thesis about learning styles in the philippines

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Learning styles, study habits and academic performance of Filipino University students in applied science courses: Implications for instruction

In order to better prepare university students as proficient, versatile and productive information and industrial technologists in the 21 st century, the need to implement instructional strategies and activities naturally align with their predispositions will make them better learners. This study examined the learning style preferences, study habits and level of academic achievement of students enrolled in applied science courses of Cagayan State University at Lasam, Philippines.  The study employed descriptive correlational research design to a total of seventy-five respondents who were purposively sampled. Two sets of standardized instruments were utilized by the researcher. Results of the study revealed that the students of applied sciences courses preferred visual, group and kinesthetic as major learning styles while they manifest a moderate level of study habits. They also have a good level of academic achievement. Test of difference revealed that academic performance, father's occupation and type of high school graduated from spelled significant differences in their perceptual learning styles. They also spelled differences in their study habits when grouped according to academic standing in high school, writing skills, mothers’ education, and test anxiety. Finally, there were significant relationships between learning styles, study habits and academic performance of students in applied science courses. The implications of the study can guide instructors plan and deliver suitable instructional interventions.

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TEACHING STYLES OF FILIPINO SUBJECT TEACHERS OF THE HIGH SCHOOL DEPARTMENT AND ITS RELATIONSHIP TO STUDENTS’ ACADEMIC PERFORMANCE OF S.Y. 2018-2019

2018, TPS-ABUDHABI

The purpose of this causal-comparative research was to ascertain if a statistically significant impact existed in the academic performance of high school students in classrooms led by teachers with varying teaching styles. Furthermore, this study sought to discover which makes the most effective teaching strategy in teaching the Filipino subject. The study led by the researchers took place during the school year 2018-2019 in The Philippine School - Abu Dhabi and involved ten respondents per grade level in the Junior High School department. The researchers utilized a questionnaire checklist which included the socio-demographic characteristics and questions that pertain to the different teaching styles of Filipino high school subject teachers which were the facilitator method, authoritarian method, hybrid method, delegator method, and demonstrator method. The result of this study proved that the age and academic performance of the respondents have a significant relationship between the teaching styles applied by Filipino subject teachers in their respective classes. With the information the researchers have collated, they concluded that the variety of teaching styles and methods have a pivotal effect on the student's performance in class. Moreover, 53% of the teaching styles employed by the Filipino subject teachers are highly impactful on the students' teaching-learning experience. Such instructional approaches and methods are significantly related to the favorable outcomes attained but not so much with the other instructional strategies and as well as the respondent's grade level. Furthermore, teaching styles and approaches proven to be effective were identified; these are the hybrid and facilitator method.

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This study was conducted to identify the teaching styles of the English teachers in top performing public high schools in Quezon City. It identified the common teaching style of the English teachers and the common teaching style that the different public high school in terms of Grasha’s five teaching styles. It also sought to determine the relationships of the teachers’ profile with their teaching styles. This study utilizes descriptive method. Frequency distribution, percentage and mean were the statistical tools used. Pearson Moment Coefficient of Correlation and ANOVA - one way were used to identify the relationships of age, sex, and number of years in service. The findings of the study revealed that Facilitator teaching style was the common teaching style that the English teachers of top performing public school in Quezon City have. It also found that there was a relationship between the age and number of years in service in the teaching style of a teacher, yet sex has found to be of no significant difference. The number of years in service has a small positive relationship with their teaching style; the age has a small positive relationship with their teaching style; while sex has no significant difference to their teaching style.

Adnan Nasir , Dr. Abid G. Chaudhry

Teaching style is defined as “planed headed for move up effective awareness during teaching, diversity of lecturer activity and learning atmosphere of the requirement of the students” The present study has been conducted at the PMAS-Arid Agriculture University, Rawalpindi as at random sampling used for both male and female students or respondents. The purpose of this study was to explore the Teacher’s strategies to cope with student behavior to enhance academic performance of university students. This research helps to find the relationship of student behavior and their academic performance on teacher behavior .Teacher behavior directly effect on students behavior and their academic performance.

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Teaching style involves students in the learning process and assists them to improve critical thinking abilities. Customary teaching styles have progressed with the arrival of distinguished teaching, stimulating teachers to modify their teaching styles towards pupils' learning requirements. The present study was intended to compare teaching styles of teachers working in various school groups at secondary level in district Chiniot. The main objective which was probed by the respondents to discover teaching styles of public and private secondary school groups. The sample of 240 teachers of 24 public and private secondary schools was selected randomly. A questionnaire developed and used by Grasha [1] in many countries, was used as research instrument to collect data. The responses were analyzed through SPSS (V-20) to find out Mean, Standard Deviation, and Frequency Distributions for the description of data. Values of t-test were also noted to measure significant difference among various variables related to research questions. It was concluded by analysis that there were five teaching styles found in public and private school groups. It was revealed that in public and private school groups there was no 2 significant difference observed on expert, facilitator and delegator teaching styles. Only significant difference was found on formal authority and personal model teaching styles in favour of public school groups.

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The objective of this study was to determine the main learning strategies frequently used by the students and frequent teaching styles as perceived by students. The sample was comprised of 560 (280 boys, 280 girls) students randomly selected from 20 Public High Schools (male = 10, female =10) of Lahore, Punjab, Pakistan. The Motivated Strategies for Learning Questionnaire (MSLQ) by Wu and Cherng and Teaching Style Questionnaire by Chen was utilized for this study. It was found that help seeking learning strategy was most frequently used by students and democratic teaching style was most frequent as observed by student's perception. A significant difference was found in the utilization of elaboration, rehearsal, help-seeking and organization strategies of learning on the basis of gender (p < .05). Whereas no significant difference was found in critical thinking, meta-cognitive, peer learning, time and study management and effort management learning strategies.

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Philippine E-Journals

Home ⇛ jpair multidisciplinary research journal ⇛ vol. 24 no. 1 (2016), kinesthetic learning style and structured approach to learning as most preferred by nursing students.

Ashley Ali-bangcola

The learning process has been the focus of numerous studies, but remains complex and affected by many factors. Since hardly any attention has been paid to how students learn and how teachers teach in many institutions, this quantitative descriptive-correlation study was conducted to determine the learning styles and attitude towards learning of nursing students. A sample of 304 nursing students was selected from all the nursing schools in Marawi City, Philippines using stratified proportionate random sampling technique with replacement. Results revealed that the four learning styles (Visual, Auditory, Tactile, and Kinesthetic) were found to have been used by the students simultaneously as major learning styles and most of them expressing a preference for kinesthetic learning style (78%). On the other hand, they expressed a minor and negligible preference for group Learning. Te results of the questionnaire on the attitude towards learning revealed a high level of academic comfort and a preference towards structured, spontaneous, and person-centered approaches to learning. Te study concludes that out of the six learning styles tested; only group learning style is found to have no significant relationship with the participants’ attitude towards learning. Based on the findings, the researcher recommends that faculty members should take into consideration the differences among the students when designing the course material.

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Thitu Island Dispatch

The Filipinos Living in the Shadow of China’s Military Might

More than 200 civilian settlers on a contested island in the South China Sea find themselves on the frontier of a possible conflict with China.

The runway on Thitu, which is occupied by the Philippines, with Chinese ships on the horizon, in June. Credit...

Supported by

By Camille Elemia

Photographs by Jes Aznar

Camille Elemia and Jes Aznar spent five days on Thitu Island in the South China Sea.

• Aug. 12, 2024

For travelers flying into the tiny island of Thitu, the reality of China’s territorial ambition becomes instantly clear. There they are: dozens of Chinese ships surrounding a speck of land that a few hundred Filipinos call home.

For now though, life is mostly peaceful and slow on the island. Small wooden fishing boats line a white sand beach on the eastern shore. Rough houses pieced together from plywood, scrap lumber and tarps are the main form of shelter. On a recent evening, a few people gathered near the beach to debone fish, while others waded into tide pools with fishing spears.

But the calm belies the fact that Thitu is contested land. Nearby, China has stationed a flotilla of coast guard ships and maritime militia vessels. On a neighboring reef, it has constructed a military base whose lights shimmer at night like a city. The intensifying Chinese presence has startled the Philippines, which has occupied Thitu for nearly half a century. So it is upgrading its crumbling military facilities that lie on the island’s southern end.

And it is encouraging more Filipinos to move in, betting more residents will strengthen its claim to Thitu, which it calls Pag-asa, or hope, and reduce hostilities with China.

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Landslide assessment classification using deep neural networks based on climate and geospatial data.

1. Introduction

2. materials and methods, 2.1. global landslide catalog, 2.2. obtaining climate data, 2.3. classification methodology.

• Number of layers: L = { l ∣ l ∈ Z , 1 ≤ l ≤ 20 }
• Neurons per layer: N = { n ∣ n ∈ Z , 1 ≤ n ≤ 128 }
• Activation functions: A = { relu , sigmoid , tan h , softmax , softplus , softsign , elu , selu , gelu , hard sigmoid , linear }
• Optimizers: O = { adam , sgd , rmsprop , adagrad , adadelta , adamax , nadam }
• Learning rates: R = { 0.0001 , 0.001 , 0.01 , 0.1 }
• l is the number of layers chosen from L ,
• n is the number of neurons per layer chosen from N ,
• a is the activation function chosen from A ,
• o is the optimizer chosen from O ,
• r is the learning rate chosen from R ,
• f is the loss function chosen from F .
• Initialize a population P ( t ) of N candidate solutions (DNN architectures) at generation t = 0 : P ( 0 ) = { A 1 , A 2 , … , A N } where A i represents the architecture of the i -th individual in the population.
• Evaluate the fitness of each individual A i in the population using the classification accuracy A i on a validation set: f ( A i ) = Accuracy ( A i )
• Select a subset of individuals based on their fitness scores f ( A i ) to act as parents for the next generation. This can be carried out using methods such as roulette wheel selection or tournament selection: P parents ( t ) = Select ( P ( t ) , f ( A i ) )
• Apply crossover operations to pairs of parent architectures to produce offspring architectures. For instance, if A p and A q are two parents, the offspring A c 1 and A c 2 can be generated as A c 1 , A c 2 = Crossover ( A p , A q )
• Apply mutation operations to the offspring architectures to introduce genetic diversity. For an architecture A c , a mutation might alter one or more hyperparameters: A m = Mutation ( A c )
• Evaluate the fitness of the offspring architectures: f ( A m ) = Accuracy ( A m )
• Replace the least fit individuals in the population with the new offspring, potentially incorporating elitism to retain the best solutions: P ( t + 1 ) = Replace ( P ( t ) , A m )
• Repeat the evaluation, selection, crossover, mutation, and replacement steps for a predefined number of generations G or until convergence criteria are met: For t = 1 to G do : P ( t ) ← Update Population End For

3.1. DNN Results

3.2. case study: application of the approach on viti levu island, fiji.

• Defining grid parameters: The first step involves setting the parameters for the grid of the study area. This includes specifying the minimum and maximum co-ordinates that define the region of interest. For Viti Levu Island, the boundaries selected were a minimum latitude of −18.5, a maximum latitude of −17.3, a minimum longitude of 177.0, and a maximum longitude of 179.5.
• Grid division: By using these parameters, the area is divided into a grid with evenly sized sectors. The number of sectors is determined based on the desired division in terms of latitude and longitude, resulting in a grid with specific sector sizes.
• Filtering out water surfaces: In order to refine the analysis, sectors with a large proportion of water surfaces (such as oceans) are excluded. These sectors are marked as non-informative for further analysis.
• Integration of climate data: For the remaining sectors, climate data for the 10 years preceding each landslide event are integrated. This includes variables such as rainfall, humidity, pressure, and temperature. These climate data are then reduced using principal component analysis (PCA), which helps to highlight the principal climate components relevant to landslide triggers.
• Classification and risk assessment: By using geospatial co-ordinates and reduced climate data, the probability of landslide occurrence is assessed. Optimized DNN models, with parameters tuned through GA, are employed to classify the likelihood of landslides. The optimized models achieved accuracies of 0.67 and 0.82 for classifying landslide triggers and sizes, respectively.

4. Discussion

5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, appendix a. geographical description of landslides worldwide.

Click here to enlarge figure

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Tynchenko, Y.; Kukartsev, V.; Tynchenko, V.; Kukartseva, O.; Panfilova, T.; Gladkov, A.; Nguyen, V.; Malashin, I. Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data. Sustainability 2024 , 16 , 7063. https://doi.org/10.3390/su16167063

Tynchenko Y, Kukartsev V, Tynchenko V, Kukartseva O, Panfilova T, Gladkov A, Nguyen V, Malashin I. Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data. Sustainability . 2024; 16(16):7063. https://doi.org/10.3390/su16167063

Tynchenko, Yadviga, Vladislav Kukartsev, Vadim Tynchenko, Oksana Kukartseva, Tatyana Panfilova, Alexey Gladkov, Van Nguyen, and Ivan Malashin. 2024. "Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data" Sustainability 16, no. 16: 7063. https://doi.org/10.3390/su16167063

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