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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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Journal of Technology and Science Education, 2011-2024
Online ISSN: 2013-6374; Print ISSN: 2014-5349; DL: B-2000-2012
Publisher: OmniaScience
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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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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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Reference | Focus | Applied Method | Results | Limitations |
---|---|---|---|---|
Merghadi et al. [ ] | Overview of ML techniques in landslide susceptibility mapping | SVM, decision trees, logistic regression | Tree-based ensemble algorithms, random forest standout; robust performance in Algeria | Limited data on landslide types and sizes; potential overfitting and data quality issues |
Tehrani et al. [ ] | ML applications in landslide detection; spatial and temporal forecasting | Various ML techniques | Significant advancements in complex landslide modeling; challenges remain in temporal forecasts | Lack of standardized model evaluation metrics; reliance on empirical data for temporal forecasts |
Wang et al. [ ] | Landslide identification using ML and DL | CNN, SVM, random forest | CNN achieves 92.5% accuracy in landslide identification in Hong Kong; RF and SVM also effective | Data preprocessing challenges; limited improvement with additional data from DEM |
Korup et al. [ ] | ML for landslide prediction based on historical data | ML, Data mining techniques | Success rates of 75–95% in predicting landslides; challenges with data quality and model selection | Difficulty in predicting specific landslide types and sizes |
Marjanovic et al. [ ] | SVM in landslide susceptibility mapping | SVM, decision trees, logistic regression | SVM outperforms AHP in mapping landslide susceptibility in Fruška Gora Mountain (Serbia) | Challenges in integrating complex geological and morphological data into models |
Goetz et al. [ ] | Comparison of ML and statistical models for regional susceptibility mapping | Logistic regression, GAM, SVM, random forest | Random forest and bundling show superior predictive performance; challenges with spatial artifacts | Variability in model performance across different geological settings |
Kavzoglu et al. [ ] | ML versus traditional statistical methods in landslide susceptibility mapping | Various ML algorithms | ML techniques show promise in areas with limited geotechnical data; challenges in model selection | Limited studies on the scalability of ML models for large-scale landslide mapping |
Ghorbanzadeh [ ] | ML methods for landslide detection using remote sensing data | ANN, SVM, RF, CNN | CNN achieves 78.26% mIOU in landslide detection; variability in CNN effectiveness based on design | Need for better understanding of CNN parameter effects; limitations in training data and augmentation |
Chen et al. [ ] | KLR models for landslide susceptibility mapping | Kernel logistic regression (PLKLR, PUKLR, and RBFKLR) | PUKLR model outperforms others in Zichang City, China; valuable insights for hazard prevention | Dependence on historical data for model construction; challenges in integrating diverse datasets |
Micheletti et al. [ ] | Adaptive ML techniques for landslide susceptibility mapping | SVM, random forest, AdaBoost | Random forest and AdaBoost effective in feature selection; challenges in deep-seated landslide characterization | Efficiency of adaptive SVM in landslide mapping; challenges with adaptive scaling in deep-seated landslides |
# | Num Layers | Neurons per Layer | Activation Functions | Optimizer | Alpha | Accuracy |
---|---|---|---|---|---|---|
1 | 2 | [71, 126] | [‘tanh’, ‘tanh’] | adagrad | 0.100 | 0.677789 |
2 | 2 | [64, 28] | [‘gelu’, ‘softsign’] | adam | 0.001 | 0.680290 |
3 | 9 | [72, 94, 60, 64, 55, 106, 37, 74, 9] | [‘linear’, ‘gelu’, ‘linear’, ‘softsign’, ‘softsign’, ‘sigmoid’, ‘gelu’, ‘softsign’, ‘hard_sigmoid’] | nadam | 0.0001 | 0.813857 |
4 | 4 | [59, 114, 84, 76] | [‘gelu’, ‘softmax’, ‘softplus’, ‘relu’] | rmsprop | 0.0100 | 0.818859 |
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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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