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Enhancing Teaching and Supervisory Staff’s Creative Problem-Solving Skills
(2025-05-20)
Charoentham, Mai
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Kantathanawat, Thiyaporn
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Pimdee, Paitoon
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Apisuksakul, Kwantisara
This research analyzed creative problem-solving (CPS) components and examined the perceptions of Thai educational personnel regarding their CPS abilities. The sample consisted of 534 primary school teachers and educational supervisors during the 2024 academic year, selected through multistage random sampling. Data were collected using a questionnaire assessing CPS skills, which were then analyzed using means (M), standard deviations (SD), and second-order confirmatory factor analysis (CFA). The research revealed that the second-order CFA model for CPS among educational personnel (teachers and supervisors) consists of five key components. Ranked from highest to lowest, these were educators' perceptions of their CPS abilities to solve problems (SOL) (M = 4.23, SD = 0.54), ability to identify problems (IDE) (M = 4.17, SD = 0.57), ability to create knowledge (CRE) (M = 4.17, SD = 0.59), ability to discover concepts (INS) (M = 4.12, SD = 0.58), and ability to discover methods to solve problems (MET) (M = 4.11, SD = 0.58). The model strongly aligned with empirical data, indicating that all three models exhibited positive component weights (β) that were statistically significant at the .01 level. This finding underscores the strength of the CPS framework for educational personnel. These findings provide compelling evidence for the effectiveness of the proposed model in assessing and enhancing CPS skills among educational professionals, contributing valuable insights to both practice and future research in this field. This study fills a gap in the literature by providing empirical evidence on the CPS capabilities of educational personnel.
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Enhancing Psychological Well-Being Assessment Through Data Mining: A Case Study from Thailand
(2025-04-01)
Treearpornwong, Asamaporn
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Kantathanawat, Thiyaporn
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Charoentham, Mai
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Pimdee, Paitoon
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Sukkamart, Aukkapong
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.
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Development of a problem-solving ability learning model for children with intellectual disabilities
(2025-04-01)
Kantathanawat, Thiyaporn
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Treearpornwong, Asamaporn
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Charoentham, Mai
BACKGROUND: Enhancing problem-solving abilities (PSA) in children with intellectual disabilities is critical for fostering independence and adaptive skills. The initial PCTL- model integrated phenomenon-based learning (PhBL), cognitive coaching (CC), task analysis (TA), and optimal learning environments (LE) to address the unique educational needs of this population. MATERIAL AND METHODS: The study employed a two-phase approach. In phase 1, a conceptual framework for the PCTL model was synthesized through a systematic review of existing literature and an analysis of the teaching contexts specific to children with intellectual disabilities. In phase 2, the model was evaluated by eight educational experts specializing in curriculum design, assessment, special education, and technology. Quantitative ratings were collected to assess feasibility, utility, propriety, and accuracy, with an overall mean of 4.40 (SD = 0.51), indicating high appropriateness. Qualitative feedback was analyzed to refine the model, which was subsequently expanded and became the PCTL + PSA model. RESULTS: The expert panel validated the PCTL model, emphasizing its comprehensive structure and practical applicability. The model after expert assessment and input was expanded to five key components for developing PSA. Moreover, PSA is identified as being involved in identifying problems, subdividing problems, analyzing and proposing solutions, evaluating methods, and assessing outcomes. Across all evaluation dimensions, the PCTL + PSA model achieved high scores, with the utility dimension rated the highest (mean = 4.69, SD = 0.49, P < 0.05). The findings highlighted the importance of fostering a supportive LE - physically, psychologically, socially, and informationally - to maximize the model's effectiveness. CONCLUSION: This study introduces a validated and practical framework tailored to enhance problem-solving skills in children with intellectual disabilities. The final PCTL + PSA model contributes to inclusive education by addressing the unique learning needs of this population and providing actionable strategies for educators to improve classroom practices.
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High dynamic range preprocessing, ParallelAttention Transformer and CoExpression analysis for facial expression recognition
(2025-04-01)
Zhou, Yuntao
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Kantathanawat, Thiyaporn
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Tuntiwongwanich, Somkiat
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Liu, Chunmao
Facial 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.
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Enhancing digital literacy in Thai higher education: A strategic imperative
(2025-01-01)
Noppakhunwong, Thaksina
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Kantathanawat, Thiyaporn
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Pimdee, Paitoon
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.
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Neural Backward Chaining Logic Algorithm Based on Dynamic Knowledge Region Segmentation in Knowledge Graph Completion
(2025-01-01)
Liu, Chunmao
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Tuntiwongwanich, Somkiat
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Kantathanawat, Thiyaporn
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.
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Artificial Intelligence-driven Cognitive Diagnosis anAdaptive Learning: The Impact of Online Course Stickiness and Learning Skills
(2025-01-01)
Liu, Chunmao
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Tuntiwongwanich, Somkiat
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Kantathanawat, Thiyaporn
The study analyzes behavioural data that express cognitive connotations, establishes AI-driven cognitive diagnosis and adaptive learning, fills the gap in multimodal data fusion in characterizing cognitive characteristics, constructs a holistic cognitive diagnosis model, and evaluates the impact of AI-driven adaptive learning models on online course stickiness and learning skills. The study innovatively proposes four components of the cognitive diagnosis model: the core theoretical framework of the model composed of cognitive dimensions, the input variables of the model consisting of behavioural data features, the diagnostic model as a method for calculating cognitive states, and the cognitive diagnosis output. The study sorted out the core theoretical framework of the model and proposed behavioural data feature input variables, and completed the cognitive diagnosis output in three dimensions: learning momentum, effectiveness, and strategy through the XGBoost model based on the Gradient Boosting framework.An adaptive online learning model based on behavioural data cognitive diagnosis and knowledge graph is proposed, which includes six parts: input layer, feature extraction layer, cognitive diagnosis module, learning path recommendation module and output layer. The cognitive diagnosis module uses the feature weights calculated by XGBoost as input to predict the mastery of knowledge points through LSTM improved by deep learning recurrent neural network (RNN) and makes learning recommendations based on knowledge graph, cognitive evaluation matrix CEM and collaborative filtering algorithm. Experimental results show that the adaptive learning model of behavioural data cognitive diagnosis has more advantages than traditional online learning, and the adaptive online learning model driven by artificial intelligence behavioural data mining can effectively improve course stickiness, learning skills and platform experience.
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Addressing the Priority Needs of Educators in Delivering Sexuality Education to High School Students with Intellectual Disabilities
(2025-01-01)
Kantathanawat, Thiyaporn
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Tungkunanan, Pariyaporn
The study assessed sexuality health education management for secondary school students with intellectual disabilities in Thai specialized schools. It utilized a mixed-methods approach including a quantitative study via questionnaires and a qualitative analysis through in-depth interviews with teachers and experts. From use of the Priority Needs Index Modified (PNI<inf>Modified</inf>) identification of the highest priority requirements showed that media characteristics understanding, media usage readiness, and content substance were most essential. Effective learning management requires easy-to-use media with stimulating visuals and understandable content. Teachers need expertise in media, teaching techniques, and technology. Results provide insights tailored for sexuality health education for students with intellectual disabilities.
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Integrating Mastery Adaptive and Problem-Solving (MAPS) Digital Technology Skills into a Thai Community College Student Learning Model
(2025-01-01)
Kantathanawat, Thiyaporn
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Ussarn, Anyamanee
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Charoentham, Mai
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Pimdee, Paitoon
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.
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Enhancing Digital-Age Metacognition: A Framework for Cognitive Innovation in Thai Secondary Education
(2025-01-01)
Kantathanawat, Thiyaporn
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Thongsomnuek, Natarika
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Charoentham, Mai
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Pimdee, Paitoon
Background/purpose. The increasing prevalence of digital tools in education necessitates models that enhance students' metacognitive skills. Despite this need, limited research exists on structured pedagogical approaches to foster metacognition within digital learning contexts. This study aimed to develop and evaluate the Cognitive Innovation Model to Enhance Metacognitive Skills in the Digital Age (CIMEMSDA) for Thai secondary students, addressing this gap in contemporary education. Materials/methods. A quasi-experimental design was employed to assess the efficacy of CIMEMSDA, which follows a four-stage structured approach: (I) introduction and recalling, (ii) reviewing and planning, (iii) investigating and applying knowledge, and (iv) summary and evaluation. Rooted in constructivist and metacognitive principles, the model was validated by nine experts for utility, feasibility, suitability, and accuracy. The study involved 80 Grade 8 students in 2024, divided equally into experimental and traditional groups. The experimental group used CIMEMSDA with modules on computational thinking and Python programming, while the traditional group received standard instruction. Results. The experimental group demonstrated significantly higher metacognitive skills and academic performance than the traditional group. The MAPS Model significantly improved students' digital technology competencies, with post-learning DTS scores exceeding the benchmark by 8.07%. This demonstrates the model's ability to surpass foundational expectations and foster advanced technological skills. Students maintained their academic achievement and digital technology skills for 14 days post-learning without significant decline, illustrating the model's effectiveness in ensuring durable and long-lasting learning outcomes. Conclusion. CIMEMSDA shows strong potential as an educational tool for enhancing metacognitive skills in the digital age. Its structured, stage-based approach aligns well with contemporary educational practices, addressing critical gaps and offering a feasible framework for integrating metacognitive skill development into secondary education.

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