Kai-Yi Chin

Department of Data Science, Soochow University, Taiwan (R.O.C.) // kychin.scholar@gmail.com

Tzu-Chi Kuo

Department of Data Science, Soochow University, Taiwan (R.O.C.) // kuotzuchi7@gmail.com

Kai-Hsiang Yang

Department of Mathematics and Information Education, National Taipei University of Education, Taiwan (R.O.C.) // khyang@mail.ntue.edu.tw

ABSTRACT: 

Many studies have integrated concept mapping mechanisms into Digital Game-Based Learning (DGBL), showing the potential for helping students memorize knowledge and grasp complex concepts during gaming. While DGBL approaches with concept maps have promising outcomes, their effectiveness depends on proper integration within instructional and game design. Consequently, this study proposed a DGBL system incorporating distinct mapping mechanisms to assess their efficacy in enhancing financial literacy education. A trial experiment was conducted to investigate the impact of employing different concept mapping mechanisms on students’ learning outcomes, motivation, and self-efficacy. Firstly, the experimental results regarding learning outcomes revealed that the DGBL system with handwriting concept maps produced the most beneficial students’ learning outcomes than those a who used the other two methods. Secondly, the experimental results of self-efficacy demonstrated that the DGBL system with handwriting concept maps leads to more positive self-efficacy perceptions among students in comparison to other methods. Lastly, the experimental results of learning motivation showed that the DGBL system with handwriting concept maps outperformed the other groups. Our findings also suggested that while the DGBL system could indeed stimulate students’ curiosity and motivation, the approach of utilizing handwriting concept maps for learning facilitated a heightened focus and improved cognitive processing capabilities during the process of concept mapping. As a result, this approach fostered a more pronounced sense of the learning content’s significance for the students.

Keywords: 

Games, Post-secondary education, Mobile learning, Concept map  

Jen-I Chiu

Department of Information Communication, Yuan Ze University, Taiwan // jichiu@saturn.yzu.edu.tw

Mengping Tsuei

Graduate School of Curriculum and Instructional Communications Technology, National Taipei University of Education, Taiwan // mptsuei@mail.ntue.edu.tw

ABSTRACT: 

Virtual reality (VR) enables the low-cost simulation of hazardous situations and has been used widely in safety education. This meta-analysis was conducted to investigate the effectiveness of VR use in this context in terms of students’ learning outcomes (motivation, presence, safety behaviour and safety knowledge). Thirty-six articles published between 2008 and 2023 were included. The overall effect size for VR use in safety education was medium (g = 0.53). Significant positive effects on motivation, presence, safety behaviour and safety knowledge were observed, supporting the effectiveness of VR in safety education. Primary moderating effects associated with the effectiveness of VR use included age, learning application type, treatment duration, pre-training, feedback timing and feedback level. Based on the results, we make suggestions regarding the impacts of instructional principles and hardware limitations on VR-based instruction.

Keywords: 

Feedback, Meta-analysis, Pre-training, Safety education, Virtual reality  

Shurui Bai

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

Khe Foon Hew

Faculty of Education, The University of Hong Kong, Hong Kong SAR // kfhew@hku.hk

ABSTRACT: 

Leaderboards visually display social comparison results. The effects of specific situational factors embodied by absolute and relative leaderboards have received little attention. In this study, we compared the effects of an absolute and a relative leaderboard on students’ learning engagement, strategies, performance, and perceptions in fully online classes. The results showed that, compared with the absolute leaderboard group, comparison with neighboring competitors in the relative leaderboard group led to a higher level of learning engagement and performance, and encouraged constructive competitiveness that motivated the students to prioritize knowledge mastery over mere task completion. The students at all levels in the relative leaderboard group reported a satisfied, motivated, and progress-oriented attitude. Although the top-ranked students in the absolute leaderboard group were motivated to work harder and were happy to see their achievements publicly displayed, the middle- and bottom-ranked students reported increased peer pressure as they progressed. Our findings lead to the following two practical implications. First, use relative leaderboards to foster constructive competition. Second, limit the public display of the rankings of bottom-ranked students to enhance their learning motivation and engagement.

Keywords: 

Absolute leaderboard, Relative leaderboard, Gamification, Social comparison 

Yang-Lu Xiong

College of Education, Zhejiang University of Technology, Zhejiang, China // 1487041493@qq.com

Wei Xu

College of Education, Zhejiang University of Technology, Zhejiang, China // 499758711@qq.com

Xin-Yu Lin

College of Foreign Languages, Zhejiang University of Technology, Zhejiang, China // 287455485@qq.com

ABSTRACT: 

Gamified learning (GL) plays a crucial role in enhancing both the efficiency and engagement of K-12 students’ learning experiences. However, there are few studies to explore whether the GL specially designed for the curriculum will affect the cultivation of students’ ability. An extensive search of publications from 2013 to 2023 was conducted across three databases: Web of Science, ERIC, and Scopus. Citations and extracted data from the articles were independently assessed. Experimental or quasi-experimental studies specifically investigating the impact of GL on K-12 students’ learning outcomes were considered eligible according to the inclusion criteria. This study aims to explore which type of GL had a more significant impact on K-12 students’ holistic abilities, including cognitive and non-cognitive abilities, when compared between specialized GL designed for the study’s curriculum and directly using pre-existing GL models. Results revealed that GL had upper-medium effects on K-12 students’ Learning outcomes (ES = 0.701, p < .001), and specialized GL designed for the study’s curriculum showed superior effectiveness and had a more significant positive influence (ES = 0.821, p < .001). Furthermore, moderator analyses showed that experimental cycle, levels of schooling and disciplines moderated the effects of GL. In conclusion, we propose that GL in K-12 needs to be tailored to different levels of schooling and disciplines, with appropriate experimental cycle to promote and stimulate learning efficiency of K-12 education. However, future research should expand data sources, incorporate more diverse moderator variables, and include multilingual literature to enhance the comprehensiveness and generalizability of the findings.

Keywords: 

Gamified learning, Learning outcomes, K-12, Meta-analysis

Kerem Ermis

Hacettepe University, Ankara // keremermis@hacettepe.edu.tr

Alev Ateş-Çobanoğlu

Ege University, İzmir // alev.ates@ege.edu.tr

ABSTRACT: 

Present study is about when and how to make use of video in a blended teacher education course for boosting learning motivation. One of the blended learning models, the lab-rotation model was applied in assistance with original video materials, which were produced for the study. In this quasi-experiment, we compared the learning motivation of two groups: learners who viewed video at the beginning of the lessons (BG) and those at the end (EG). The experimental process took place in the Instructional Material Design course for four weeks. The authors produced 15 educational videos and applied them to 52 preservice teachers of Turkish language. The pretest and posttest learning motivation scores and participants’ views on contributions of video on their learning motivation and lab-rotation model itself were collected. The quantitative findings suggest that the lab-rotation design significantly increased learner motivation of both groups towards the course. Besides, no significant difference was found between the two groups, which indicates that there was almost equal increase in the learning motivation. Moreover, qualitative findings also support that lab-rotation positively affects learning motivation. Thus, we recommend practitioners to apply the lab-rotation model amongst other blended learning models to increase learner motivation especially by facilitating educational video in higher education. Regardless of the timing for using video in a course flow, instructional designers can be encouraged to use educational video to increase learner motivation.

Keywords: 

Blended learning, Lab-rotation model, Multimedia, Video, Motivation

Isha DeCoito

Western University, Canada // idecoito@uwo.ca

Lisa K. Briona

Western University, Canada // lbriona@uwo.ca

ABSTRACT: 

Early attempts to teach travel training to people with intellectual and/or developmental disabilities (IDDs) utilized simulation, role playing, and prompting systems; however, these studies utilized interventions that were not age appropriate. More recently, non-interactive computer-based video instruction has been shown to be temporarily effective in teaching young adults diagnosed with IDDs how to signal the bus driver when they wanted to disembark, but the lack of interactivity precluded lasting outcomes. To address this gap, the authors document the creation of a gamified training application, Catch the Bus©(CtB), that uses gamification dynamics, mechanisms, and components. Through the lenses of goal-setting theory, the technology acceptance model, and social cognitive theory CtB training emulates progression as users acquire skills necessary to scaffold to the next level of training. CtB training explicitly focuses on elapsed time to (a) circumvent the expectation of conventions of near-instantaneous travel often associated with video games, and (b) foster time management skills. To explore the effectiveness of CtB training, a mixed methods pilot case study was conducted with individuals having IDDs. The study focused on the efficacy and impact of CtB training on participants’ skills and anxiety related to public transit. Findings indicate that CtB training resulted in an increase in participants’ self-efficacy in terms of planning a bus trip to new destinations, utilizing bus route maps, and transferring buses correctly. Additionally, CtB training decreased participants’ anxiety associated with public transportation usage and increased participants’ confidence in navigating public transit.

Keywords: 

Public transportation, Gamification, Teaching/learning strategies, Cooperative/collaborative learning, Instructional games

Listiani Listiani

University of Szeged, Hungary // University of Muhammadiyah Purwokerto, Indonesia // listianiriyanto@gmail.com

Ágnes Hódi

University of Szeged, Hungary // hodi.agnes@szte.hu

Marianne Nikolov

University of Pécs, Hungary // nikolov.marianne@pte.hu

ABSTRACT: 

In recent years, there has been an increase in research on English as a foreign language (EFL) learners’ engagement with teachers’ feedback; however, little is known about how students engage with feedback combining written and audio feedback. Previous research has primarily focused on a single mode of feedback addressing specific writing issues, although findings have indicated the potential of combined modes of feedback (CMF) for addressing a broader range of writing problems across different levels. The purpose of this study was to address this gap by examining how 23 low-proficient university students behaviorally engaged with their teacher’s CMF in an EFL writing class in Indonesia. Datasets included students’ initial and final drafts of their descriptive and narrative tasks and their teacher’s CMF (audio and written). The findings revealed that successfully used feedback was more frequent than partially and unused feedback. The level of behavioral engagement varied across the language features addressed in the teacher’s feedback and the error categories also varied between the two writing tasks. Students used several strategies, including Revision, No Revision, Deletion, Substitution, and Addition. These strategies generally concerned micro level errors, which did not require extensive understanding and knowledge to implement the feedback. This article discusses the study’s pedagogical implications, limitations, and potential directions for future research.

Keywords: 

Behavioral engagement, Teacher combined audio and written feedback mode, L2 writing, Feedback uptake, Revision strategies

Kristjan-Julius Laak

Institute of computer science, University of Tartu, Estonia // kristjan-julius.laak@ut.ee

Jaan Aru

Institute of computer science, University of Tartu, Estonia // jaan.aru@ut.ee

ABSTRACT: 

Personalized learning (PL) aspires to provide an alternative to the one-size-fits-all approach in education. Technology-based PL solutions have shown notable effectiveness in enhancing learning performance. However, their alignment with the broader goals of modern education is inconsistent across technologies and research areas. In this paper, we examine the characteristics of AI-driven PL solutions in light of the goals outlined in the OECD Learning Compass 2030. Our analysis indicates a gap between the objectives of modern education and the technological approach to PL. We identify areas where the AI-based PL solutions could embrace essential elements of contemporary education, such as fostering learner’s agency, cognitive engagement, and general competencies. While the PL solutions that narrowly focus on domain-specific knowledge acquisition are instrumental in aiding learning processes, the PL envisioned by educational experts extends beyond simple technological tools and requires a holistic change in the educational system. Finally, we explore the potential of generative AI, such as ChatGPT, and propose a hybrid model that blends artificial intelligence with a collaborative, teacher-facilitated approach to personalized learning.

Keywords: 

Personalized learning, Artificial intelligence, Adaptive learning systems, Educational technology, Generative artificial intelligence

Letty Y.-Y. Kwan

The University of Macau, Taipa, Macau // lettykwan@um.edu.mo

Yu Sheng Hung

The University of Macau, Taipa, Macau // YC47387@um.edu.mo

Yilin Wang

The University of Macau, Taipa, Macau // yilinwang482@gmail.com

ABSTRACT: 

MOOCs (Massive Open Online Courses) experienced exponential growth during the COVID-19 pandemic, and their relevance in education continues today. However, research on improving learning outcomes in MOOCs remains limited. This study addresses this gap by collecting data from 25,745 learners (with 11,904 learners that fit the analytical criteria) enrolled in a computer science course on Coursera. It examines learners’ clickstream behaviors, homework participation, and forum discussions to explore how these behaviors relate to final learning outcomes. The findings reveal that overall learning performance is predicted by learners’ engagement, with this relationship mediated by interaction with peers and instructor and moderated by early engagement with course materials. Interestingly, early-engaging solvers—those prioritizing assignments over video viewing—demonstrate a stronger positive moderation effect on this link. These results provide new insights into how engagement patterns and interactions influence learning outcomes, offering practical implications for improving pedagogy in MOOCs and other online learning platforms.

Keywords: 

MOOC, E-learning, Interaction, Learning engagement, Early engagement learning outcome

Fatih Erdoğdu

Zonguldak Bülent Ecevit University, Türkiye // fatiherdogdu67@gmail.com

Mehmet Kara

Amasya University, Türkiye // m.kara@live.com

Seyfullah Gökoğlu

Bartın University, Türkiye // gokogluseyfullah@gmail.com

Serkan Telci

Zonguldak Bülent Ecevit University, Türkiye // serkantelci@beun.edu.tr

ABSTRACT: 

This study aims to investigate the trends and topics in Computer-Supported Collaborative Learning (CSCL) research papers from the emergence to the present. For this purpose, all documents on the Scopus database (5775 papers) were gathered and analyzed through bibliometric analysis and Latent Dirichlet Allocation (LDA) analysis, one of the topic modeling-based machine learning methods. The titles, abstracts, and keywords of the papers were included in the analysis. The bibliometric findings first indicated the annual distribution and subject areas in addition to the top productive sources, affiliations, countries, and authors of the CSCL papers from the emergence to the present. The findings from the LDA analysis revealed nine topics in CSCL. The high-density topics are (1) instructional design for CSCL, (2) designing learner groups and learning environments for CSCL activities, and (3) design and development for social constructivist learning. The medium-density topics are (4) developing social affordances in learning groups and CSCL environments, (5) constructivist approaches in CSCL contexts, and (6) social agency with CSCL technology. The low-density topics, focusing more on specific issues in CSCL, are (7) CSCL technologies, (8) online collaborative problem-solving, and (9) knowledge construction in CSCL. The publication accelerations of these topics were observed upward over time from the emergence of the field. These findings imply that instructional design, social issues, constructivism, and CSCL technologies are the common characteristics of the gathered topics, which makes many of them highly interrelated. The findings were discussed based on the relevant bibliometric and other review studies on CSCL.

Keywords: 

Topic modeling, Research trends, CSCL, Latent Dirichlet Allocation, Bibliometric

Zheng-Hong Guan

Institute of Education, National Yang Ming Chiao Tung University, Taiwan // a0935220867@gmail.com

Sunny S. J. Lin

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

Ying-Chih Chen

Institute of Education, National Yang Ming Chiao Tung University, Taiwan // janice1857@gmail.com

ABSTRACT: 

Information literacy is crucial in learning from multiple digital texts. Understanding when and how cognitive processes are taxed in developing information literacy is urgent. Previous research mainly used log data, think-aloud protocols, or note-taking to explore digital reading processes, but fine-grained cognitive processes need further investigation. This study combines eye-tracking technology, click times, and essay writing to examine in-depth multiple-text reading. Forty post-secondary novices read multiple history texts and wrote essays expressing their opinions. They read two topics—one familiar and one unfamiliar—and were instructed to write either an argument or a summary. Each topic had four texts connected through hyperlinks, including three paragraphs: background, source, and content. Eye-movement data revealed that during early reading, novices allocated attention to different paragraphs depending on the task instruction. For the familiar topic, the argument group selectively reread content paragraphs longer for integration, while the summary group evenly distributed rereading time across paragraphs. Both groups had more source-content back-and-forth saccade counts. The argument group had more click times for hyperlink selection than the summary group. In their essays, the argument group produced more text-based inferences and higher-quality writing for both topics. Conversely, the summary group demonstrated the poorest comprehension quality for the familiar topic. This study provides educators with guidance on selecting appropriate reading materials for diverse students. Educators may assign argumentative tasks for familiar topics to deepen comprehension, and summary tasks for unfamiliar topics to reduce cognitive load and support learning. These insights contribute to cultivating information literacy through multiple-text reading.

Keywords: 

21st-century abilities, Eye movement, Information literacy, Multiple-text reading, Teaching/learning strategies

Zehui Zhan

School of Information Technology in Education, South China Normal University, China // Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China // zhanzehui@m.scnu.edu.cn 

Xuanxuan Zou 

School of Information Technology in Education, South China Normal University, China // zxx200010@163.com

Guangwei Zhang

School of Information Technology in Education, South China Normal University, China // dreamgoway@qq.com

Qianyi Wu

Shenzhen Longhua Senior High School, China // 15521442392@163.com  

Jiajing Yao

Shenzhen Yantian Experimental Middle School, China // 350971224@qq.com

ABSTRACT: 

This study examined the effects of two performance assessment approaches on students’ learning achievement, attitude, computational thinking, and 4C key competencies in an introductory robotics course. An experimental study was conducted with 72 students in a middle school for 8 weeks. A performance assessment given at the end of the course was used as a reflection tool in one group (RPA), while given during the learning process as a guidance tool in another group (GPA). Results indicated that GPA showed a significantly better effect than RPA on students’ learning attitude and computational thinking, although the improvement in learning performance and 4C key competencies was not evident. Within the 4C sub-dimensions, GPA outperformed RPA in promoting students’ creativity and collaboration. However, the RPA group performed significantly better in improving critical thinking. The results confirmed that both GPA and RPA played an important role in robotics education. GPA provides a structured learning experience with clear goals and expectations, allowing students to effectively adjust their learning strategies through immediate feedback and a greater sense of control over their learning process with a positive attitude. In contrast, RPA encourages students to critically reflect on their learning journey from a holistic perspective, which increases student autonomy and deep learning during the process, while requiring a level of self-awareness and introspection that may be challenging for some students. The advantages and disadvantages of both approaches were discussed. The findings highlight the importance of adopting an appropriate approach to performance assessment and avoiding over-guidance in robotics courses.

Keywords: 

Performance assessment, Robotics course, Computational thinking, 4C key competencies  

Ting-Chia Hsu

Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taiwan // ckhsu@ntnu.edu.tw

Yi-Ting Lin

Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taiwan // i79432ytl@gmail.com

ABSTRACT: 

This study developed an artificial intelligence (AI) image recognition application integrated with a robot-based game-based learning (GBL) approach to enhance undergraduates’ understanding of computational thinking (CT) and AI concepts. The learning process was guided by experiential learning theory (ELT) or subject-based learning (SBL), combined with interactive gameplay. Sequential behavior pattern analysis and paired sample t-tests were conducted to evaluate the effects of ELT and SBL on learning outcomes, including improvements in learning achievement of CT and AI concepts, computer programming self-efficacy (CPSE), supervised machine learning self-efficacy (MLSE), and reductions in AI anxiety. Analysis of covariance (ANCOVA) and the Johnson-Neyman technique were further applied to compare differences in CPSE and MLSE between the ELT and SBL approaches. The research results show that both learning approaches can increase students’ CT and AI concepts in the robot GBL. The ELT is more suitable for students who initially have lower CT ability and lower self-efficacy. The stage of reflection, abstraction and operation process of ELT in implementing the AI application can enable students to generate discussion, cooperation, and direct manipulation behaviors. Those behavior patterns can enhance their CT and self-efficacy in particular for those who with low CPSE. Conversely, the SBL is more suitable for students who have advanced CT ability and high self-efficacy initially and this study revealed further discussion and suggestions for future studies.

Keywords: 

Computational thinking, Artificial intelligence anxiety, Game-based learning, Self-efficacy, Behavioral patterns

Chohui Lee

Ewha Womans University, South Korea // etdev97@ewha.ac.kr 

Hyo-Jeong So

Ewha Womans University, South Korea // hyojeongso@ewha.ac.kr

ABSTRACT: 

As STEM education evolves to meet the demands of the 21st century, fostering creativity and problem-solving skills has become a critical objective. This study examines how learners approach STEM problems requiring creative thinking with generative AI (GenAI). The research explores two key questions: (a) How do students engage in the creative problem solving (CPS) process using GenAI across social and scientific problem domains? (b) What differences exist between high- and low-performing students in using prompts during CPS with GenAI? The study involved 38 middle school students (aged 15) in Korea, who solved social and scientific problems from the PISA 2022 Creative Thinking Assessment using ChatGPT. The findings revealed distinct patterns in students’ use of GenAI, varying by problem domain and performance level. In the social problem domain, students primarily relied on ChatGPT’s suggestions, frequently using prompts to request fully developed solutions or elaboration on ChatGPT’s ideas. In contrast, in the scientific problem domain, students developed problem solving strategies around their ideas and integrated ChatGPT’s suggestions in a more balanced way. A detailed analysis of prompt strategies revealed prominent differences between high- and low-performing students. High-performing students used diverse prompt types and strategically combined them with their own insights. In contrast, low-performing students relied heavily on ChatGPT’s suggestions with minimal modification or originality. This study contributes to understanding how learners engage with GenAI for CPS across different domains and provides implications for designing educational strategies that equip learners to utilize GenAI effectively for CPS in STEM education.

Keywords: 

Creativity, Creative problem solving, STEM, Generative AI, PISA

Yuyue Zhang

Department of Educational Technology, Graduate School of Education, Peking University, China // persistbetter@stu.pku.edu.cn

Wen Zhou

Center for Educational Technology and Resource Development, Ministry of Education, China // zhouw@moe.edu.cn

Yu Liu

Education and Research Center, Huairou District, Beijing, China // liuyu1009@163.com

Jiyou Jia

Department of Educational Technology, Graduate School of Education, Peking University, China // jjy@pku.edu.cn

ABSTRACT: 

Currently, STEM education often emphasizes performance on final exams, which can undermine the development of students’ intrinsic motivation and long-term career interests. This focus on assessment results leads to a narrow approach that neglects the broader goals of engaging students in meaningful learning experiences. This study investigates the impact of an intelligent robotics-enabled STEM education project (IRESEP) on the interest levels of elementary school students in Beijing, involving 42 students (aged 10–12, from Grades 4 to 6) and a STEM teacher. The IRESEP was designed using effective teaching strategies such as phased teaching, blended learning, life-oriented teaching, and project-based learning. A mixed-methods approach was employed, combining quantitative and qualitative analyses to examine motivation mechanisms and influencing factors. Quantitative data were gathered through student questionnaires assessing basic information, STEM motivation performance, and influencing factors, analyzed using descriptive statistics, independent sample t-tests, cluster analysis, correlation analysis, and regression modeling. Qualitative insights were derived from interviews with both teachers and students. Results indicated that the IRESEP significantly enhanced students’ cognition, interest, and abilities in STEM, while also increasing their willingness to pursue STEM careers. Key factors influencing students’ STEM motivation included Teacher Support, Collaboration Skills, and Positive Academic Emotions. These findings suggest that intelligent robotics-based STEM projects should prioritize teacher support, collaborative learning, and positive educational experiences to effectively foster student motivation. The study offers a practical framework for designing engaging STEM programs that not only enhance student interest but also support their career aspirations in primary education.

Keywords: 

STEM education, Intelligent robotics-enabled STEM education project, STEM motivation, STEM career aspirations

Shurui Bai

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

Peiyao Tian

Faculty of Education, The University of Hong Kong, Hong Kong SAR, China // ptian@connect.hku.hk

ABSTRACT: 

This meta-analysis of educational robotics synthesizes existing empirical evidence to clarify its impact on students’ conceptual knowledge, applied skills, and learning attitude in science, technology, engineering, and mathematics subjects. Unlike previous meta-analyses, it encompasses all educational levels and programming and social robotics, analyzing various moderators like robot type and teaching strategy. All empirical peer-reviewed studies published from the database inception to 31 March 2024 are included, comprising 28 primary publications reporting 79 independent interventions. In summary, robotic-supported STEM education has a significant medium-sized effect on students’ conceptual knowledge (Hedges’ g = 0.636, p < .001, 95% CI: 0.385-0.888), applied skills (Hedges’ g = 0.663, p < .001, 95% CI: 0.335-0.991), and learning attitude (Hedges’ g = 0.422, p < .001, 95% CI: 0.227-0.616) compared with non-robotic-supported STEM education, per a random-effects model. We recommend three instructional strategies and robotic design based on moderator analyses. First, problem-based learning involving real-life challenges may help enhance knowledge mastery and problem-solving skills. Second, long-term, team-based programming robotics activities are recommended to foster a positive learning attitude. Third, educational robots should be user-friendly, age-appropriate, and capable of providing cognitive and emotional support.

Keywords: 

Robotics for education, STEM education, Meta-analysis, Conceptual knowledge

Hui-Chun Hung

Graduate Institute of Network Learning Technology, National Central University, Taoyuan City, Taiwan // hch@cl.ncu.edu.tw

Pei-Jhou Lai

Graduate Institute of Network Learning Technology, National Central University, Taoyuan City, Taiwan // teencentury21@gmail.com

ABSTRACT: 

Incorporating intelligent robotics into STEM education enhances students’ knowledge proficiency and higher-order thinking. In the era of big data, students must possess strong data literacy and visualization skills. However, students often struggle to understand and apply data effectively. This study developed an Intelligent Inquiry-Based Learning Companion System utilizing Generative AI (GenAI) to address these challenges and improve data visualization literacy among graduate students. The study involved 53 graduate students from a university in northern Taiwan, enrolled in an 18-week course. The experimental group used the Intelligent Inquiry-Based Learning Companion System, while the control group used a general ChatGPT for their interactions. The students interacted with an AI, which supported their inquiry-based learning through interactive conversations. The results indicate that students become more confident in interpreting complex material sets after using the Intelligent Inquiry-Based Learning Companion System. Students also find it easier to share their findings with classmates. The inquiry-based learning companion system enhances organizational strategies and critical thinking, although it has a limited impact on metacognitive self-regulation, peer learning, and help-seeking behaviors. The system completed the coursework, although some users reported issues with more complicated, open-ended tasks that required further assistance from humans to complete them properly. It enabled students to dive into a more specific set of questions. In addition, students mentioned that immediate, customized responses and dashboards can enrich their learning experience.

Keywords: 

Data literacy, Inquiry-based learning, Generative artificial intelligence, Learning companion

Yingxiao Qian

University of South Carolina, United States // yingxiao@mailbox.sc.edu

Ikseon Choi

Emory University, United States // ike.choi@emory.edu

ABSTRACT: 

Developing students’ computational thinking has emerged as a crucial objective across K-12 curricula nationwide. Particularly, abstraction is the cornerstone of computational thinking, but endeavors of cultivating students’ abstract thinking have not yielded significant results. The purpose of this study was to examine the effectiveness of the Explicit Guidance and Practices on Abstraction (EGPA) on fostering 5th graders’ abstraction in a STEM-integrated robotics curriculum. By tapping into the cognitive root of abstraction, EGPA refers to a series of theory-informed strategies and instructional practices focused on students’ abstraction. A convergent mixed methods study was conducted to collect quantitative and qualitative data about K-5 students’ abstraction and their experience with the EGPA. The findings reported K-5 students attending the STEM-integrated robotics curriculum with EGPA integrated had statistically significant improvements on their abstraction. Qualitative findings provided in-depth insights about students’ experiences with the EGPA. The findings from both sources of data were converged to describe the effectiveness of EGPA on K-5 students’ abstraction. Practical implications are offered for professionals investing in STEM education.

Keywords: 

Abstraction, Computational thinking, STEM education, Explicit guidance and practices on abstraction (EGPA), STEM-integrative robotics curriculum

Yuru Lin

Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China // linyuru@mails.ccnu.edu.cn

Yuqin Yang

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

Yi Zhang

Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China // zhangyi@mail.ccnu.edu.cn

ABSTRACT: 

Scaffolding type and timing are key factors in designing strategies to develop students’ computational thinking (CT). Although these factors have been studied separately, their combined effects remain underexplored. To address this gap, a 2×2 factorial experiment was conducted in this study to examine the effect of scaffolding type (conceptual versus problem-solving) and timing (immediate versus delayed) on CT concepts, practices, and perspectives. A total of 136 third graders from four classes were assigned to four experimental conditions: delayed conceptual scaffolding, immediate conceptual scaffolding, immediate problem-solving scaffolding, and delayed problem-solving scaffolding. The study was conducted over 6 weeks using the machine learning unit of an artificial intelligence (AI) course. The results, as measured by knowledge tests and final worksheets, showed that problem-solving scaffolding and immediate scaffolding were more effective in developing students’ CT concepts than conceptual scaffolding and delayed scaffolding. Lag sequential analysis indicated that students guided by problem-solving scaffolding and delayed scaffolding developed more complete problem-solving paths and achieved higher levels of CT practice. Additionally, an analysis of students’ learning reflections revealed that most students could explain the functions of machine learning techniques. However, students guided by problem-solving scaffolding and immediate scaffolding were better at understanding and reflecting on the problem-solving process. These findings provide valuable insights for researchers and practical guidance for teachers. By highlighting effective scaffolding methods and their timing, this study advances CT education and helps prepare students for a technology-driven world.

Keywords: 

Computational thinking, Scaffolding, Artificial intelligence, Timing of scaffolding, Elementary education

Zemin Guo

Faculty of Education, The University of Hong Kong, Hong Kong SAR, China // zeminguo@connect.hku.hk

Peiyao Tian

Faculty of Education, The University of Hong Kong, Hong Kong SAR, China // ptian@connect.hku.hk

Muhammad Ali

Faculty of Education, The University of Hong Kong, Hong Kong SAR, China // akula@connect.hku.hk

Ka Wai Gary Wong

Faculty of Education, The University of Hong Kong, Hong Kong SAR, China // wongkwg@hku.hk

ABSTRACT: 

Self-efficacy in teaching with educational robotics (SETER) plays a critical role in shaping STEAM teachers’ instructional behaviors and, consequently, in enriching students’ learning experiences. Despite its importance, research on effective strategies to enhance STEAM teachers’ SETER and how participants’ demographics affect SETER remains limited. This study employed a single-group mixed-method research design to examine and explain changes in SETER among 51 STEAM trainee teachers who participated in a three-week Collaborative Lesson Designing (CLD) training program incorporating educational robotics (ER). Data were collected through the SETER questionnaire, reflective writings, open-ended survey, and collaboratively developed lesson plans. Quantitative findings, derived from paired-sample t-tests and MANCOVA, revealed a statistically significant improvement in SETER following the CLD intervention, with female participants exhibiting notably greater gains than their male counterparts. Qualitative analysis further identified three areas through which CLD influenced SETER: enhanced capacity for authentic learning design, assessment planning, and outcome alignment; challenges in addressing students’ prior knowledge and learning disabilities; and the observed impact of CLD performance on self-efficacy development. These findings suggest that CLD serves as an effective pedagogical strategy for fostering SETER in STEAM teacher education. The study contributes to a growing body of literature on teacher professional development and offers implications for designing future programs that integrate CLD with differentiated instruction and practical implementation. Longitudinal research is recommended to examine sustained impacts on teaching practice.

Keywords: 

Collaborative lesson designing, Educational robotics, Self-efficacy, STEAM teacher training

Wen-Song Su

Department of Dentistry, Tri-Service General Hospital, Taipei, Taiwan // Department of Dentistry, Armed Forces General Hospital, Taoyuan City, Taiwan // doc6131f.edu@gmail.com

Gwo-Jen Hwang

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

Chengjiu Yin

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

Ching-Yi Chang

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

ABSTRACT: 

In the 21st century, STEM (Science, Technology, Engineering, and Mathematics) education plays a critical interdisciplinary role in cultivating students’ seven core competencies (7C) learning performance: computational thinking, collaboration, communication, problem-solving, metacognitive awareness, creative thinking, and critical thinking. While these competencies are essential for future-ready learners, limited research has quantitatively examined how learner characteristics, such as gender, education level, learning attitudes, and learning satisfaction, affect students’ 7C learning performance in robot-integrated STEM education. This study adopted a quantitative research design involving 67 students from healthcare-related programs who participated in a robot-integrated STEM learning activity. Data were collected through validated instruments measuring learning attitudes, satisfaction, and 7C learning performance. Statistical analyses, including t tests, one-way ANOVA, Pearson correlation, and multiple regression, were used to examine group differences and predictive effects. The findings revealed no significant gender differences in perceived learning needs. However, students with higher education levels demonstrated stronger development in 7C learning performance. In addition, learning attitudes and learning satisfaction significantly predicted students’ 7C learning performance, and the seven competencies were strongly intercorrelated. Based on these findings, the study proposes strategies for enhancing robot-integrated STEM and programming education, particularly within healthcare-related contexts, through interdisciplinary curriculum design. These insights provide guidance for educators, curriculum designers, and policymakers aiming to foster holistic, competency-based STEM education.

Keywords: 

21st century core competencies, Higher order thinking, STEM education, Robot-assisted learning, Experiential learning theory