Journal of Information and Learning

Exploring Technology Acceptance and Social Media Addiction: Effects on Academic Success among International University Students

Wanlee Putsom — 2026-04-01

This research aims to study the effects of technology acceptance and social media addiction on the academic achievement of international university students. Data were collected via online questionnaires from a sample of 556 students. Data were collected from 556 students and analyzed using Exploratory Factor Analysis (EFA), correlation, Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). Model fit was acceptable (×²/df = 1.10; p = 0.10; GFI = 0.96; AGFI = 0.94; NFI = 0.97; IFI = 1.00; CFI = 1.00; RMSEA = 0.01), with maximum likelihood estimation. The results of the research found that security and privacy and perceived behavioral control had a positive impact on perceived usefulness. Self-efficacy and perceived behavioral control had a positive effect on perceived ease of use. In addition, perceived usefulness had a positive impact on online social media addiction and academic success; and perceived ease of use had a positive effect on academic success. However, the data analysis showed that perceived ease of use had a negative effect on online social media addiction. Furthermore, security and privacy did not affect perceived ease of use, self-efficacy did not affect perceived usefulness, and perceived ease of use did not affect perceived usefulness. The study concludes that perceived usefulness, perceived ease of use, and online social media addiction are positively associated with academic success, suggesting that students who view social media as academically useful may paradoxically become more dependent on it for learning and non-learning purposes.

Development of Learning Management Activities Integrating Artificial Intelligence Technology and Design Thinking to Enhance Innovation Capability and Learning Engagement of Eighth Grade Students

Jaruwan Khotngao — 2026-04-03

The objectives of this research are 1) to develop instructional activities integrating artificial intelligence (AI) technology with the design thinking process to achieve quality and effectiveness based on the 80/80 criterion; 2) to examine students’ progress scores from learning activity worksheets; 3) to compare students’ learning achievement before and after instruction; 4) to investigate students’ innovation capability; and 5) to examine students’ learning engagement. The samples consisted of 40 MathayomSuksa 2 students at Nawaminthrachinuthit Satriwitthaya Phutthamonthon School in the first semester of the academic year 2025, selected through simple random sampling. The research instruments included: 1) a semi-structured expert interview form; 2) an interactive learning module developed on the Google Classroom platform, integrating artificial intelligence technology with the design thinking process; 3) instructional activity plans; 4) an achievement test; 5) an innovation capability assessment form; and 6) a learning engagement scale. Data were analyzed using mean, standard deviation, percentage, and a dependent t-test. The results showed the following: 1) the developed instructional activities were rated at the highest level of appropriateness (M = 4.67, SD = 0.49) and demonstrated an effectiveness index of E₁/E₂ = 92.42/91.25, exceeding the set criterion; 2) the average progress score across six assessments (total score = 30) was 27.73 points, or 92.43%, indicating continuous improvement in students’ learning performance; 3) students’ posttest achievement scores were significantly higher than those of their pretest ones; 4) students’ innovation capability was at a good level (M = 33.50 out of 40); and 5) learning engagement after instruction reached the highest level (M = 4.69, SD = 0.65). The findings achieved all five research objectives, indicating that the developed instructional activities were of high quality and effectiveness, and could enhance students’ learning achievement, progress, innovation capability, and learning engagement effectively.

Development of an Intelligent Tutoring System for SQL SELECT Statement Practice: A Case Study of Buriram Rajabhat University

Wilairat Yathongchai — 2026-04-03

This research aims to: 1) design and develop an intelligent tutoring system (ITS) for the learners to practice SQL SELECT statement skills, 2) evaluate learning outcomes and SELECT query-writing skills after using the system, and 3) assess learners’ satisfaction. The study integrated ITS design principles with the system development life cycle (SDLC). The participants were 95 undergraduate students of information technology program at Buriram Rajabhat University, selected via cluster sampling. The proposed ITS comprises four modules—User Interface, Pedagogical, SQL Knowledge Base, and Learner—leveraging an ontology with rule-based inference to select appropriate resources/practice tasks, and deliver personalized feedback. The system consists of two major functions: (a) a SELECT practice component that validates both syntax and semantics and provides progressive, effort-contingent feedback; and (b) contents for the SELECT statement. Evaluation results showed process/product efficiency scores of E₁/E₂ = 79.56/75.12 (exceeding the 75/75 criterion) and an effectiveness index of 0.633, corresponding to a medium N-gain with 93.68% of learners demonstrating medium to high progress. Log analysis indicated purposeful trial-and-error behavior; however, while success rates decreased because of task difficulty, learner engagement persisted due to the specific, timely, and incremental feedback. Learner satisfaction reached the highest level across four areas: usability, content, learning activities, and learning support. This reflects that the system is effective and efficient, and can be used to develop skills and promote learning for students.

The Development of an Online Training Management System Integrating Artificial Intelligence Tools and Gamification Concepts for Teachers under the Ministry of Education

Anansak Srangkham — 2026-04-03

The objectives of this research were 1) to develop an online training management system that integrates artificial intelligence tools and gamification concepts for teachers under the Ministry of Education, and 2) to examine users’ satisfaction with the system. The sample comprised 100 teachers under the Nakhon Pathom Primary Educational Service Area Office 1 who voluntarily participated in the training and were selected using volunteer sampling. The research instruments consisted of the developed online training management system with integrated artificial intelligence and gamification components, a system quality evaluation form, and a user satisfaction questionnaire. Data were analyzed using percentage, mean, standard deviation, and content analysis. The results revealed that: 1) the developed online training management system integrating artificial intelligence tools and gamification for teachers under the Ministry of Education consisted of five components: (1) user management, (2) content management, (3) progress tracking and assessment, (4) an AI-driven chatbot system, and (5) gamification system. The system quality was rated at a very high level (M = 4.83, SD = 0.37); and 2) the participants reported the highest level of satisfaction with the system (M = 4.81, SD = 0.39). The research results showed that the developed online training management system can be effectively used for training purposes.

User Requirements for Designing an Agricultural Research Dataset Repository

Benjamart Saenluang — 2026-04-01

This research aimed to examine user requirements and propose design guidelines for an agricultural research dataset repository to support education and research. A qualitative approach was used to collect data through in-depth interviews with 21 key informants. Results revealed that users perceived the necessity and benefits of an agricultural research dataset repository as a storage, dissemination, and retrieval platform for research and development. This approach facilitated user access and increased efficiency while also providing cost-effective research resources and promoting long-term and sustainable utilization. Regarding the agricultural dataset repository's requirements, users preferred a simple interface design with clear data organization and a color scheme consistent with the University's main website to enhance credibility and support cross-platform data linkage, registration-free access, basic and advanced search functions, and an automatic backup system for long-term preservation. Users emphasized the importance of accurate and comprehensive metadata appropriate for agricultural research datasets, with content classification by data characteristics (Tabular, image, text) and readiness (Raw, processed). Furthermore, the repository was expected to be reliable and systematically managed, save searching time, and provide opportunities for further research. However, researchers expressed concerns about dataset ownership, rights infringement, academic advantages, and data security, which impact the disclosure and sharing of research datasets and the promotion of open science in Thailand.

A Study of Factors Related to Research Articles Published in International Journals of Mahidol University, Kanchanaburi Campus

Sudarat Pandonlan — 2026-04-01

The assessment of research quality through the evaluation of articles published in international academic journals is widely recognized among scholars in Thailand. Therefore, understanding trends and factors associated with research articles published in international journals is highly beneficial for researchers. This study aimed to investigate factors associated with the publication of research articles in international journals by Mahidol University, Kanchanaburi Campus. A retrospective analysis was conducted on 160 research articles indexed in the SCOPUS database from 2019 to 2023. The data were analyzed using descriptive statistics and inferential statistics (chi-square test) to examine the relationships between relevant factors and the quality levels of journals, categorized by quartiles (Q1–Q4) and non-quartile journals. The results showed that publications were distributed across journal quartiles as follows: Q1 (43.75%), Q2 (23.12%), Q3 (23.75%), Q4 (6.88%), and non-quartile journals (2.50%). Factors significantly associated with publication in Q1 journals included the number of co-authors, research collaboration partners, the number of collaborating institutions, and the h-index of the corresponding author (p < .05). No significant factors were found for Q2 journals. For Q3 journals, the number of co-authors showed a significant association (p < .05). In Q4 journals, significant factors included research collaboration partners, the number of collaborating institutions, and the h-index of the corresponding author (p < .05). For non-quartile journals, significant associations were found with research collaboration partners and the number of collaborating institutions (p < .05). These findings indicate that promoting academic collaboration networks and enhancing researchers’ capacity, particularly that of lead or corresponding authors, can effectively increase the likelihood of their publishing in high-quality international journals.

The Relationship Between Attitudes Toward Artificial Intelligence and Work Performance: A Case Study of Support Staff at Prince of Songkla University, Surat Thani Campus

Tawatchai Adithepsathit — 2026-04-01

This research aimed to study the attitudes and readiness for applying artificial intelligence (AI) technology and to analyze the relationship between attitudes towards AI, readiness for applying such technology, and the performance of administrative staff in the Surat Thani Campus Office, Prince of Songkla University. The sample consisted of 116 administrative staff members, representing 77.33% of the total population. The research instrument was a questionnaire, which was validated using Cronbach's alpha coefficient of 0.86. Data were analyzed using descriptive statistics, Pearson's correlation coefficient, and multiple regression analysis. The results showed that administrative staff members had a high level of attitude towards AI (M = 4.17), a high level of overall performance (M = 4.09), and a high level of readiness for applying the technology. However, the mean score was lower than other aspects (M = 3.86). Correlation analysis showed that attitude, readiness, and performance had a statistically significant positive correlation at the .01 level. Furthermore, multiple regression analysis revealed that attitude towards AI and application readiness could jointly predict performance at a relatively high level (R² = 0.559), with attitude towards AI having a slightly greater predictive influence than application readiness. The research results reflect that promoting positive attitudes coupled with developing readiness for use, especially in terms of organizational support and access to resources, is crucial for supporting the application of AI in managerial roles and can be used as supporting information for policy planning and organizational management in the context of higher education institutions.

A Bibliometric Analysis of Recent Research on Major Rice Components

Janya Thonabut — 2026-04-01

Rice is a globally important staple crop with growing scientific attention to its value-added components. This study aimed to conduct a bibliometric analysis to map research trends related to three major rice components: rice starch, rice protein, and rice bran oil. Bibliometric data were retrieved from the Scopus database, covering publications from 2020 to early 2025. A total of 298 documents on rice starch, 141 on rice protein, and 213 on rice bran oil were analyzed using Scopus analytical tools and VOSviewer software for keyword co-occurrence and author co-citation mapping. The findings revealed a consistent growth in research output across all three components, with rice starch receiving the greatest scientific attention, followed by rice bran oil and rice protein. China demonstrated dominant research productivity across authorship, institutional contributions, and funding support. Thematic analysis indicates a strong emphasis on structural characterization, functional properties, processing technologies, and functional food applications. Cluster analysis identified four, five, and five thematic clusters for rice starch, rice protein, and rice bran oil, respectively, highlighting diverse research trajectories. Despite the increasing research output, rice protein remains comparatively underexplored. This study contributes to knowledge mapping in food science by providing a component-level bibliometric perspective and offers insights for research planning, policy development, and industrial innovation in rice-based product development. This bibliometric analysis emphasizes the dynamic and developing nature of rice research, concentrating on three primary components: rice starch, rice protein, and rice bran oil. A substantial rise in research output is evident from the upward trend of research documents between 2020 and 2025. Rice starch is the most extensively researched component, succeeded by rice bran oil and rice protein. China far surpasses other nations in global contributions regarding rice research.

The Development of an AI-Driven Personalized Project-Based Learning Model to Strengthen Programmers’ Competencies in Higher Education

Napharat Chooprai — 2026-04-03

The role of technology in the digital era extends to economic and social development, including developing various capabilities for students' education. In technology fields, programming competence is considered a fundamental and essential skill for graduate studies, therefore, it is promoted in higher education curricula. This research study aims to synthesize and evaluate a conceptual framework of a learning model that integrates project-based learning (PBL) with personalized learning driven by artificial intelligence agents, to enhance programmers’ basic competencies in higher education. The research methodology involved developing a learning model through a study of concepts and theories, drafting a detailed model, conducting focus group discussions to refine the draft, and evaluating the model's suitability with experts. A group of nine experts participating in the evaluation. The research instruments included learning model that integrates Project-Based Learning with personalized learning driven by artificial intelligence agents, to enhance the basic competencies of programmers in higher education, in-dept higher education, in-depth interviews and group discussions, Data were analyzed using basic statistical methods, specifically mean and standard deviation. The results indicated that the developed model consisted of three main components: 1) input factors encompassing core principles, objectives, and content; 2) learning process featuring a six-step framework (1) conceptual sparking, (2) strategic planning, (3) implementation, (4) success summarizing, (5) idea dissemination, and (6) experiential reflection and summarization; 3) Assessment and evaluation of programmers’ competencies before and after the learning. The evaluation of the model’s appropriateness was at the highest level (M = 4.70, SD = 0.50) indicating that the model is highly suitable and can be applied as a prototype for actual instructional practices.