Tai-Ping Hsu

National Taiwan Normal University, Taiwan // taipinshe@gmail.com

Mu-Sheng Chen

National Taiwan Normal University, Taiwan // mushengchen946@gmail.com

Ting-Chia Hsu

National Taiwan Normal University, Taiwan // ckhsu@ntnu.edu.tw

ABSTRACT: 

While generative artificial intelligence (GenAI) such as ChatGPT offers potential for education, its widespread use by teachers often leads to inefficient interactions, and it lacks pedagogical structure. This can diminish learners’ engagement and increase their cognitive load, hindering effective professional development. This study investigated whether a Pedagogical GenAI Agents Teaching Mode (Pedagogical GenAI Agents-TM), integrating Design Thinking as a pedagogical framework, could enhance in-service teachers’ professional development compared to a conventional ChatGPT Teaching Mode (GPT-TM). A quasi-experimental design involved 76 Taiwanese in-service teachers assigned to either Pedagogical GenAI Agents-TM or GPT-TM groups, with a focus on designing Sustainable Development Goal (SDG)-related teaching activities. Quantitative and qualitative analyses revealed that, compared to GPT-TM, Pedagogical GenAI Agents-TM significantly improved teachers’ SDG learning achievement and TPACK self-efficacy, while reducing their cognitive load. The Pedagogical GenAI Agents-TM group demonstrated more collaborative AI interaction and adopted effective learning strategies, perceiving AI as a learning partner, contrasting with the GPT-TM group’s tool-centric view and less effective information processing. The study concludes that pedagogically structured AI interaction, as embodied by Pedagogical GenAI Agents-TM, more effectively overcomes the limitations of general-purpose AI for teacher professional development. Integrating established pedagogical frameworks into AI systems fosters deeper learning, enhances teacher confidence, and alleviates cognitive strain. We therefore advocate for specialized, Pedagogical GenAI Agents designed as collaborative partners rather than mere informational tools.

Keywords: 

Pedagogical GenAI agents, Teacher professional development, Cognitive load reduction, Teacher agency, TPACK

Harun Dursun

Ministry of National Education, Turkey // Department of Education, Gazi University, Turkey // hdursun3890@gmail.com

Neşe Işık Tertemiz

Department of Education, Gazi University, Turkey // tertemiz@gazi.edu.tr

ABSTRACT: 

In the current study, the process of designing a mathematics learning environment at the primary school level with WEB 2.0 tools according to the ADDIE design model is aimed. It addresses the gap in the field by taking into account learning and teaching design models in developing content for mathematics education with new generation technological tools. In this study, it was aimed to design a learning environment for the subject of natural numbers at the fourth grade level of primary school using WEB 2.0 tools according to the ADDIE design model (analysis, design, development, implementation and evaluation). In the ADDIE stages, planning was made to prepare a suitable learning environment by revealing the students’ learning deficiencies related to the subject. In addition, 24 different WEB 2.0 tools were used in the learning environment activities designed and implemented on a group of 25 fourth-grade students who had previously studied the learning objectives of the subjects but had learning deficiencies and errors. The effectiveness of the prepared and implemented design was evaluated by looking at the learning levels of the participating students. In the light of the evaluation results, the design was improved and implemented again. This application was again implemented on a new group of 24 students. The findings revealed that the students’ learning level of the subject significantly increased as a result of the implementation of the activities within the learning environment developed with WEB 2.0 tools according to the ADDIE design model.

Keywords: 

ADDIE design model, Mathematics education, Natural numbers, WEB 2.0 tools

Xiao-Fen Shi

Institute of Teacher Education and Advanced Studies, Jiangxi Normal University, Jiangxi Province, P. R. China // 1297248249@qq.com

Zhi-Xian Zhong

Institute of Teacher Education and Advanced Studies, Jiangxi Normal University, Jiangxi Province, P. R. China // Jxzzx@126.com

Ran-Xi Yan

China-Korea Institute of New Media, Zhongnan University of Economic and Law, China // 2648004215@qq.com

ABSTRACT: 

Amid the global trend toward educational digitalization, the concept of human-machine collaboration is reshaping the ecosystem of basic education. However, the digital transformation of K-12 education faces profound challenges. Uneven resource allocation has led to a pronounced stratification pattern, resulting in a widening digital divide among K-12 teachers. This divide has emerged as a critical barrier to achieving the goal of inclusive education. To address this issue, the present study adopts grounded theory with a three-level coding approach to systematically analyze the manifestations of the digital divide among K-12 teachers. Through iterative refinement using the Delphi method, an assessment framework was developed, comprising three primary dimensions, eleven secondary indicators, and thirty-five tertiary indicators. Subsequently, the Analytic Hierarchy Process (AHP) was employed for subjective weighting, while the entropy weight method was used for objective weighting, leading to the determination of integrated weight coefficients. This study provides a theoretical basis and methodological tools for the future assessment of teachers’ digital development index and for bridging the digital divide within the teaching profession.

Keywords: 

Behaviour pattern extraction and analysis, K-12 education, Teacher training, Information literacy

Hsin-Yun Wang

Taoyuan Municipal Shou Shan Senior High School, Taiwan // wanghsinyun@gmail.com

Jerry Chih-Yuan Sun

Institute of Education, National Yang Ming Chiao Tung University, Taiwan // jerrysun@nycu.edu.tw

ABSTRACT: 

This study examined how objective and subjective engagement data predict learning outcomes in virtual reality (VR) co-creation environments. Conducted at a public high school in northern Taiwan, 83 students participated in an 18-week online-merge-offline (OMO) VR co-creation course. Random Forest (RF) models compared objective indicators (e.g., EEG, logs, performance scores) with self-reported surveys. Results showed that objective data provided greater predictive accuracy and explanatory power, while subjective measures were constrained by retrospective bias and unidimensional data structure. However, incremental validity analyses revealed comparable gains in explained variance across both data types with each added engagement dimension. This suggests that although objective data excel in predictive performance, subjective measures capture complementary insights into learners’ internal states, metacognitive awareness, and contextual experiences. Moreover, multimodal data outperformed unidimensional inputs, underscoring the value of data fusion in representing the complexity of student engagement. Sentiment analysis revealed trust as the dominant emotion, along with a mix of negative feelings, underscoring the emotional dynamics of immersive learning. To enhance instructional relevance, the study calls for the development of real-time multimodal dashboards co-designed with teachers and instructional designers – ensuring that feedback is pedagogically meaningful and context-sensitive. These findings support the integration of diverse data sources to advance adaptive support and deepen understanding of engagement in immersive learning.

Keywords: 

Multimodal learning analytics, Performance prediction, Sentiment analysis, Student engagement, Virtual reality co-creation

Siu Cheung Kong

Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR // Artificial Intelligence and Digital Competency Education Centre, The Education University of Hong Kong, Hong Kong SAR // siucheungkong@gmail.com // sckong@eduhk.hk

Luwei Ye

Artificial Intelligence and Digital Competency Education Centre, The Education University of Hong Kong, Hong Kong SAR // yealoowei@gmail.com // lye@eduhk.hk

ABSTRACT: 

Research has highlighted the importance of self-regulated learning (SRL) for personal development and lifelong learning. Therefore, it is essential to foster SRL skills in learners from a young age to prepare them to adapt to and succeed in a rapidly changing world. Generative AI (GenAI) tools offer significant potential for helping learners develop these skills through immediate, individualised feedback. This study evaluated the use of GenAI learning tools to enhance students’ SRL skills. Eighty-four senior secondary students completed a 30-hour GenAI application course and were required to reflect on their SRL skills. A mixed-methods approach was employed to evaluate the course. Pre- and post-survey comparisons indicated that the students’ SRL skills improved significantly after attending the course. A thematic analysis of their self-reflective writings revealed that the students strongly believed the course had inspired them to apply GenAI tools for acquiring subject knowledge and managing their learning. The effectiveness of this course can guide and inspire future empirical research and pedagogical practices focused on integrating GenAI tools into the educational process to develop students’ SRL.

Keywords: 

Course evaluation, Generative artificial intelligence tools, Secondary students, Self-regulated learning, Subject learning

Yuhan Dong

Faculty of Education, Shaanxi Normal University, Shaanxi, China // dongyuhan0755@163.com

Hongliang Ma

Faculty of Education, Shaanxi Normal University, Shaanxi, China // mahl@snnu.edu.cn

Bin Jing

Faculty of Education, Shaanxi Normal University, Shaanxi, China // jingbin@snnu.edu.cn

Qin Wang

Faculty of Education, Shaanxi Normal University, Shaanxi, China // qin.wang@snnu.edu.cn

Na Zhu

Zhengzhou Xingzhi Middle School, Henan, China // 717193995@qq.com

Pengcheng Cao

Faculty of Education, Shaanxi Normal University, Shaanxi, China // caopengcheng@snnu.edu.cn

ABSTRACT: 

The importance of integrating computational thinking (CT) into classrooms in K-12 education has been widely recognized. However, previous studies have shown that teachers’ abilities in this area still need to be explored and improved. This study addressed this issue by designing and implementing professional development (PD) for in-service computer science (CS) teachers incorporating established effective PD elements. Forty teachers participated in a three-month online Python programming course during the COVID-19 pandemic. To examine the effectiveness of this professional development of computational thinking (CT-PD) program, researchers investigated the impact of PD on CS teachers’ attitudes towards CT and teaching practices in the classroom. The learning outcomes attained by 104 students were also tracked to analyze its impact on students’ CT levels. Results from analyses of quantitative data and interview records show that (a) this CT-PD program positively enhanced CS teachers’ attitudes towards CT and promoted their teaching practices in the classroom, and (b) these enhancements in teacher competencies were positively associated with improvements in students’ CT skills and self-efficacy. These findings provide practical implementations for teacher educators and theoretical insights into effective models for impactful PD programs for in-service CS teachers.

Keywords: 

Computer science teachers, Computational thinking (CT), Professional development (PD), Teaching practice, Self-efficacy

Hui-Chun Hung

National Central University, Taiwan // hch@cl.ncu.edu.tw

Kai-Hsiang Yu

National Central University, Taiwan // walence31@gmail.com

Shu-Ming Wang

Chinese Culture University, Taiwan // scottie.wang@gmail.com

ABSTRACT: 

In the era of big data, a lack of project management and data analytics skills may hinder students’ ability to effectively manage projects and advance their careers. This study explores applying project management principles in a master’s program curriculum to enhance students’ learning outcomes. This study aims to utilize the Trello platform and follow the five main project management processes, including Initiation, Planning, Execution, Monitoring, and Closing, so students can improve their learning experiences. Twenty-seven students participated in a “Visual Analytics Techniques and Applications” course at a university in northern Taiwan. The activity logs in the project management platform were extracted to create a learning dashboard; utilizing learning dashboards allows students to monitor both intra-group and inter-group task progress, thereby facilitating peer regulation. This study also examined how integrating a Project Management Platform (PMP) with a Learning Dashboard (LD) contributes to students’ learning processes by tracking their participation and outcomes. The findings revealed that the project management learning dashboard increased students’ motivation and academic performance. Four key points were identified: (1) enhanced data analysis learning abilities, (2) improved project management knowledge and skills, (3) facilitated active participation in group discussions, and (4) contributed to students’ learning outcomes through planning and monitoring. The proposed project-based learning model efficiently facilitated students’ acquisition of professional knowledge, enhanced their learning outcomes, and cultivated project management knowledge and skills. This study highlights the potential benefits of integrating project management principles into educational settings to provide a novel and practical learning experience.

Keywords: 

Project-based learning, Project management, Learning analytics dashboard, Collaborative learning, Digital collaboration tools

Ewa Angoneze-Grela

Institute of Interior Design and Industrial Design, Poznan University of Technology, ul. Jacka Rychlewskiego 2, 61–131 Poznan, Poland // ewa.grela@put.poznan.pl

Barbara Linowiecka

Institute of Interior Design and Industrial Design, Poznan University of Technology, ul. Jacka Rychlewskiego 2, 61–131 Poznan, Poland // barbara.linowiecka@put.poznan.pl

Magda Matuszewska

Institute of Architecture and Heritage Protection, Poznan University of Technology, ul. Jacka Rychlewskiego 2, 61–131 Poznan, Poland // magda.matuszewska@put.poznan.pl

Łukasz Borak

Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60–965 Poznan, Poland // lukasz.borak@student.put.poznan.pl

Jakub Jagła

Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60–965 Poznan, Poland // jakub.jagla@student.put.poznan.pl

Marcin Kapiszewski

Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60–965 Poznan, Poland // marcin.kapiszewski@student.put.poznan.pl 

Marcel Rojewski

Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60–965 Poznan, Poland // marcel.rojewski@student.put.poznan.pl 

Adam Tomys

Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60–965 Poznan, Poland // adam.tomys@student.put.poznan.pl 

Dariusz Brzezinski

Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60–965 Poznan, Poland // dariusz.brzezinski@cs.put.poznan.pl

ABSTRACT: 

Recent advances in image-generative artificial intelligence (AI) have provided designers with powerful tools for quickly exploring and iterating on their creative concepts. However, the effectiveness of different sources of inspiration in supporting design development remains underexplored, particularly within design education. This study examines the impact of AI-generated images versus web-sourced images on designers’ creative outcomes. For this purpose, we have gathered a dataset of design projects created by 50 students, who used AI-generated and web-sourced images as inspiration. To identify sources of inspiration in each project, we propose computer vision metrics that measure contrast and color similarity, as well as deep learning embedding similarity. The similarities computed between final designs and inspiration images are compared with student surveys expressing their perception of sources of inspiration and with expert ratings assigned to each project. The comparison shows that the students’ stated inspiration is positively correlated with the computed inspiration-design similarity and that students exhibit a preference for AI-generated inspiration, even if they are first-time users of generative tools. Moreover, our study reveals that both web and AI images are inspirational, but they affect different aspects of design. AI inspirations correlate strongly with the overall composition and mood, and web-sourced inspirations play an important role in defining color, materials, and grounding the design in reality.

Keywords: 

Inspiration, Design education, Creative output, Generative AI, Learner perceptions

Wenhao Wang

Graduate School of Information Science and Electrical Engineering, Kyushu University // wang.wenhao.471@s.kyushu-u.ac.jp

Gwo-Jen Hwang

Graduate Institute of Educational Information and Measurement, National Taichung University of Education, Taiwan // Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taiwan // Yuan Ze University, Taiwan // gjhwang.academic@gmail.com

Asako Ohno

Information Science and Technology Center, Kobe University // ohno@kitty.kobe-u.ac.jp

Etsuko Kumamoto

Information Science and Technology Center, Kobe University // kumamoto@kobe-u.ac.jp

Hui-Chun Chu

Department of Computer Science and Information Management, Soochow University, Taiwan // Department of Applied Informatics, Fo Guang University, Taiwan // carolhcchu@gmail.com

Ching-Yi Chang

School of Nursing, College of Nursing, Taipei Medical University, Taiwan // frinng.cyc@gmail.com

Chengjiu Yin

Research Institute For Information Technology, Kyushu University, Japan // yin.chengjiu.247@m.kyushu-u.ac.jp

ABSTRACT: 

Personalized learning recommendations utilizing learning behavior patterns have emerged as a crucial research area. Backtracking behavior, defined as students revisiting pages that they have already studied, has been shown to improve learning outcomes. However, it is difficult for students to locate the content they wish to revisit within a large number of resources. In this study, we designed a page-jumping recommendation plugin to help students find the content they require, thereby enhancing their backtracking behavior. By analyzing educational content and student reading logs, this plugin can predict and suggest pages that students are likely to want to revisit. An experiment was conducted in a university course to verify the plugin’s effectiveness with respect to learning outcomes. Students who used the page-jumping recommendation plugin achieved better academic performance and demonstrated high acceptance of the system.

Keywords: 

Backtracking behavior, Adaptive learning, BERTopic, Log data, Recommendation system

Junhua Xian

Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR // junhua.ax@gmail.com

Junjie Gavin Wu

Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR // gavinjunjiewu@gmail.com

Di Zou

Department of English and Communication, The Hong Kong Polytechnic University, Hong Kong SAR // dizoudaisy@gmail.com

ABSTRACT: 

Recent researchers have explored the effects of digital games on the academic performance of students with intellectual disabilities (ID), particularly through the lens of game-based learning (GBL). GBL has emerged as an innovative approach that integrates communication and teaching activities, making the process of information transmission more vivid. However, one significant critique within this research field is the limited attention given to the impact of different variables within GBL on learners with ID. This meta-analysis aims to fill this gap by investigating certain moderating variables, including control group treatment, game type, game platform, and intervention duration, to explore their potential impact on academic outcomes. The results of analyzing the effect sizes of 24 studies published between 2013 and 2023 showed that GBL has a moderate effect on the learning of students with ID compared to instructional activities without game-based instructional interventions. It is evident that students with ID can achieve better learning outcomes in an environment that is motivating and enjoyable.

Keywords: 

Intellectual disabilities, Game-based learning, Meta-analysis, Moderating variables

Lung-Chun Chang

Department of Information Management, National Taipei University of Business, Taipei, Taiwan, ROC // angus77@ntub.edu.tw

ABSTRACT: 

Students often feel that programming logic is challenging to understand and that the learning objectives of programming courses lack real-world relevance; thus, they tend to exhibit high learning anxiety and low learning engagement in programming courses. Experiential learning models enhance students’ engagement, motivation, and learning outcomes by enabling hands-on involvement. Moreover, project-based learning models enable students to achieve learning objectives and outcomes through observation, critical thinking, and problem-solving. Therefore, the present study developed an experience-design-implementation (EDI)-based learning model for a programming course. In the experience phase, students were grouped to play a board game, allowing them to experience the enjoyment of gameplay and understand game logic, thereby increasing their learning engagement and reducing learning anxiety. During the design phase, group members collaborated to design and create meaningful game rules and procedures, thus enhancing their critical thinking and problem-solving skills. Finally, in the implementation phase, an instructor introduced the programming concepts and skills required in each step of game design, enabling students to understand how to apply programming syntax, which enhanced their learning motivation and learning outcomes. Experimental results indicated that students who engaged in EDI-based learning exhibited significantly lower learning anxiety and significantly enhanced learning outcomes, learning motivation, critical thinking tendency, and problem-solving tendency than did students who used a conventional learning approach. Furthermore, learning outcomes, learning motivation, critical thinking tendency, and problem-solving tendency had significantly positive correlations with each other.

Keywords: 

EDI-based learning, Learning outcomes, Learning motivation and anxiety, Critical thinking, Problem-solving tendency

Christopher C.Y. Yang

Department of Computer Science, National Taipei University of Education, Taiwan (R.O.C.) // cyyang@mail.ntue.edu.tw

Chengjiu Yin

Research Institute for Information Technology, Kyushu University, Japan // yin.chengjiu.247@m.kyushu-u.ac.jp

ABSTRACT: 

In recent years, the rise of detailed educational data has fueled the growth of learning analytics (LA), particularly in blended learning. While LA provides the methodology for analyzing course features and predicting performance, a robust theoretical lens is needed to interpret student behaviors meaningfully. To better understand students’ digital learning behaviors, it is essential to integrate educational theory with data-driven methods by combining behavioral data with self-reported information. Consequently, this study addresses this by investigating the relationship between interaction behaviors, learning styles, and learning outcomes grounded in Self-Regulated Learning (SRL) theory. A total of 233 first-year university students participated in a course integrating a digital textbook system, namely Digital Textbook for Improving Teaching and Learning (DITeL). Behavioral interaction data and self-reported learning styles were analyzed using correlation analysis and hierarchical clustering. Three distinct behavioral patterns were identified, with the deep engagement group outperforming others in GPA. Reflective and sensing learners demonstrated more active interaction with the digital system. The findings provide valuable insights into the dynamics of LA and student behavior analysis in blended learning models.

Keywords: 

Self-regulated learning, Learning analytics, Digital textbook log, Behavioral pattern, Learning style, Blended learning

Amir Reza Rahimi

University of Valencia, Spain // Rahimia891@gmail.com

Ana Sevilla-Pavon

University of Valencia/ IULMA, Spain // ana.m.sevilla@uv.es

ABSTRACT: 

A Virtual Exchange (VE) refers to sustained online interaction and collaboration between groups of students, and it is a subfield of computer-assisted language learning. While scholars have extensively documented its benefits in developing language learners’ cultural and language skills, few studies have examined its success through the lens of technology acceptance and L2 identities. To fill this gap, we explored students’ L2 self-identities and behavioral intentions to use VE in the future. Accordingly, 92 Spanish foreign language learners exchanged language, culture, and academic information with Cypriot and Irish students. To identify both should-have and must-have factors, we used a bisymmetric approach based on sufficiency and Nassery logic. In the symmetric phase of the study, perceived usefulness was only correlated with participants’ future ideal L2 self and current L2 self. Furthermore, students perceived VE as more authentic than their previous learning environments when achieving their objectives. This led to VE being viewed as an effective and easy-to-use learning environment. Also, the asymmetrical phase of the study simultaneously applied Fuzzy Set Qualitative Comparative Analysis (fsQCA) and Necessary Conditional Analysis (NCA), which showed that there were only two peripheral conditions that shaped learners’ behavioral intentions toward VE. Moreover, current L2 self, perceived usefulness, and attitudes were among the necessary conditions that shaped learners’ behavioral intentions in VE. Therefore, the study developed a new conceptual framework, suggesting educators acknowledge the L2 self-identities of partners before initiating an exchange and aim to incorporate those partners with similar identity goals, influencing participation intentions.

Keywords: 

Virtual exchange, L2 motivational self-system (L2MSS), Technology acceptance model, Computer-assisted language learning (CALL), Behavioral intention

Zhen Zou

Faculty of Educational Studies, Universiti Putra Malaysia(UPM), Serdang Selangor, Malaysia // Wuhan Qingchuan University, Wuhan City, Hubei Province, China // gs62501@student.upm.edu.my

Norhakimah Khaiessa Ahmad

Faculty of Educational Studies, Universiti Putra Malaysia(UPM), Serdang Selangor, Malaysia // norhakimah@upm.edu.my

Lilliati Ismail

Faculty of Educational Studies, Universiti Putra Malaysia(UPM), Serdang Selangor, Malaysia // lilliati@upm.edu.my

ABSTRACT: 

Academic writing is important in English as a foreign language (EFL) research and education, but it is difficult for teachers and students to teach and learn about the organized expression of ideas, evidence-based arguments, and logical reasoning. Advances in generative artificial intelligence (GenAI) now offer new opportunities to solve these challenges. In particular, the Chat Generative Pre-Trained Transformer (ChatGPT), released by OpenAI in November 2022, has gained a lot of attention for its ability to perform language tasks and generate human-like responses. However, its use in academic writing has raised concerns about ethics and the authenticity of scholarly work. This study focuses on the impact of ChatGPT on teaching and learning processes as well as writing performance and provides some practical recommendations for its use in the writing process by using a systematic review approach. A total of 77 empirical studies that were published between 2022 and 2024 were identified after searching Web of Science, Scopus and Google Scholar. From these studies, this review synthesized 10 topics related to the teaching and learning processes, 10 topics on writing performance, and 3 topics that make practical recommendations. The results demonstrate that the advantages of using ChatGPT in academic writing outweigh the disadvantages. Rather than only concentrating on the possible concerns, it is important to discuss solutions, such as reforming pedagogical strategies, developing institutional policies and equipping teachers and students with specific AI literacy, to take advantage of the potential of the technology effectively.

Keywords: 

ChatGPT, Academic writing, Benefits and drawbacks, Teaching and learning, Systematic review

Thean Pheng Lim

Department of Accountancy Finance and Business, Tunku Abdul Rahman University of Management and Technology, Malaysia // limtp@tarc.edu.my

Wee Chuan Teh

Department of Accountancy Finance and Business, Tunku Abdul Rahman University of Management and Technology, Malaysia // tehwc@tarc.edu.my

ABSTRACT: 

As online learning environments become increasingly prevalent, understanding the psychological and technological factors that shape students’ experiences is essential. This study investigates how broad and narrow personality traits influence techno-eustress, a positive form of technology-induced stress that enhances learning and performance. We employed a mixed-methods approach combining Partial Least Squares Structural Equation Modelling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine both individual trait effects and personality configurations. Data from 255 Malaysian university students were collected on the Big Five personality traits, IT mindfulness, personal innovativeness in IT (PIIT), and techno-eustress using validated five-point Likert scales. PLS-SEM results revealed that IT mindfulness was the strongest predictor of techno-eustress, while openness, conscientiousness, and agreeableness showed positive effects. Neuroticism negatively influenced techno-eustress, and extraversion showed an unexpectedly small negative effect. The fsQCA identified five distinct personality configurations leading to high techno-eustress, with IT mindfulness emerging as a necessary condition across all profiles. These findings offer educators useful information. Fostering IT mindfulness through specific interventions should take priority over choosing students based on personality. Meanwhile, personalised learning strategies can use different personality profiles to improve digital learning experiences.

Keywords: 

Personality profiles, Techno-eustress, IT mindfulness, Personal innovativeness in IT, Online learning

Sanki Zuquan Shao

School of Foreign Languages & Center for Technology Enhanced Language Learning, Shenzhen Technology University, Shenzhen, China // sankishao@link.cuhk.edu.hk

Michael Yi-Chao Jiang

School of Foreign Languages & Center for Technology Enhanced Language Learning, Shenzhen Technology University, Shenzhen, China // mjiang@sztu.edu.cn

Morris Siu-Yung Jong

Department of Curriculum and Instruction & Centre for Learning Sciences and Technologies, The Chinese University of Hong Kong, Hong Kong, China // mjong@cuhk.edu.hk

Bin Shen

School of Foreign Languages & Center for Foreign Language Education and Teaching, Fuzhou University, Fuzhou, China // bin.shen@fzu.edu.cn

Di Zou

Department of English and Communication, The Hong Kong Polytechnic University, Hong Kong, China // daisy.zou@polyu.edu.hk

Ching Sing Chai

Department of Curriculum and Instruction & Centre for Learning Sciences and Technologies, The Chinese University of Hong Kong, Hong Kong, China // cschai@cuhk.edu.hk

ABSTRACT: 

Traditional writing instruction often uses verbal prompts and topics that may not be relatable to language learners, making expression challenging for those lacking firsthand experience. Spherical video-based virtual reality (SVVR) offers an immersive solution by placing learners in authentic and culturally relevant contexts, fostering meaningful writing. Unlike traditional computer-generated virtual reality, SVVR is more cost-effective, accessible, and capable of providing realistic, situated experiences for learners. Based on the L2 motivational self system, this study extends existing literature by examining how SVVR fosters the ideal L2 self and positively impacts learners’ motivation and lexical gains in writing. A 14-week quasi-experiment was conducted involving 65 first-year college students. The experimental group engaged in SVVR-assisted English writing sessions, while the control group participated in the same sessions using the same materials but in a 2D format. Pre- and post-intervention questionnaires and tests measured students’ English as a foreign language (EFL) writing motivation and writing performance. Results revealed that the experimental group outperformed the control group in ideal L2 self (partial η² = 0.114) and L2 learning experience (partial η² = 0.192), while no significant difference was observed in the ought-to L2 self. In writing performance, the experimental group scored significantly better in lexical resources (partial η² = 0.524), though no differences were witnessed in task achievement, coherence and cohesion, or grammatical range and accuracy. These findings highlight the potential of SVVR to enhance EFL learners’ intrinsic motivation and lexical proficiency, offering a practical pedagogical tool to foster self-regulated learning experiences in EFL contexts.

Keywords: 

Spherical video-based virtual reality, English as a foreign language, Writing performance, Motivation, L2 Motivational Self System

Xuemin Gao

Institute of Computing Science and Technology, Guangzhou University, China // gaoxmesther@gzhu.edu.cn

Yuqin Yang

Faculty of Artificial Intelligence in Education, Central China Normal University, China // yangyuqin@ccnu.edu.cn

Yuxia Du

School of Education (Teachers College), Guangzhou University, China // dyxyuxia@126.com

Daner Sun

Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China // dsun@eduhk.hk

ABSTRACT: 

Large-class instruction limits instructors’ ability to provide timely personalized feedback. Partial pair programming (PPP) has gained attention as a means to address this challenge and enhance student engagement. Nonetheless, how to pair elementary students of different ability levels effectively within PPP instruction remains underexplored. This study therefore examined the effects of two ability-based pairing methods on CT development among elementary students with varying CT ability levels. The experimental group adopted Hamming distance heterogeneous pairing, while the comparison group used split-half Hamming distance pairing. Results showed that students paired using the Hamming distance heterogeneous method achieved greater gains in both computational concept understanding and computational problem-solving, with statistically significant advantages for medium- and low-CT students. Follow-up instructor interviews corroborated the benefits of the Hamming distance heterogeneous pairing, while also noting challenges in implementation. This study contributes empirical evidence on ability-based pairing in PPP and offers practical guidance for supporting elementary students’ CT development.

Keywords: 

Computational thinking, Partial pair programming, Pairing methods, Elementary education

Junseong Bang

Ymatics Corp, Korea // hjbang21pp@gmail.com

Sangmin-Michelle Lee

Kyung Hee University, Korea // sangminlee@khu.ac.kr

Nam Ju Kim

Yonsei University, Korea // namjukim@yonsei.ac.kr

ABSTRACT: 

As artificial intelligence (AI) becomes increasingly embedded in educational systems, concerns about its ethical, technical, and social risks are growing, particularly in large-scale deployments such as Korea’s nationwide AI-powered digital textbook (AIDT) initiative. Despite growing discourse on AI ethics, there remains a critical gap in empirical research that compares how different stakeholder groups, such as developers and teachers, perceive these risks in real-world educational contexts. This study examines stakeholder perceptions of AI-related risks in AIDT by comparing the responses of two key groups: developers (N = 31) and teachers (N = 40). The study uses a mixed-methods approach, combining survey data based on a nine-principle AI risk assessment checklist with in-depth interviews. Quantitative results reveal a significant divergence between developers and teachers in perceived risk likelihood and severity, with teachers consistently rating risks higher, especially in educational domains. Qualitative analysis further reveals differences in priority, awareness, and concern between stakeholder roles, highlighting a gap between technical development and classroom realities. Developers emphasized compliance and infrastructure, while teachers emphasized limitations in pedagogy, students’ overreliance, and the lack of AI transparency. These discrepancies are interpreted through Sociotechnical Systems Theory, emphasizing the misalignment between technical and social subsystems. The study calls for collaborative optimization through co-design and communication to build trustworthy, equitable AI in education. This research provides critical insights for education policymakers, AI developers, and practitioners seeking to implement AI in education responsibly while balancing innovation and student protection.

Keywords: 

Artificial intelligence, AI risk management, Stakeholders’ perceptions, AI digital textbook

Gwo-Jen Hwang

Graduate Institute of Educational Information and Measurement, National Taichung University of Education, Taichung City, Taiwan // Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taipei City, Taiwan // College of Management, Yuan Ze University, Taoyuan City, Taiwan // gjhwang.academic@gmail.com

Hsin-Yi Hung

Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taipei City, Taiwan // alanna00218@gmail.com

Chun-Chun Chang

Department of Nursing and Clinical Competency Center, Chang Gung University of Science and Technology, Taoyuan City, Taiwan // pure4985@gmail.com

ABSTRACT: 

In professional training, case-based learning which engages students in detailed scenarios reflecting real-world problems has been widely adopted. Virtual reality (VR) is increasingly utilized to provide immersive and safe learning environments. Through the authentic contexts provided by VR, students are generally guided to experience problem situations and practice problem solving following the PEAR (Problem-Guiding, Exploration, Abstraction, Reflection) model. However, identifying the problems to be solved by analyzing practical cases experienced in VR contexts is still a challenge for most students. To enhance students’ case-analyzing performance in VR contexts, a graphic organizer-based VR approach is proposed which integrates the features of concept maps and mind maps into the PEAR-based virtual learning process. Moreover, a VR-based learning system was implemented accordingly. To evaluate the effectiveness of the proposed learning mode, a quasi-experiment was designed in a nursing training course. Participants were nursing students from two classes at a university in northern Taiwan. One class, consisting of 39 students, was the experimental group which adopted the graphic organizer-integrated PEAR-based virtual learning (GO-PEAR-VL) approach. The other class, also consisting of 39 students, was the control group which adopted the conventional PEAR-based virtual learning (C-PEAR-VL) approach. The results showed that the GO-PEAR-VL approach effectively improved students’ learning achievement and problem-solving tendency compared to the C-PEAR-VL approach. This study enhanced students’ learning performance in VR environments, enabling them to reflect from various perspectives and build self-confidence for future clinical practice.

Keywords: 

Professional training, Nursing education, Technology-assisted learning, Graphic organizer, Virtual reality