Kantathanawat, Thiyaporn
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Kantathanawat, Thiyaporn
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thiyaporn.ka@kmitl.ac.th
27 results
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Item type:Publication, Integrating Mastery Adaptive and Problem-Solving (MAPS) Digital Technology Skills into a Thai Community College Student Learning Model(2025-01-01); ; Background/purpose. The increasing integration of digital technology in education underscores the need for instructional models that support personalized, skill-based, and problem-solving-focused learning. While traditional pedagogies often fail to address these needs comprehensively, this study proposes that the Mastery Adaptive Problem-Solving (MAPS) Model offers an innovative solution for enhancing digital technology skills (DTS) and academic achievement (AA) among Thai community college students. Materials/methods. The MAPS Model incorporates mastery learning, micro-learning, adaptive learning, and problem-based learning (PBL) into a six-step framework. Expert validation of the model was conducted using the Connoisseurship method via focus group discussions. A quasi-experimental design was employed to evaluate the model's implementation with 19 second-year Thai community college students. Student DTS and AA were measured using validated tools with reliability indices exceeding 0.80. Retention of skills and knowledge was assessed 14 days post-instruction. Results. Experts rated the MAPS Model highly for utility, feasibility, appropriateness, and accuracy. After implementation, students' DTS significantly exceeded the set threshold (70%), with statistical significance at the .01 level. Additionally, students demonstrated significantly higher AA post-instruction than pre-instruction levels, at the .01 significance level. Retention analysis revealed no significant decline in DTS or AA 14 days after program completion. Conclusion. The MAPS Model effectively integrates innovative pedagogical strategies to enhance DTS and AA in community college settings. Its structured, adaptable framework addresses diverse learner needs, ensuring immediate and sustained educational benefits. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, High dynamic range preprocessing, ParallelAttention Transformer and CoExpression analysis for facial expression recognition(2025-04-01); ; Liu, ChunmaoFacial expression recognition (FER) aims to enable computers to automatically detect and recognize human facial expressions, thereby understanding their emotional states. Despite significant technological advancements in recent years, FER tasks still face several challenges, including expression diversity, individual differences, and the impact of lighting and detail variations on recognition accuracy. To address these challenges, a high-performance FER model is proposed that comprises three key components: High Dynamic Range (HDR) Preprocessing Module, ParallelAttention VisionTransformer structure, and CoExpression Head. In the preprocessing stage, the HDR Preprocessing Module optimizes input images through local contrast and detail enhancement techniques, improving the model's adaptability to lighting and detail variations. During the feature processing stage, the ParallelAttention VisionTransformer structure employs a multi-head self-attention mechanism encoder to effectively capture and process facial expression features at various scales, allowing for a detailed understanding of subtle facial expression differences. Finally, the CoExpression Head utilizes a collaborative expression mechanism to efficiently handle and refine features across different expression states during the feature integration process. Combining these three stages significantly enhances the accuracy of facial expression recognition. Extensive experimental evaluations on public datasets, RAF-DB and AffectNet, demonstrate that the model achieves accuracy rates of 92.11%, 67.25%, and 63.40% on RAF-DB, AffectNet, and AffectNet-8, respectively, exhibiting outstanding performance comparable to other state-of-the-art models. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Integrated Design-Based Learning Management Model to Promote Thai Undergraduate Computational Thinking Skills and Programming Proficiency(2022-01-01); ; Sukkamart, ThanateThe study applied concepts of computational thinking, waterfall programming, and design-based learning management (DBLM) combined with a participatory action research (PAR) methodology to obtain an integrated learning management model (LMM) designed as a base to promote computational thinking skills (CTS) and programming proficiency. The study used three groups consisting of 21 volunteer computer education undergraduate students, three teachers, and three computer studies graduates from the Computer Studies Department of the Faculty of Education at Dhonburi Rajabhat University in Thailand. To verify the model, nine educational experts conducted an assessment of the proposed model, and determined that the evaluation standards were overall at a high level (x= 4.23, SD = 0.44), with the experts feeling that the integration of the DBLM computational thinking framework, the Waterfall programming conceptual framework, and the PAR meeting the study's stated objectives at the highest level (x= 4.56, SD = 0.53). The proposed model is best undertaken in five week sessions of five hours each using five exercises per week. Activity effectiveness is best measured by taking notes, worksheets, and practicing the learned skills. In conducting this research, there may be obstacles from learners who may withdraw during research due to personal activities or becoming discouraged in learning activities based on a five-week schedule, preventing them from participating every time. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Flipping the Undergraduate Classroom to Develop Student Analytical Thinking Skills(2022-08-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The development of a learning management model according to the concept of self-reliance with inquiry-based learning to enhance system analysis and design skills(2020-01-01); Sukkamat, AukkapongThis study has the objective to develop a learning management model according to the concept of self-reliance with inquiry-based learning to enhance skills in system analysis and design for students. The author uses documentation research as the main process to study related books, research, and articles both domestically and internationally for 58 topics. The documents are analysed by Systematic Review. From the results of this study, the learning management model to enhance system analysis and design skills for students by self-reliance consists of 3 steps as follows: Step 1 Delivery of Knowledge Phase; Step 2 Creating Knowledge Phase which integrates 5 steps of inquiry-based learning consisting of 1) Knowledge Management Stage, 2) Quest Design Stage, 3) Action Investigations Stage, 4) Discussion and Conclusions Stage, and 5) Discovery delivery Stage, and Step 3 Applied Knowledge Phase. The result of the learning management supports students to have the two higher skills of system analysis and design skills which are 1) problem analysis skills 2) Summarising Skills by self-reliance.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancing Psychological Well-Being Assessment Through Data Mining: A Case Study from Thailand(2025-04-01); ; ; This study examines the psychological well-being (PWB) of lower secondary school students in Bangkok’s Secondary Educational Service Area Offices (SESAO) 1 and 2, using data mining techniques to analyze key influencing factors and develop a culturally adapted PWB questionnaire. The research framework is based on six components: autonomy, environmental mastery, personal growth, positive relationships, life purpose, and self-acceptance. Data were collected from 2543 students in the 2023 academic year and analyzed using the Waikato Environment for Knowledge Analysis (WEKA) program and the JRip rule-based classification model. Results indicate that personal growth is the most predictive in the classification performance of PWB, followed by positive relationships and life purpose. A newly developed PWB questionnaire was tested for reliability, with the Supplied Test Set (80:20) method yielding strong performance metrics, including accuracy (90.18%), precision (69.00%), recall (90.90%), and F-measure (78.40%). This study demonstrates data mining’s effectiveness in identifying factors influencing adolescent PWB within the Thai context. The findings provide educators and policymakers with insights for fostering student well-being and contribute to research by offering a validated, culturally relevant assessment tool.3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Computer Education Student Teacher Complex Problem-Solving Skills Development using Computational Thinking and Visualization Tools(2024-01-01); ; 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancing digital literacy in Thai higher education: A strategic imperative(2025-01-01); This study aims to enhance digital literacy in Thai higher education through the development of an integrated framework—3CLPC (Cloud-based, Collaborative, and Positive Coaching). A mixed-methods approach was employed, combining surveys and interviews with students and faculty from three public universities. Quantitative data were analyzed using descriptive statistics, while qualitative input informed iterative model refinement. The results indicated improvements in students’ digital competencies, especially in collaboration, content creation, and problem-solving. Participants also reported increased confidence and engagement when using cloud-based tools and receiving supportive coaching. The 3CLPC framework effectively addresses the divide between access to technology and pedagogical effectiveness, particularly in under-resourced institutions. The model is scalable and adaptable to other Southeast Asian contexts. Practical implications include integration into teacher training programs and digital curriculum policies. The study contributes to bridging the gap between policy and practice in digital education, emphasizing equity and learner-centered design.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Neural Backward Chaining Logic Algorithm Based on Dynamic Knowledge Region Segmentation in Knowledge Graph Completion(2025-01-01); With the rapid development of the Internet, the information age has arrived in a comprehensive way. The explosive growth of information data has greatly increased people's demand for knowledge management. Knowledge graph, as an effective structured knowledge representation tool, greatly enhances the organization and retrieval capabilities of information. However, in practical applications, knowledge graphs often face problems of knowledge loss and incompleteness, which severely limit their widespread application. To address this issue, this study proposes a neural backward chain inference method based on dynamic knowledge region generation. This method overcomes the bottleneck of performance degradation of traditional static methods on large datasets by introducing a dynamic knowledge region generation mechanism, which significantly improves the completion effect of knowledge graphs. The experiment was conducted on the Unified Medical Language System dataset and the Nations dataset. The results showed that when the size of the Unified Medical Language System dataset reached 2500, the accuracy of the proposed method reached 0.85. It was 6.25%, 20%, and 51.8% higher than the 0.80 of the neural backward chain inference method generated by static knowledge regions, 0.70 of the conditional theorem prover algorithms, and 0.56 of the traditional neural theorems proving algorithm, respectively. In the Nations dataset, the accuracy of the proposed method was 0.80 at the same data scale, which was significantly better than other methods. In addition, the method based on dynamic knowledge region generation reduced the iteration time to 1.4 seconds, which was about 52% and 63% higher than the static method's 2.9 s and the traditional method's 3.8 s, respectively. The research results indicate that the proposed neural backward chain logic algorithm based on dynamic knowledge region generation exhibits good performance in completing knowledge graphs.3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancing Personalized Learning in Online Education: The Impact of Adaptive Learning Systems and Recommendation Technologies(2024-01-01); The study investigates the impact of integrated adaptive learning systems and recommender technologies on the improvement of online education. A component-level quantitative evaluation was conducted, which involved measuring user interaction, content applicability, knowledge acquisition, and system usability, with support from surveys and interviews. The findings indicate that recommendation systems enhance active user participation, content relevance, and learning outcomes, while maintaining high usability rates that positively influence learners’ perceptions. However, certain limitations were identified, including the system’s less-than-ideal suitability for advanced learners and the absence of contextual information. The study concludes that, when appropriately implemented as suggested by existing literature, adaptive learning systems possess significant potential to transform online education by offering personalised and efficient learning methods. Recommendations for future developments include the integration of third-generation machine learning, ensuring equal opportunities for learners, and further refining the system to address small learner differences.5