April 2023, Volume 26, Issue 2

Special Issue on "Creating Computational Thinkers for the Artificial Intelligence Era—Catalyzing the Process through Educational Technology

Guest Editor(s):  Ahmed Tlili, Daniel Burgos and Chee-Kit Looi  

Editorial Position Paper

Gwo-Jen Hwang

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

Nian-Shing Chen

Institute for Research Excellence in Learning Sciences, Program of Learning Sciences, National Taiwan Normal University, Taiwan // nianshing@gmail.com


Generative artificial intelligence (GAI) applications, such as ChatGPT (Chat Generative Pre-trained Transformer) and Midjourney, have recently attracted much attention from researchers and school teachers. While many people are eager to learn more about GAI applications, some scholars are concerned about the potential misuse of them. It is predicted that the use of GAI applications will increase rapidly in the coming years. Therefore, it is important to consider the challenges and research issues through some concrete application examples of using GAI for education. In this position paper, the authors aim to address these issues from the perspectives of academic research and educational objectives. Along with defining GAI, several illustrative examples of using GAI applications in educational settings are provided. Moreover, potential research issues of GAI-based learning, including research design, relevant learning strategies, research focus, and measuring tools, are discussed. ET&S journal is especially welcoming research on unlocking the potential of GAI for education to realize the two notions of “Knowing [why] is the essential element for learners to have in-depth understanding” and “It is all about prompts: Get rid of the search mindset and use ‘programming prompt’ instead.”


Generative artificial intelligence, ChatGPT, Midjourney, Artificial Intelligence in education, Programming prompt

Cite as:Hwang, G.-J., & Chen, N.-S. (2023). Editorial Position Paper: Exploring the Potential of Generative Artificial Intelligence in Education: Applications, Challenges, and Future Research Directions. Educational Technology & Society, 26(2), I-XVIII. https://doi.org/10.30191/ETS.202304_26(2).0014
Published April 9, 2023

Full Length Articles

Yin Zhang

Department of Education, Ocean University of China, Qingdao, P. R. China // zhangyinouc@sina.com

Samuel Kai Wah Chu

Faculty of Education, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, P. R. China // samchu@hku.hk

Yonghui Liu

College of Engineering, Ocean University of China, Qingdao, P. R. China // liuyonghui@ouc.edu.cn

Xiaoli Lu

School of Mathematical Sciences & Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai, P. R. China // xllu@math.ecnu.edu.cn


Previous research has looked into educational approaches to prevent plagiarism in academic writing, yielding insights into how plagiarism can be avoided. However, plagiarism remains a major problem in the education sector. We designed a training module that includes a customised Online Scaffolding Writing System (OSWS) to help faculty teach undergraduates how to avoid committing plagiarism in their academic writing. A quasi-experimental design was used to analyse the plagiarism-related perceptions and behavioural changes of 121 undergraduate students and to test the effects of the new module on students’ academic writing. The experimental group performed significantly better than the control group in terms of decreasing the extent of plagiarism in their writing (with a mean decrease from a moderate to minor level of plagiarism), and improving their writing quality (with a mean increase of 18 percentage points in writing scores). Furthermore, more than 95% of the students in the experimental group and their instructor reported that they valued the benefits of adopting the training module in class, and almost 90% of them expressed high levels of satisfaction with the learning they had obtained from the OSWS. This study also provides insights into how the new training module can be implemented across disciplines.


Plagiarism, Hybrid training, Academic writing, Online Scaffolding Writing System (OSWS)

Cite as:Zhang, Y., Chu, S. K W., Liu, Y., & Lu, X. (2023). Effects of a Hybrid Training for Plagiarism Prevention Module on Plagiarism-free Academic Writing in Higher Education. Educational Technology & Society, 26(2), 1-18. https://doi.org/10.30191/ETS.202304_26(2).0001
Submitted February 5, 2022; Revised July 19, 2022; Accepted August 18, 2022; Published September 26, 2022

Ersin Kara

Middle East Technical University, Turkey // ersinkara07@gmail.com 

Kursat Cagiltay

Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey // The Digital Economy Research Center, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan // cagiltay@gmail.com


This paper reports on the design and development of educational games and materials that utilize affordable e-textile technology. The researchers employed a design-based approach whereby preschool children used three e-textile materials in two cycles to inform on the development of interactive materials from ordinary objects and bodily interactive games. The study’s data were collected and analyzed according to the design-based research framework through iterative cycles of interviewing, video recording, and note-taking. The paper describes the characteristics, pros, and cons of e-textiles and what to consider when using them to create interactive educational materials for preschool-aged children.


E-textile, Wearable technology, Preschool education, Design-based research (DBR), Executive functions

Cite as:Kara, E., & Cagiltay, K. (2023). Using E-textiles to Design and Develop Educational Games for Preschool-aged Children. Educational Technology & Society, 26(2), 19-35. https://doi.org/10.30191/ETS.202304_26(2).0002
Submitted November 28, 2021; Revised August 28, 2022; Accepted September 19, 2022; Published September 26, 2022

Rui Li

School of Foreign Languages, Hunan University, Hunan, China // liruidianzi@hotmail.com


Although an increasing number of studies have focused on the use of mobile-assisted language learning (MALL) for English as a foreign language (EFL) learners’ listening skill development, there is a lack of comprehensive meta-analysis regarding the effect sizes of these studies. To fill the gap, 20 selected experimental studies involving 1218 participants were included for a meta-analysis based on the proposed inclusion and exclusion criteria. Results showed that the overall effect size was moderate-to-large, g = 0.792, 95% CI [0.536, 1.047], suggesting that MALL for EFL learners’ listening skill development is more effective than traditional methods. Regarding moderators for the overall effect, different moderator effects of educational levels, software types, control conditions, intervention settings, measured outcome types and intervention durations were reported. Specifically, educational levels were found to be a significant moderator, while software types, control conditions, intervention settings, measured outcome types and intervention durations were not the significant moderators. The implications for practice were discussed as well.


English as a foreign language (EFL), Evidence-based applied linguistics (EBAL), Listening skill, Meta-analysis, Mobile-assisted language learning (MALL)

Cite as:Li, R. (2023). Effects of Mobile-Assisted Language Learning on EFL Learners’ Listening Skill Development. Educational Technology & Society, 26(2), 36-49. https://doi.org/10.30191/ETS.202304_26(2).0003
Submitted March 24, 2022; Revised August 16, 2022; Accepted August 18, 2022; Published September 26, 2022

Tingting Wang

Department of Educational and Counselling Psychology, McGill University, Canada // tingting.wang4@mail.mcgill.ca

Shan Li

Lehigh University, United States // shla22@lehigh.edu 

Susanne Lajoie

Department of Educational and Counselling Psychology, McGill University, Canada // susanne.lajoie@mcgill.ca 


Cognitive load can be induced by both learning tasks and self-regulated learning (SRL) activities, which compete for limited working memory capacity. However, there is little research on the relationship between cognitive load and SRL. This study explored how cognitive load interplayed with SRL behaviors and their joint effects on task performance (i.e., diagnostic efficiency) in the context of clinical reasoning. Specifically, twenty-seven (N = 27) medical students diagnosed three virtual patient cases in BioWorld, a simulation-based learning environment to improve medical students’ clinical reasoning skills. Students’ SRL behaviors were automatically recorded in BioWorld log files as they accomplished the tasks. We employed text mining techniques to extract four linguistic features from students’ concurrent think-aloud, i.e., cognitive discrepancy, insight, causation, and positive emotions, which were further used to represent students’ cognitive load. The latent profile analysis was then performed to cluster students into high- and low-load group. We also conducted a path analysis to investigate the mediation roles of SRL behaviors in the relationship between cognitive load and diagnostic efficiency (task performance). The results revealed that cognitive load negatively affected diagnostic efficiency, mediated by the ratio of SRL behaviors in the self-reflection phase. This study provides theoretical and methodological insights regarding the measurement of cognitive load and its interplay with SRL. This study informs the design of effective interventions for managing cognitive load in SRL within intelligent tutoring systems. 


Cognitive load, Self-regulated learning, Technology-rich learning environment, Text mining 

Cite as:Wang, T., Li, S., & Lajoie, S. P. (2023). The Interplay Between Cognitive Load and Self-Regulated Learning in a Technology-Rich Learning Environment. Educational Technology & Society, 26(2), 50-62. https://doi.org/10.30191/ETS.202304_26(2).0004
Submitted January 19, 2022; Revised July 9, 2022; Accepted August 12, 2022; Published September 28, 2022

Christopher C.Y. Yang

Graduate School of Informatics, Kyoto University, Japan // yang.yuan.57e@st.kyoto-u.ac.jp

Hiroaki Ogata

Academic Center for Computing and Media Studies, Kyoto University, Japan // ogata.hiroaki.3e@kyoto-u.ac.jp


Blended learning (BL) is regarded as an effective strategy for combining traditional face-to-face classroom activities with various types of online learning tools (e.g., e-books). An effective feature of e-books is the ability to use digital notes. When e-books are used in BL, the strategic adoption of note-taking provides benefits that influence the learners’ progress for self-regulated learning (SRL) and course achievements. However, learners tend to be unsure about how note-taking is performed using online learning materials and lack knowledge of effective strategies for SRL. Furthermore, few studies have investigated blended learners’ sequential patterns of e-book note-taking for SRL. Thus, in this paper, an exploratory study was conducted in an undergraduate course that implemented the BL design. The learning task for the blended learners in the present study was to study the learning material using BookRoll, an e-book system, during in-class and out-of-class learning sessions. Lag sequential analysis of the e-book learning behavior data was conducted to identify the blended learners’ sequential behaviors of e-book note-taking for the cognitive strategy use of SRL. Moreover, the difference between higher- and lower-achievement blended learners in terms of their sequential behaviors of e-book note-taking for SRL was revealed. This study can help educators provide evidence-based educational feedback to learners regarding the identified sequential patterns of e-book note-taking that can be applied as effective strategies for promoting the cognitive strategy use of SRL and improvement of course achievement in BL.


Lag sequential analysis, Sequential pattern, Note-taking, Blended learning, Self-regulated learning

Cite as:Yang, C. C. Y., & Ogata, H. (2023). Lag Sequential Analysis for Identifying Blended Learners’ Sequential Patterns of e-Book Note-taking for Self-Regulated Learning. Educational Technology & Society, 26(2), 63-75. https://doi.org/10.30191/ETS.202304_26(2).0005
Submitted April 18, 2022; Revised August 23, 2022; Accepted September 17, 2022; Published September 28, 2022

Liang-Yi Li

Program of Learning Sciences, Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taiwan // lihenry12345@ntnu.edu.tw 

Wen-Lung Huang

Department of Communication, Fo Guang University, Yilan, Taiwan // wlhuang@mail.fgu.edu.tw


With the increasing bandwidth, videos have been gradually used as submissions for online peer assessment activities. However, their transient nature imposes a high cognitive load on students, particularly low-ability students. Therefore, reviewers’ ability is a key factor that may affect the reviewing process and performance in an online video peer assessment activity. This study examined how reviewers’ ability affected the comments they provided and their reviewing behaviors and performance. Thirty-eight first-year undergraduate students participated in an online video peer assessment activity for 3 weeks. This study analyzed data collected from the teacher’s and peer reviewers’ ratings, comments provided by peer reviewers, and system logs. Several findings are significant. First, low-ability reviewers preferred to rate higher scores than high-ability reviewers did. Second, low-ability reviewers had higher review errors than high-ability reviewers. Third, high-ability reviewers provided more high-level comments, while low-ability reviewers provided more low-level comments. Finally, low- and high-ability reviewers showed different behavior patterns when reviewing peers’ videos. In particular, low-ability reviewers invested more time and effort in understanding video content, while high-ability reviewers invested more time and effort in detecting and diagnosing problems. These findings are discussed, and several suggestions for improving the instructional and system design of online video peer assessment activities are provided.


Video peer assessment, Learning analytics, Comments provided, Behavior pattern 

Cite as:Li, L.-Y., & Huang, W.-L. (2023). Effects of Undergraduate Student Reviewers’ Ability on Comments Provided, Reviewing Behavior, and Performance in an Online Video Peer Assessment Activity. Educational Technology & Society, 26(2), 76-93. https://doi.org/10.30191/ETS.202304_26(2).0006
Submitted January 28, 2022; Revised August 16, 2022; Accepted Aug 29, 2022; Published September 28, 2022

Special Issue Articles

Ahmed Tlili

Smart Learning Institute of Beijing Normal University, China // ahmed.tlili23@yahoo.com 

Daniel Burgos

UNIR iTED, Universidad Internacional de La Rioja (UNIR), Spain // daniel.burgos@unir.net

Chee-Kit Looi

National Institute of Education, Nanyang Technological University, Singapore // cheekit.looi@outlook.com


There is an ongoing debate in the literature about the ways of using technology to enhance students’ Computational Thinking (CT). This special issue further enriches this debate by investigating how educational technology could be used, and for which purposes, to facilitate learning CT. It includes six papers demonstrating the innovative design of curricula and the use of various technologies to teach CT for students in different educational levels. Based on these papers, this special issue points out that more research is needed to investigate the best educational practices that could be used to teach CT rather than focusing on the technology itself. It also reveals that future work could cover smart learning analytics and precision education to better model students’ individual differences, hence effectively supporting learning CT.


Computational thinking, Artificial Intelligence, Educational technology, Future education, Competencies 

Cite as:Tlili, A., Burgos, D., & Looi, C.-K. (2023). Guest Editorial: Creating Computational Thinkers for the Artificial Intelligence Era— Catalyzing the Process through Educational Technology. Educational Technology & Society, 26(2), 94-98. https://doi.org/10.30191/ETS.202304_26(2).0007
Published March 14, 2023

Panagiotis Kampylis, Valentina Dagienė, Stefania Bocconi, Augusto Chioccariello, Katja Engelhardt, Gabrielė Stupurienė, Vaida Masiulionytė-Dagienė, Eglė Jasutė, Chiara Malagoli, Milena Horvath and Jeffrey Earp

Panagiotis Kampylis

National Research Council, Italy // panagiotis.kampylis@itd.cnr.it 

Valentina Dagienė

Vilnius University, Lithuania // valentina.dagiene@mif.vu.lt 

Stefania Bocconi

National Research Council, Italy // stefania.bocconi@itd.cnr.it

Augusto Chioccariello

National Research Council, Italy // augusto@itd.cnr.it 

Katja Engelhardt

European Schoolnet, Belgium //  katja.engelhardt@outlook.com

Gabrielė Stupurienė

Vilnius University, Lithuania // gabriele.stupuriene@mif.vu.lt

Vaida Masiulionytė-Dagienė

Vilnius University, Lithuania // vaida.masiulionyte-dagiene@mif.vu.lt

Eglė Jasutė

Vilnius University, Lithuania // egle.jasute@fsf.vu.lt

Chiara Malagoli

National Research Council, Italy // chiara.malagoli@itd.cnr.it

Milena Horvath

European Schoolnet, Belgium // milena.horvath@eun.org

Jeffrey Earp

National Research Council, Italy // jeffrey.earp@itd.cnr.it


In recent years, many countries have introduced Computational Thinking (CT) concepts into compulsory education as part of general curriculum reform efforts. A systematic review of academic and grey literature has been conducted to analyse the state of the art in implementing CT in primary and secondary education. In total, 1977 publications were identified, out of which 98 met the inclusion criteria for the review. The results show that, despite a lack of consensus on a common definition, a core set of key CT skills is addressed in primary and lower secondary education. Implementation approaches that emerged from the analysis are discussed and presented according to the European Commission’s Joint Research Centre (2016) classification: (i) embedding CT across the curriculum as a transversal theme/skill set; (ii) integrating CT as a separate subject; and (iii) incorporating CT skills within other subjects such as Mathematics and Technology. New approaches to formative assessment of CT are emerging, reflecting different conceptualisations and differences in contextual and motivational aspects of CT curriculum integration. However, further investigation is needed to understand better how gender/equity/inclusion issues impact the quality of computing education integration. 


Computational thinking, Computer Science education, Compulsory education, CT skills 

Cite as:Kampylis, P., Dagienė, V., Bocconi, S., Chioccariello, A., Engelhardt, K., Stupurienė, G., Masiulionytė-Dagienė, V., Jasutė, E., Malagoli, C., Horvath, M., & Earp, J. (2023). Integrating Computational Thinking into Primary and Lower Secondary Education: A Systematic Review. Educational Technology & Society, 26(2), 99-117. https://doi.org/10.30191/ETS.202304_26(2).0008
Published March 14, 2023

Emily Relkin, Sara K. Johnson and Marina U. Bers

Emily Relkin

Center for Children and Technology, Education Development Center, USA // erelkin@edc.org

Sara K. Johnson

Eliot-Pearson Department of Child Study and Human Development, Tufts University, USA // s.johnson@tufts.edu

Marina U. Bers

Lynch School of Education and Human Development, Boston College, USA // marina.bers@bc.edu


TechCheck is an assessment of Computational Thinking (CT) for early elementary school children consisting of fifteen developmentally appropriate unplugged challenges that probe six CT domains. The first version of TechCheck showed good psychometric properties as well as ease of administration and scoring in a validation cohort of 768 children between 5 and 9 years of age. To increase sensitivity and reduce possible ceiling and floor effects, grade-specific versions of TechCheck (K, 1, 2) were subsequently created. In the present study, we explored how CT skills could be compared across grades when grade-specific versions of TechCheck are administered. First, we examined TechCheck raw score distributions and responses within CT domains in a representative sample of students from the three grades. Grade-specific Z-scores and percentile rankings were then calculated. To show utility of this normalization system, we used percentiles to compare CT outcomes between first and second graders who participated in a ScratchJr coding educational intervention. While TechCheck change scores suggested an unexpected 42.74% difference in CT outcomes between first and second grade, application of the normative scoring system indicated a more plausible 5.17 percentile rank difference between grades. Normative analysis may provide a more meaningful way to compare results across grades when grade-specific versions of TechCheck are used. Implications for the future use of the TechCheck CT assessments are discussed.


Assessment, Computer science, Early childhood, Coding

Cite as:Relkin, E., Johnson, S. K., & Bers, M. U. (2023). A Normative Analysis of the TechCheck Computational Thinking Assessment. Educational Technology & Society, 26(2), 118-130. https://doi.org/10.30191/ETS.202304_26(2).0009
Published March 14, 2023

Zhihao Cui

Department of Curriculum and Instruction, The Chinese University of Hong Kong, Hong Kong SAR, China // cuizhihao@link.cuhk.edu.hk 

Oi-lam Ng

Department of Curriculum and Instruction, The Chinese University of Hong Kong, Hong Kong SAR, China // oilamn@cuhk.edu.hk 

Morris Siu-Yung Jong

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


Grounded in problem-based learning and with respect to four mathematics domains (arithmetic, random events and counting, number theory, and geometry), we designed a series of programming-based learning tasks for middle school students to co-develop computational thinking (CT) and corresponding mathematical thinking. Various CT concepts and practices articulating the designated mathematical problems were involved in the tasks. In addition to delineating the design of these learning tasks, this paper presents a qualitative study in which we examined 74 students’ learning outcomes and characterized their CT and mathematical thinking co-development as they accomplished the tasks. The research results demonstrate the co-development of both mathematics- and CT-related concepts and practices in the four mathematics domains. Two types of interactions are identified: (i) applying mathematical knowledge to construct CT artifacts and (ii) generating new mathematical knowledge with CT practice. The new insights provided by the present work are threefold. First, from a mathematical learning perspective, the nature of the solution processes of the designed problems should not be immediately obvious. Second, from a technology-enhanced learning perspective, the dynamic representations and immediate visual feedback afforded by the programming tool are beneficial to student learning. Third, from a pedagogical perspective, the room for customization offered by both the designed problems and programming tools can provide affordances for learning.


Computational thinking, Mathematics education, Problem-based learning, Problem solving, STEM

Cite as:Cui, Z., Ng, O., & Jong, M. S.-Y. (2023). Integration of Computational Thinking with Mathematical Problem-based Learning: Insights on Affordances for Learning. Educational Technology & Society, 26(2), 131-146. https://doi.org/10.30191/ETS.202304_26(2).0010
Published March 20, 2023

Yeonju Jang

Dept. of Computer Science and Engineering, The Graduate School of Korea University, Republic of Korea // spring0425@korea.ac.kr

Seongyune Choi

Dept. of Computer Science and Engineering, The Graduate School of Korea University, Republic of Korea // csyun213@korea.ac.kr

Seonghun Kim

Dept. of Education, Gachon University, Republic of Korea // ryankim@gachon.ac.kr 

Hyeoncheol Kim

Dept. of Computer Science and Engineering, Korea University, Republic of Korea // harrykim@korea.ac.kr


Given the importance of digital technology in daily life, computational thinking (CT) has become a necessary skill for everyone, not just for computer scientists. For CT development, students need to receive appropriate social learning support. However, instructors find it difficult to provide such support to many students in online courses. This study aimed to examine the effectiveness of e-mentoring via social network services (SNS) in developing students’ CT during large-scale online courses. A total of 327 undergraduate students volunteered to participate in this study, which included 16 weeks of lectures aimed at developing CT. The effects of SNS-based e-mentoring on CT development, the influences of each e-mentoring activity on CT development, and gender differences were analyzed using data on participants’ CT assistance scores and their utilization of e-mentoring activities. The findings indicated that SNS-based e-mentoring was effective in developing the CT of undergraduate students’ engagement in a large-scale online course. The most influential e-mentoring activities for students’ CT development were informational and technical support in a group and informational support in a private environment. Female students benefited more from SNS-based e-mentoring than male students, and they also engaged in more types of e-mentoring activities than male students. Participation in SNS-based e-mentoring was found to lower the gap in CT between students with and without prior learning experience. Our study findings can be used by educational institutions and instructors when designing courses for students’ CT development in large-scale online courses or when developing strategies to close the gender gap in CT ability.


Computational thinking, e-Mentoring, Social Network Service (SNS), Gender difference, Computational thinking and prior learning experience

Cite as:Jang, Y., Choi, S., Kim, S., & Kim, H. (2023). The SNS-based E-mentoring and Development of Computational Thinking for Undergraduate Students in an Online Course. Educational Technology & Society, 26(2), 147-164. https://doi.org/10.30191/ETS.202304_26(2).0011
Published March 20, 2023

Xiao-Fan Lin, Jing Wang, Yingshan Chen, Yue Zhou, Guoyu Luo, Zhaoyang Wang, Zhong-Mei Liang, Xiaoyong Hu and Wenyi Li

Xiao-Fan Lin

Guangdong Provincial Philosophy and Social Sciences Key Laboratory of Artificial Intelligence and Smart Education, Guangdong Engineering Technology Research Center of Smart Learning, South China Normal University, Guangzhou, P.R. China // Guangdong Provincial Institute of Elementary Education and Information Technology, Guangzhou, P.R. China // School of Education Information Technology, South China Normal University, Guangzhou, P.R. China // linxiaofan@m.scnu.edu.cn

Jing Wang

School of Education Information Technology, South China Normal University, Guangzhou, P.R. China // Teacher Education College of Guangdong-Hong Kong-Macao Greater Bay Area, South China Normal University, Guangzhou, P.R. China // jjw15683078248@163.com

Yingshan Chen

School of Education Information Technology, South China Normal University, Guangzhou, P.R. China // 15107152939@163.com 

Yue Zhou

School of Education Information Technology, South China Normal University, Guangzhou, P.R. China // 2021020874@m.scnu.edu.cn 

Guoyu Luo

School of Education Information Technology, South China Normal University, Guangzhou, P.R. China // 2475317832@qq.com 

Zhaoyang Wang

School of Education Information Technology, South China Normal University, Guangzhou, P.R. China // m17754831067@163.com 

Zhong-Mei Liang

Zhixin South Road Primary School, Guangzhou, P.R. China // 351632147@qq.com

Xiaoyong Hu

School of Education Information Technology, South China Normal University, Guangzhou, P.R. China // Institute of Artificial Intelligence in Education, South China Normal University, Guangzhou, P.R. China // 472275060@qq.com

Wenyi Li

Guangdong Provincial Institute of Elementary Education and Information Technology, Guangzhou, P.R. China // liwenyi@pku.edu.cn 


Computational thinking (CT) is an imperative competency in the 21st century. Mindtools can assist students in understanding concepts and decomposing tasks during CT development through programming. However, students may encounter challenges in complex CT problem-solving tasks due to being confused when using mindtools without proper guidance. Research evidence shows the potential of reflection in complex CT problem-solving by regulating cognitive activities. Accordingly, this study designed a reflection-guided visualized mindtool strategy to address CT development challenges. A quasi-experiment and lag sequential analysis were conducted by recruiting 97 junior high school students to examine the effects of the proposed strategy on CT development and to explore students’ behavior patterns. Results revealed that the proposed approach improved students’ CT performance, CT disposition, meta-cognitive awareness, and learning motivation. Students learning with the proposed strategy exhibited more key behaviors of facilitating CT problem-solving (e.g., generalizing the knowledge, re-designing the algorithm scheme, and evaluating the feasibility of their proposed schemes) than students in the control group, revealing the essential process of CT development and enlightening teachers on guiding students to produce such learning processes when cultivating CT.


 Reflection, Mindtool, Computational thinking, Behavior, Junior high school students

Cite as:Lin, X.-F., Wang, J., Chen, Y., Zhou, Y., Luo, G., Wang, Z., Liang, Z.-M., Hu, X., & Li, W. (2023). Effect of a Reflection-Guided Visualized Mindtool Strategy for Improving Students’ Learning Performance and Behaviors in Computational Thinking Development. Educational Technology & Society, 26(2), 165-180. https://doi.org/10.30191/ETS.202304_26(2).0012
Published March 20, 2023

Zhichun Liu

Human Communication, Development, and Information Sciences, The University of Hong Kong, Hong Kong SAR, China // liulukas91@gmail.com 

Jewoong Moon

Department of Department of Educational Leadership, Policy, & Technology Studies, University of Alabama, Tuscaloosa, AL, USA // jmoon19@ua.edu


In this study, we have proposed and implemented a sequential data analytics (SDA)-driven methodological framework to design adaptivity for digital game-based learning (DGBL). The goal of this framework is to facilitate children’s personalized learning experiences for K–5 computing education. Although DGBL experiences can be beneficial, young children need personalized learning support because they are likely to experience cognitive challenges in computational thinking (CT) development and learning transfer. We implemented the educational game Penguin Go to test our methodological framework to detect children’s optimal learning interaction patterns. Specifically, using SDA, we identified children’s diverse gameplay patterns and inferred their learning states related to CT. To better understand children’s gameplay performance and CT development in context, we used qualitative data as triangulation. We discuss adaptivity design based on the children’s gameplay challenges indicated by their gameplay sequence patterns. This study shows that SDA can inform what in-game support is necessary to foster student learning and when to deliver such support in gameplay. The study findings suggest design guidelines regarding the integration of the proposed SDA framework.


Digital game-based learning, Computational thinking, Sequential data analytics, Adaptivity, Personalized learning

Cite as:Liu, Z., & Moon, J. (2023). A Framework for Applying Sequential Data Analytics to Design Personalized Digital Game-Based Learning for Computing Education. Educational Technology & Society, 26(2), 181-197. https://doi.org/10.30191/ETS.202304_26(2).0013
Published March 20, 2023

Starting from Volume 17 Issue 4, all published articles of the journal of Educational Technology & Society are available under Creative Commons CC-BY-ND-NC 3.0 license.