacher's teaching style on students' motivation.

Action Research Questions

This paper attempted to answer specific questions such as: 1. What is the effect of teacher's teaching style using English As A Second Language Strategies on student's motivation? 2. How does teacher's teaching style affect students' motivation? 3. What could be some categories that make one's teaching style effective in motivating students?

Research Design/Methods of Collecting Data

The descriptive-survey method was used in this study, and descriptive means that surveys are made in order to discover some aspects of teacher's teaching style and the word survey denotes an investigation of a field to ascertain the typical condition is obtaining. The researchers used questionnaires, observations, interviews, students' class work and other student outputs for this study. The questionnaires were administered before and after ESL strategies were applied. Observation refers to what he/she sees taking place in the classroom based on student's daily participation. Student interviews were done informally before, during, and after classes. Several categories affecting motivation were being presented in the questionnaire.

Research Environment and Respondents

The research was conducted at IS 164 and IS 143 where three teachers conducting this research were the subjects and the students of these teachers selected randomly specifically in the eighth and sixth grade. The student respondents were the researchers' own students, where 6 to 7 students from each teacher were selected. Twenty students were used as samples.

To measure students' motivation, researchers used questionnaires which covered important categories, namely: attitudes, student's participation, homework, and grades. Open-ended questions were also given for students' opinion, ideas and feelings towards the teacher and the subject. The teacher's teaching style covers the various scaffolding strategies. The data that were collected from this research helped the teachers to evaluate their strengths and weaknesses so as to improve instruction. The results of this study could benefit both teachers and students.

Research Procedure

Data gathering.

The researchers personally distributed the questionnaires. Each item in each category ranges from a scale of 5-1 where 5 rated as Strongly Agree while 1 as Strongly Disagree. The questionnaires were collected and data obtained were tabulated in tables and interpreted using the simple percentage. While the open ended questions, answers that were given by the students with the most frequency were noted.

Review of Related Literature

Helping students understand better in the classroom is one of the primary concerns of every teacher. Teachers need to motivate students how to learn. According to Phil Schlecty (1994), students who understand the lesson tend to be more engaged and show different characteristics such as they are attracted to do work, persist in the work despite challenges and obstacles, and take visible delight in accomplishing their work. In developing students' understanding to learn important concepts, teacher may use a variety of teaching strategies that would work best for her/his students. According to Raymond Wlodkowski and Margery Ginsberg (1995), research has shown no teaching strategy that will consistently engage all learners. The key is helping students relate lesson content to their own backgrounds which would include students' prior knowledge in understanding new concepts. Due recognition should be given to the fact that interest, according to Saucier (1989:167) directly or indirectly contributes to all learning. Yet, it appears that many teachers apparently still need to accept this fundamental principle. Teachers should mind the chief component of interest in the classroom. It is a means of forming lasting effort in attaining the skills needed for life. Furthermore teachers need to vary teaching styles and techniques so as not to cause boredom to the students in the classroom. Seeking greater insight into how children learn from the way teachers discuss and handle the lesson in the classroom and teach students the life skills they need, could be one of the greatest achievements in the teaching process.

Furthermore, researchers have begun to identify some aspects of the teaching situation that help enhance students' motivation. Research made by Lucas (1990), Weinert and Kluwe (1987) show that several styles could be employed by the teachers to encourage students to become self motivated independent learners. As identified, teachers must give frequent positive feedback that supports students' beliefs that they can do well; ensure opportunities for students' success by assigning tasks that are either too easy nor too difficult; help students find personal meaning and value in the material; and help students feel that they are valued members of a learning community. According to Brock (1976), Cashin (1979) and Lucas (1990), it is necessary for teachers to work from students' strengths and interests by finding out why students are in your class and what are their expectations. Therefore it is important to take into consideration students' needs and interests so as to focus instruction that is applicable to different groups of students with different levels.

CHAPTER II  PRESENTATION, ANALYSIS AND INTERPRETATION OF DATA

This chapter presents and analyzes data that answer the subsidiary problems of the study. Table I showed that out of the 20 student respondents, 50% were males and 50% females. Of the male students respondents, only 2 males belong to the high group while 8 males from the low group. For the females, each of the group had 5 respondents. It also showed that there were 7 respondents from the high group and 13 came from the low group.

Table 1:Respondents by Gender

Table 2 showed that out of the 20 students respondents, 80% of students were of Hispanic origin; 10% of respondents were White (not of Hispanic origin); and 10% were Black (not of Hispanic origin); while 0% were of American Indian, Asian or Pacific Islander ethnicity. The results also showed that among the Hispanic, 40% came from the low and 40% came from the high group. There were only 10% White respondents from both groups. There were 10% respondents who were Black from both groups.

Table 2: Respondents by Ethnicity

Table 3 showed that 15% of the respondents had grades between 96-100 in Science, 0% between 91-95, while 15% scored between 86-90, the same as the range between 81-85. However, on the low group 25% of the respondents had grades between 71-75, 5% each had a range between 66-70 and 61-65; while 15% of the respondents did not have Science last year.

Table 3: Grades in Science

Table 4 revealed that for students' motivation-attitude, more than half of the respondents agreed that they are always excited to attend classes this school year. 75% of the students believed that Science is fun and interesting. Similarly, 80% of the respondents agreed that Science is important for them and 60% said that they love Science.

For student motivation-participation, it showed that more than half of the respondents affirm that they are always prepared in their Science classes. 75% of the students participated in Science activities; 50% did their Science assignments consistently.

For student motivation-homework, it could be noted that 60% of the students completed their homework on time and 50% found homework useful and important. 85% of the students said that they got enough support to do homework at home and 90% said that the teachers checked their homework.

For student motivation-grades, 65% got good grades in Science. 65% of the respondents said that they study their lessons before a test or a quiz. More than half of the respondents disagreed that the terms or words used in the test were difficult to understand. Less than half of the respondents agreed tests measure their understanding of Science concepts and knowledge, while 80% thought that grading is fair. On the other hand, the data under teaching style as noted on table 4 showed that 65% of the students strongly agreed that they have a good relationship with their Science teacher and no one disagreed. 75% noted that their Science teachers used materials that were easy to understand. 60% said that their teachers presented the lessons in many ways. More than half of the students said that they understood the way their Science teachers explained the lesson while 25% were not sure of their answer. 75% said that they got feedback from their Science teacher.

Table 4: Data on the Five Categories

Using AI to Implement Effective Teaching Strategies in Classrooms: Five Strategies, Including Prompts

The Wharton School Research Paper

26 Pages Posted: 24 Mar 2023

Ethan R. Mollick

University of Pennsylvania - Wharton School

Lilach Mollick

Date Written: March 17, 2023

This paper provides guidance for using AI to quickly and easily implement evidence-based teaching strategies that instructors can integrate into their teaching. We discuss five teaching strategies that have proven value but are hard to implement in practice due to time and effort constraints. We show how AI can help instructors create material that supports these strategies and improve student learning. The strategies include providing multiple examples and explanations; uncovering and addressing student misconceptions; frequent low-stakes testing; assessing student learning; and distributed practice. The paper provides guidelines for how AI can support each strategy, and discusses both the promises and perils of this approach, arguing that AI may act as a “force multiplier” for instructors if implemented cautiously and thoughtfully in service of evidence-based teaching practices.

Keywords: AI, GPT4, ChatGPT, Learning

Suggested Citation: Suggested Citation

Ethan R. Mollick (Contact Author)

University of pennsylvania - wharton school ( email ).

The Wharton School Philadelphia, PA 19104-6370 United States

3641 Locust Walk Philadelphia, PA 19104-6365 United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics, related ejournals, pedagogy ejournal.

Subscribe to this fee journal for more curated articles on this topic

The Wharton School, University of Pennsylvania Research Paper Series

Subscribe to this free journal for more curated articles on this topic

Artificial Intelligence eJournal

Educational technology, media & library science ejournal, teacher education ejournal, environment for innovation ejournal, cognitive psychology ejournal, psychology research methods ejournal, libraries & information technology ejournal, educational & school psychology ejournal.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

effective teaching strategies: A research paper

Related Papers

NAZREEN RAHMAN

Azizah Rajab

Zbornik Odseka za pedagogiju

Sonja Petrovska

Lap Lambert Academic Publisher Publishing

Jayanta Mete

Teacher Education is a book with the objectives of bringing forth all the issues of teacher education. The issue of teaching learning situation in present scenario has become one of the most important points of discussion. This book has tried to throw light on how teaching’s presence in all the spheres of education has become important to teacher. Towards Teacher Education aims to enhance theory, action research, and practice in teaching and teacher education through the publication of review papers concerned with the analysis of teaching, teaching effectiveness, professionalization in teaching, Implementation of curricula, practicing different schools, the factors that determine teachers thought processes and performances, the social policies that affect teachers in all aspects and stages of their careers and current problems in teaching process. The book is especially brought forth for the educationists, students, teachers, researchers and common people to enlighten them upon teacher education. The book will recognize that discipline – teacher education has important contributions to make to the achievement of its goals. Almost all perspectives have been taken into consideration in the worthy discussions of the experts and the Authors welcome contributions from them. A reader can get complete concept about the areas, policy, effectiveness, quality, practices and researches of teaching in Teacher Education.

Mark Vincent

Harmawati Umma

Feyza Doyran

Adesoye Sunday

Tanzeela Urooj

Dr. Nakul Baniya

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Where are the costs using an economic analysis of educational interventions approach to improve the evaluation of a regional school improvement programme.

1. Introduction

2. formative assessment in policy and practice.

• Making observations: The teacher needs to explore what the learner does or does not know, and this is typically achieved by listening to learners’ responses, observing the learner on tasks, and/or assessing class or homework tasks.
• Interpretation: The teacher interprets the skill, knowledge, or attitudes of the learners.
• Judgement: Once evidence has been gathered through observation and interpretation, the teacher then makes a judgement on the next course of action to move the learner forward.
• Sharing Learning Expectations: Ensuring the learner knows what they are going to learn and the success criteria to achieve this goal.
• Questioning: Using effective questioning to facilitate learning.
• Feedback: Providing feedback that enhances learning within the moment.
• Self-assessment: Allowing learners to take ownership of, and reflect on, their learning.
• Peer assessment: Providing opportunities for learners to discuss their work with, and to instruct, others.

3.1. Trial Design

3.2. recruitment, 3.3. study population, 3.4. outcomes, 3.5. analysis, 3.6. interviews, 3.7. focus group, 3.8. procedure, 3.9. observations.

• Is it clear what the teacher intends the students to learn?
• Does the teacher identify student learning needs?
• Do students understand what criteria will make their work successful?
• Are students chosen at random to answer questions?
• Does the teacher ask questions that make students think?
• Does the teacher give students time to think after asking a question?
• Does the teacher allow time for students to elaborate their responses?
• Is a whole-class response system used?
• Is teaching adjusted after gathering feedback from pupils (data collection)?
• Is there more student talk than teacher talk?
• Are most students involved in answering questions?
• Are students supporting each other’s learning?
• Is there evidence that various forms of teacher feedback advance student learning?
• Do students take responsibility for their own learning?
• Does the teacher provide oral formative feedback?
• Does the teacher find out what the students have learned before they leave the room?

4. Intervention

5. economic analysis of educational interventions (eaei), 5.1. cost-consequence analysis, 5.2. rationale for cca, 5.3. cost collecting methodology, 5.4. collating costs, 5.5. sensitivity analysis, 6.1. learner outcomes, 6.2. classroom observations, 6.3. the opinions of teachers and learners, 6.4. interviews with teachers.

“…helps me feel I get a greater understanding of my children. And I don’t go home at the end of the week thinking, I don’t think I’ve said five words to that child.” Teacher 6

“And sometimes it can be a little bit of idleness of picking up a pen but sometimes it’s their belief in themselves a lot of the time. And it is, it’s them thinking, “actually, I can do it”. “I think a lot of it is the confidence they have…” Teacher 4

“So, it’s easier. I think the quality of work is easier to mark…I do feel I’ve got extra time.” Teacher 2

“I would say predominantly it’s that the lower achievers it’s had the bigger impact on.” Teacher 5

6.5. Focus Group Interviews with Learners

“You kind of get to know them more, cos like…you just like…you don’t really play with them, cos you like different things, but if you’re discussion partners, you might have to try and get to know them…You might think better of them.” Learner, school L

“It makes the work a bit more straight forward, Cos when you look at the success criteria when you’re working, then it like gives you more to think about it and then more to think about the work.” Learner, school P

“…the teacher will take you out of the lessons and things just to like go over your piece of work and if you’ve done something well, he’ll tell you what you’ve done well and he’ll like highlight it on the success criteria, which is a list of things that you have to do and he’ll highlight it pink and then if you need to do something better, he’ll highlight it green and then he’ll tell you to re-do it and he’ll tell you what to re-do and stuff.” Learner, school O

The teachers discussed how FAIP supported them to help learners better understand the nature of successful outcomes and understand the expectations of quality standards in their work. Additionally, learners described how success criteria helped them complete tasks more successfully.

Teachers identified how using the strategies contained within the FAIP training helped them better understand where the learners were in their learning and provided them with useful, additional information to plan next steps. Teachers also indicated that they were more able to identify which learners needed support and adapt teaching in real time to provide next steps advice and support to learners.

Teachers discussed how feedback strategies supported them to better understand learner progress. Some teachers discussed being able to give immediate feedback to support learners to improve the outcomes they achieve.

Learners identified how the use of a range of self-assessment strategies impacted positively on the learning process and how it helped them engage with, and complete, tasks more successfully.

Teachers identified improved opportunities for learners to discuss their own work to enhance understanding and knowledge. Learners understood what a talk partner was, and how it helped them with their learning. It also enabled them to provide support for other learners. Learners also identified how it improved social relationships in school.

6.6. The Full Economic Cost of FAIP for Tier 2 Teachers

6.7. sensitivity analysis, 6.7.1. sensitivity analysis 1, 6.7.2. sensitivity analysis 2, 6.7.3. sensitivity analysis 3, 6.7.4. sensitivity analysis 4, 7. discussion, 8. limitations, 9. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

• Gorard, S.; See, B.H.; Siddiqui, N. What is the evidence on the best way to get evidence into use in education? Rev. Educ. 2020 , 8 , 570–610. [ Google Scholar ] [ CrossRef ]
• OECD. Value for Money in School Education: Smart Investments, Quality Outcomes, Equal Opportunities ; OECD Publishing: Paris, France, 2022. [ Google Scholar ] [ CrossRef ]
• Pegram, J.; Watkins, R.C.; Hoerger, M.; Hughes, J.C. Assessing the range and evidence-base of interventions in a cluster of schools. Rev. Educ. 2022 , 10 , e3336. [ Google Scholar ] [ CrossRef ]
• Slavin, R.E. How evidence-based reform will transform research and practice in education. Educ. Psychol. 2020 , 55 , 21–31. [ Google Scholar ] [ CrossRef ]
• Levin, H. Waiting for Godot: Cost-effectiveness analysis in education. New Dir. Eval. 2001 , 2001 , 55–68. [ Google Scholar ] [ CrossRef ]
• Owen, K.L.; Watkins, R.C.; Hughes, J.C. From evidence-informed to evidence-based: An evidence building framework for education. Rev. Educ. 2022 , 10 , e3342. [ Google Scholar ] [ CrossRef ]
• Hummel-Rossi, B.; Ashdown, J. The state of cost-benefit and cost-effectiveness analyses in education. Rev. Educ. Res. 2002 , 72 , 1–30. [ Google Scholar ] [ CrossRef ]
• Machin, S.; Mcnally, S.; Wyness, G. Educational Research Educational attainment across the UK nations: Performance, inequality and evidence Educational attainment across the UK nations: Performance, inequality and evidence. Educ. Res. 2013 , 55 , 139–164. [ Google Scholar ] [ CrossRef ]
• Kraft, M.A. Interpreting Effect Sizes of Education Interventions. Educ. Res. 2020 , 49 , 241–253. [ Google Scholar ] [ CrossRef ]
• EEF. Cost Evaluation Guidance for EEF Evaluations. 2019. Available online: https://d2tic4wvo1iusb.cloudfront.net/documents/evaluation/evaluation-design/Cost_Evaluation_Guidance_2019.12.11.pdf (accessed on 9 January 2024).
• CBCSE. Centre for Benefit Cost Studies in Education. 2015. Available online: https://www.cbcse.org/costout (accessed on 8 January 2024).
• Hinde, S.; Walker, S.M.; Lortie-Forgues, H. Applying the Three Core Concepts of Economic Evaluation in Health to Education in the UK ; Centre for Health Economics, University of York: York, UK, 2019. [ Google Scholar ]
• Quinn, B.; Van Mondfrans, A.; Worthen, B.R. Cost-Effectiveness of Two Math Programs as Moderated by Pupil SES. Educ. Eval. Policy Anal. 1984 , 6 , 39–52. [ Google Scholar ] [ CrossRef ]
• Hollands, F.; Bowden, A.B.; Belfield, C.; Levin, H.M.; Cheng, H.; Shand, R.; Pan, Y.; Hanisch-Cerda, B. Cost-Effectiveness Analysis in Practice: Interventions to Improve High School Completion. Educ. Eval. Policy Anal. 2014 , 36 , 307–326. [ Google Scholar ] [ CrossRef ]
• Buxton, M.J. Economic evaluation and decision making in the UK. Pharmacoeconomics 2006 , 24 , 1133–1142. [ Google Scholar ] [ CrossRef ]
• Corbacho, B.; Pinto-Prades, J.L. Health economic decision-making: A comparison between UK and Spain. Br. Med. Bull. 2012 , 103 , 5–20. [ Google Scholar ] [ CrossRef ]
• Black, P.; Wiliam, D. Assessment and classroom learning. Assess. Educ. Princ. Policy Pract. 1998 , 5 , 7–74. [ Google Scholar ] [ CrossRef ]
• Black, P.; Wiliam, D. Classroom assessment and pedagogy. Assess. Educ. Princ. Policy Pract. 2018 , 25 , 551–575. [ Google Scholar ] [ CrossRef ]
• Clarke, S. Outstanding Formative Assessment: Culture and Practice ; Hachette: London, UK, 2014. [ Google Scholar ]
• EEF. Teacher Feedback to Improve Pupil Learning. 2021. Available online: https://d2tic4wvo1iusb.cloudfront.net/production/eef-guidance-reports/feedback/Teacher_Feedback_to_Improve_Pupil_Learning.pdf?v=1713861365 (accessed on 10 January 2024).
• Bennett, R.E. Formative assessment: A critical review. Assess. Educ. Princ. Policy Pract. 2011 , 18 , 5–25. [ Google Scholar ] [ CrossRef ]
• Black, P.; Wiliam, D. Developing the theory of formative assessment. Educ. Assess., Eval. Account. (Former. J. Pers. Eval. Educ.) 2009 , 21 , 5–31. [ Google Scholar ] [ CrossRef ]
• OECD; CERI. Formative Assessment: Improving Learning in Secondary Classrooms ; CERI/OECD: Paris, France, 2005. [ Google Scholar ]
• Wiliam, D. Embedded Formative Assessment: Strategies for Classroom Assessment That Drives Student Engagement and Learning ; Solution Tree: Bloomington, IN, USA, 2017. [ Google Scholar ]
• James, M. Embedding formative assessment in classroom practice. In Life in Schools and Classrooms ; Springer: Singapore, 2017; pp. 509–525. [ Google Scholar ]
• Wiliam, D. Research into practice. In Getting Evidence into Education: Evaluating the Routes to Policy and Practice ; Routledge: London, UK, 2020. [ Google Scholar ]
• Donaldson, G. Successful Futures Independent Review of Curriculum and Assessment Arrangements in Wales Cardiff: Welsh Government. 2015. Available online: https://gov.wales/sites/default/files/publications/2018-03/successful-futures.pdf (accessed on 9 January 2024).
• Allal, L. Involving primary school students in the co-construction of formative assessment in support of writing. Assess. Educ. Princ. Policy Pract. 2021 , 28 , 584–601. [ Google Scholar ] [ CrossRef ]
• Anders, J.; Foliano, F.; Bursnall, M.; Dorsett, R.; Hudson, N.; Runge, J.; Speckesser, S. The effect of embedding formative assessment on pupil attainment. J. Res. Educ. Eff. 2022 , 15 , 748–779. [ Google Scholar ] [ CrossRef ]
• Cosi, A.; Voltas, N.; Lázaro-Cantabrana, J.L.; Morales, P.; Calvo, M.; Molina, S.; Quiroga, M.Á. Formative assessment at university through digital technology tools. Profr. Rev. Curric. Y Form. Profr. 2020 , 24 , 164–183. [ Google Scholar ] [ CrossRef ]
• Cisterna, D.; Gotwals, A.W. Enactment of ongoing formative assessment: Challenges and opportunities for professional development and practice. J. Sci. Teach. Educ. 2018 , 29 , 200–222. [ Google Scholar ] [ CrossRef ]
• Barana, A.; Marchisio Conte, M. Promoting socioeconomic equity through automatic formative assessment. J. Math. Educ. 2023 , 15 , 227–252. [ Google Scholar ] [ CrossRef ]
• Kingston, N.; Nash, B. Formative assessment: A meta-analysis and a call for research. Educ. Meas. Issues Pract. 2011 , 30 , 28–37. [ Google Scholar ] [ CrossRef ]
• James, M. Assessment for learning: Research and policy in the (dis) United Kingdom. In Assessment Reform in Education ; Springer: Dordrecht, The Netherlands, 2011; pp. 15–32. [ Google Scholar ]
• Leahy, S.; Wiliam, D. Embedding Formative Assessment: Practical Techniques for K-12 Classrooms ; Hawker Brownlow Education: Cheltenham, UK, 2015. [ Google Scholar ]
• Campbell, D.T.; Stanley, J.C. Experimental and Quasi-Experimental Designs for Research ; Ravenio Books: Charleston, SC, USA, 2015. [ Google Scholar ]
• Gray, E.; McCambridge, J.; Strang, J. The effectiveness of motivational interviewing delivered by youth workers in reducing drinking, cigarette and cannabis smoking among young people: Quasi-experimental pilot study. Alcohol Alcohol. 2005 , 40 , 535–539. [ Google Scholar ] [ CrossRef ]
• Hakim, C. Research Design: Successful Designs for Social Economics Research ; Routledge: London, UK, 2012. [ Google Scholar ]
• Handley, M.A.; Lyles, C.R.; McCulloch, C.; Cattamanchi, A. Selecting and improving quasi-experimental designs in effectiveness and implementation research. Annu. Rev. Public Health 2018 , 39 , 5–25. [ Google Scholar ] [ CrossRef ]
• Palinkas, L.A.; Horwitz, S.M.; Green, C.A.; Wisdom, J.P.; Duan, N.; Hoagwood, K. Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Adm. Policy Ment. Health Ment. Health Serv. Res. 2015 , 42 , 533–544. [ Google Scholar ] [ CrossRef ]
• Welsh Government. National Reading and Numeracy Personalised Assessments: Administration Handbook. 2019. Available online: https://hwb.gov.wales/curriculum-for-wales/reading-and-numeracy-assessments/personalised-assessments/national-reading-and-numeracy-personalised-assessments-administration-handbook#specific-requirements-for-numeracy-(procedural)-and-numeracy-(reasoning)-personalised-assessments (accessed on 8 February 2024).
• Dunphy, E. Assessing early learning through formative assessment: Key issues and considerations. Ir. Educ. Stud. 2010 , 29 , 41–56. [ Google Scholar ] [ CrossRef ]
• Stevens, K. Valuation of the child health utility 9D index. Pharmacoeconomics 2012 , 30 , 729–747. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Boyer, N.; Miller, S.; Connolly, P.; McIntosh, E. Population Health Economic Evaluation of the School-Based Roots of Empathy Program in Northern Ireland. In Proceedings of the 2018 Annual Research Meeting, Washington, DC, USA, 24–26 June 2018; AcademyHealth: Washington, DC, USA, 2018. [ Google Scholar ]
• White, J.; Connelly, G.; Thompson, L.; Wilson, P. Assessing children’s social and emotional wellbeing at school entry using the strengths and difficulties questionnaire: Professional perspectives. Educ. Res. 2013 , 55 , 87–98. [ Google Scholar ] [ CrossRef ]
• Weintraub, N.; Bar-Haim Erez, A. Quality of life in school (QoLS) questionnaire: Development and validity. Am. J. Occup. Ther. 2009 , 63 , 724–731. [ Google Scholar ] [ CrossRef ]
• Ghotra, S.; McIsaac, J.L.D.; Kirk, S.F.; Kuhle, S. Validation of the “Quality of Life in School” instrument in Canadian elementary school students. PeerJ 2016 , 4 , e1567. [ Google Scholar ] [ CrossRef ]
• Gorard, S.A.C. Rethinking ‘quantitative’methods and the development of new researchers. Rev. Educ. 2015 , 3 , 72–96. [ Google Scholar ] [ CrossRef ]
• Gorard, S.; Gorard, J. What to do instead of significance testing? Calculating the ‘number of counterfactual cases needed to disturb a finding’. Int. J. Soc. Res. Methodol. 2016 , 19 , 481–490. [ Google Scholar ] [ CrossRef ]
• Bell, A. Designing and testing questionnaires for children. J. Res. Nurs. 2007 , 12 , 461–469. [ Google Scholar ] [ CrossRef ]
• Welsh Government. Curriculum for Wales ; Welsh Government: Cardiff, UK, 2024. Available online: https://hwb.gov.wales/curriculum-for-wales/ (accessed on 12 February 2024).
• Morris, S.; Devlin, N.; Parkin, D.; Spencer, A. Principles of economic evaluation in health care. In Economic Analysis in Health Care , 2nd ed.; John Wiley & Sons: Chichester, UK, 2012; pp. 232–250. [ Google Scholar ]
• Charles, J.; Edwards, R.T. A Guide to Health Economics for Those Working in Public Health: A Concise Desktop Handbook ; Centre for Health Economics and Medicines Evaluation: Bangor, UK, 2016. [ Google Scholar ]
• Charles, J.M.; Harrington, D.M.; Davies, M.J.; Edwardson, C.L.; Gorely, T.; Bodicoat, D.H.; Khunti, K.; Sherar, L.B.; Yates, T.; Edwards, R.T. Micro-costing and a cost-consequence analysis of the ‘Girls Active’programme: A cluster randomised controlled trial. PLoS ONE 2019 , 14 , e0221276. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Haghparast-Bidgoli, H.; Skordis, J.; Harris-Fry, H.; Krishnan, S.; O’Hearn, M.; Kumar, A.; Pradhan, R.; Mishra, N.K.; Upadhyay, A.; Pradhan, S.; et al. Protocol for the cost-consequence and equity impact analyses of a cluster randomised controlled trial comparing three variants of a nutrition-sensitive agricultural extension intervention to improve maternal and child dietary diversity and nutritional status in rural Odisha, India (UPAVAN trial). Trials 2019 , 20 , 287. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hartfiel, N.; Edwards, R.T. Cost–consequence analysis of public health interventions. In Applied Health Economics for Public Health Practice and Research ; Oxford University Press: Oxford, UK, 2019; pp. 233–247. [ Google Scholar ] [ CrossRef ]
• Coast, J. Is economic evaluation in touch with society’s health values? BMJ 2004 , 329 , 1233–1236. [ Google Scholar ] [ CrossRef ]
• Rogers, J.C.; Simmons, E.A.; Convery, I.; Weatherall, A. Public perceptions of opportunities for community-based renewable energy projects. Energy Policy 2008 , 36 , 4217–4226. [ Google Scholar ] [ CrossRef ]
• Glouberman, S.; Zimmerman, B. Complicated and Complex Systems: What Would Successful Reform of Medicare Look Like? Commission on the Future of Health Care in Canada: Ottawa, ON, Canada, 2002. [ Google Scholar ]
• Chapko, M.K.; Liu, C.F.; Perkins, M.; Li, Y.F.; Fortney, J.C.; Maciejewski, M.L. Equivalence of two healthcare costing methods: Bottom-up and top-down. Health Econ. 2009 , 18 , 1188–1201. [ Google Scholar ] [ CrossRef ]
• Scammacca, N.; Swanson, E.; Vaughn, S.; Roberts, G. Cost-Effectiveness of a Grade 8 Intensive Reading and Content Learning Intervention. Sch. Psychol. Rev. 2020 , 49 , 374–385. [ Google Scholar ] [ CrossRef ]
• Harden, J. The True Cost of £ducation. 2019. Available online: https://www.ascl.org.uk/ASCL/media/ASCL/Our%20view/Campaigns/The-True-Cost-of-Education.pdf (accessed on 10 January 2024).
• NASUWT. Directed Time (Wales). 2024. Available online: https://www.nasuwt.org.uk/advice/conditions-of-service/teachers-working-hours/directed-time-wales.html#TeachersWorkingTime (accessed on 25 January 2024).
• Limwattananon, S. Sensitivity analysis for handling uncertainty in an economic evaluation. J. Med. Assoc. Thail. Chotmaihet Thangphaet 2014 , 97 , S59–S64. [ Google Scholar ]
• Vaismoradi, M.; Jones, J.; Turunen, H.; Snelgrove, S. Theme development in qualitative content analysis and thematic analysis. J. Nurs. Educ. Pract. 2016 , 6 , 100–110. [ Google Scholar ] [ CrossRef ]
• Braun, V.; Clarke, V. Thematic Analysis ; American Psychological Association: Washington, DC, USA, 2012. [ Google Scholar ]
• Welsh Government. Draft Curriculum for Wales 2022: A Guide to Curriculum for Wales 2022 ; Welsh Government: Cardiff, UK, 2019. Available online: https://cyfarthfahigh.merthyr.sch.uk/wp-content/uploads/2019/05/A-Guide-to-the-Curriculum-for-Wales-2022.pdf (accessed on 12 February 2024).
• Levin, H.; McEwan, P.; Belfield, C.; Bowden, A. Economic Evaluation in Education: Cost-Effectiveness and Benefit-Cost Analysis ; SAGE Publications: London, UK, 2017. [ Google Scholar ]
• Welsh Government. School Census Results ; Welsh Government: Cardiff, UK, 2018. Available online: https://www.gov.wales/sites/default/files/statistics-and-research/2018-12/180725-school-census-results-2018-en.pdf (accessed on 12 August 2024).
• Boström, E.; Palm, T. The effect of a formative assessment practice on student achievement in mathematics. Front. Educ. 2023 , 8 , 1101192. [ Google Scholar ] [ CrossRef ]
• Martin, C.; Mraz, M.; Polly, D. Examining elementary school teachers’ perceptions of and use of formative assessment in mathematics. Int. Electron. J. Elem. Educ. 2022 , 14 , 417–425. [ Google Scholar ] [ CrossRef ]
• Beames, J.R.; Spanos, S.; Roberts, A.; McGillivray, L.; Li, S.; Newby, J.M.; O’Dea, B.; Werner-Seidler, A. Intervention programs targeting the mental health, professional burnout, and/or wellbeing of school teachers: Systematic review and meta-analyses. Educ. Psychol. Rev. 2023 , 35 , 26. [ Google Scholar ] [ CrossRef ]
• Gorard, S.; Siddiqui, N.; See, B.H. What works and what fails? Evidence from seven popular literacy ‘catch-up’schemes for the transition to secondary school in England. Res. Pap. Educ. 2017 , 32 , 626–648. [ Google Scholar ] [ CrossRef ]
• Speckesser, S.; Runge, J.; Foliano, F.; Bursnall, M.; Hudson-Sharp, N.; Rolfe, H.; Anders, J. Embedding Formative Assessment: Evaluation Report and Executive Summary ; Education Endowment Foundation: London, UK, 2018. [ Google Scholar ]
• Newell, R.G.; Pizer, W.A.; Prest, B.C. The Shadow Price of Capital: Accounting for Capital Displacement in Benefit-Cost Analysis. Environ. Energy Policy Econ. 2024 , 5 , 49–69. [ Google Scholar ] [ CrossRef ]
• Curtis, L.A. Unit Costs of Health and Social Care 2013 ; Personal Social Services Research Unit: Kent, UK, 2013. [ Google Scholar ]
• Shand, R.; Bowden, A.B. Empirical support for establishing common assumptions in cost research in education. J. Res. Educ. Eff. 2022 , 15 , 103–129. [ Google Scholar ] [ CrossRef ]
• MacKillop, E.; Sheard, S. Quantifying life: Understanding the history of quality-adjusted life-years (QALYs). Soc. Sci. Med. 2018 , 211 , 359–366. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hartz, S.; John, J. Contribution of economic evaluation to decision making in early phases of product development: A methodological and empirical review. Int. J. Technol. Assess. Health Care 2008 , 24 , 465–472. [ Google Scholar ] [ CrossRef ]
• Ramsey, S.D.; Willke, R.J.; Glick, H.; Reed, S.D.; Augustovski, F.; Jonsson, B.; Briggs, A.; Sullivan, S.D. Cost-effectiveness analysis alongside clinical trials II—An ISPOR Good Research Practices Task Force report. Value Health 2015 , 18 , 161–172. [ Google Scholar ] [ CrossRef ]
• Levin, H.M.; Belfield, C. Guiding the development and use of cost-effectiveness analysis in education. J. Res. Educ. Eff. 2015 , 8 , 400–418. [ Google Scholar ] [ CrossRef ]
• McMillan, J.H.; Venable, J.C.; Varier, D. Studies of the effect of formative assessment on student achievement: So much more is needed. Pract. Assess. Res. Eval. 2013 , 18 , 1–15. [ Google Scholar ] [ CrossRef ]
• Gorard, S. A proposal for judging the trustworthiness of research findings. Radic. Stat. 2014 , 110 , 47–60. [ Google Scholar ]
• Gorard, S. Judging the relative trustworthiness of research results: How to do it and why it matters. Rev. Educ. 2024 , 12 , e3448. [ Google Scholar ] [ CrossRef ]
• Greenberg, M.T.; Abenavoli, R. Universal interventions: Fully exploring their impacts and potential to produce population-level impacts. J. Res. Educ. Eff. 2017 , 10 , 40–67. [ Google Scholar ] [ CrossRef ]
• Charles, J.M.; Edwards, R.T.; Bywater, T.; Hutchings, J. Micro-costing in public health economics: Steps towards a standardized framework, using the incredible years toddler parenting program as a worked example. Prev. Sci. 2013 , 14 , 377–389. [ Google Scholar ] [ CrossRef ]
• Dolan, P.; Peasgood, T. Estimating the economic and social costs of the fear of crime. Br. J. Criminol. 2007 , 47 , 121–132. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Tiesteel, E.; Watkins, R.C.; Stringer, C.; Grigorie, A.; Sultana, F.; Hughes, J.C. Where Are the Costs? Using an Economic Analysis of Educational Interventions Approach to Improve the Evaluation of a Regional School Improvement Programme. Educ. Sci. 2024 , 14 , 957. https://doi.org/10.3390/educsci14090957

Tiesteel E, Watkins RC, Stringer C, Grigorie A, Sultana F, Hughes JC. Where Are the Costs? Using an Economic Analysis of Educational Interventions Approach to Improve the Evaluation of a Regional School Improvement Programme. Education Sciences . 2024; 14(9):957. https://doi.org/10.3390/educsci14090957

Tiesteel, Emma, Richard C. Watkins, Carys Stringer, Adina Grigorie, Fatema Sultana, and J. Carl Hughes. 2024. "Where Are the Costs? Using an Economic Analysis of Educational Interventions Approach to Improve the Evaluation of a Regional School Improvement Programme" Education Sciences 14, no. 9: 957. https://doi.org/10.3390/educsci14090957

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals