Si Zhang

Hubei Key Laboratory of Digital Education, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China // djzhangsi@mail.ccnu.edu.cn

Qian Yang

Hubei Key Laboratory of Digital Education, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China // yqian@mails.ccnu.edu.cn

Honghui Li

Hubei Key Laboratory of Digital Education, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China // edulihonghui@mails.ccnu.edu.cn

Chaowang Shang

Hubei Key Laboratory of Digital Education, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China // scw@mail.ccnu.edu.cn

ABSTRACT: 

To address issues such as insufficient depth and low efficiency in online discussions within computer-supported collaborative learning (CSCL), this study aims to enhance regulated learning by integrating group awareness tools (GATs) with collaborative reflection scripts, thereby alleviating these challenges. A total of 33 student teachers participated in the experiment. Epistemic network analysis (ENA) was used to analyze group discourse data, investigating differences in regulated learning behaviors among student teachers with high- and low-self-regulation (SR) levels under the support of the GAT integrated with a collaborative reflection script, as well as the changes in regulated learning patterns across stages. Results indicated that the GAT effectively supported regulated learning, primarily promoting “monitoring and evaluation,” social regulation, and “content monitoring” among both high- and low-SR groups. Specifically, the high-SR groups primarily exhibited a regulation focus pattern characterized by “content monitoring-organizing,” while the low-SR groups primarily exhibited a pattern centered on “content monitoring-process monitoring.” The GAT may have a more significant impact on facilitating regulatory behaviors among low-SR groups. Data from interviews explained the differences in the use of the GAT between high- and low-SR groups, as well as the impact of this tool on regulated learning. This study provides evidence of how GATs integrated with collaborative scripts can promote regulated learning, providing insights into instructional practices for integrating collaborative scripts and GATs to support student teachers engaging in CSCL.

Keywords: 

Cooperative/collaborative learning, Improving classroom teaching, Regulated learning, Reflection script

Leon Yufeng Wu

Graduate School of Education, Chung Yuan Christian University, Taiwan // leonwu@cycu.edu.tw

Jia-Wei Liu

Department of Information and Computer Engineering, Chung Yuan Christian University, Taiwan // 10827231@cycu.org.tw

Chih-Chang Yu

Department of Information and Computer Engineering, Chung Yuan Christian University, Taiwan // ccyu@cycu.edu.tw

ABSTRACT: 

Novice programmers face significant challenges in comprehending abstract concepts, tracking program execution flow, and debugging. This study developed a code visualization system (COVIS) and introduced it to an introductory programming course. COVIS visualized variable states, call stack status, and pointer-referenced data while featuring user-defined input, selective display of code snippets, and seamless integration within online learning platforms. Using the Motivated Strategies for Learning Questionnaire (MSLQ) and COVIS usage questionnaires, this study identified multiple positive impacts of COVIS. Students who consistently used COVIS demonstrated increased intrinsic and extrinsic goals, alongside enhanced self-efficacy. These students also demonstrated superior effort regulation capabilities and greater persistence when confronting challenges. Regarding learning effectiveness, COVIS demonstrated delayed cumulative effects. While no significant differences were observed during the midterm exam, final exam results revealed that consistent COVIS users outperformed inactive users, particularly in implementation problems. Additionally, approximately half of the students utilized COVIS to interpret AI-generated code, rather than relying on the AI’s output unthinkingly; regarding peer discussions, the number of students increased to over 70%, indicating that students use COVIS in multiple ways to improve themselves. These findings suggest that COVIS successfully enhanced novice programmers’ performance in terms of motivation, strategic learning approaches, and academic achievement, providing a practical instructional resource for programming education. A demonstration site can be found at https://cyculab618.github.io/COVIS-demo-site/.

Keywords: 

Code visualization, Learning motivation and strategy, Introductory programming learning

Miguel Nussbaum

Pontificia Universidad Católica de Chile, Chile // mn@uc.cl

Zvi Bekerman

The Hebrew University of Jerusalem, Israel // zvi.bekerman@mail.huji.ac.il

Carla Gallardo-Estrada

Pontificia Universidad Católica de Chile, Chile // cogallardo@uc.cl

ABSTRACT: 

Artificial Intelligence (AI) is increasingly integrated into educational practice, assisting teachers in planning, content creation, assessment, and administrative tasks. Yet, most existing studies focus on specific contexts—individual teachers, subjects, or institutions—limiting the generalizability of their findings. This study addresses that gap by analyzing how AI is incorporated into teaching across primary, secondary, and university levels. Drawing on the experiences of 770 educators from Latin America and Spain, we identify patterns in teachers’ reported uses of AI and their perceptions of its pedagogical benefits. Results reveal a Use–Impact mismatch: educators frequently rely on AI for routine and logistical tasks such as lesson planning and content generation (“high use, low perceived impact”), while underutilizing areas where AI could yield greater pedagogical value—such as time management, student feedback, and material preparation (“low use, high perceived impact”). The analysis also uncovers gender-related differences in AI engagement and a progressive sophistication of use across educational levels, with university educators displaying more strategic and pedagogically aligned integration. Methodologically, the study introduces a grounded categorization framework that enables systematic comparison of AI use and impact across diverse educational contexts. These findings highlight the need for professional development initiatives that help teachers move from instrumental to pedagogically meaningful use of AI, particularly in primary and secondary education.

Keywords: 

Artificial intelligence, Teachers, Reported use, Pedagogical benefit