ETR&D

This study investigated the effectiveness of localized learning content (LLC) in enhancing computational thinking (CT) skills and learning motivation among 6th-grade elementary school students in Busan, South Korea. In contrast to conventional methodological approaches, our research focuses on the transformative power of culturally and contextually relevant educational content. The study involved eight classroom teachers and 153 students, half of whom were exposed to programming education based on LLC and the other half to a conventional curriculum approved by the Ministry of Education. Quantitative analysis revealed a statistically significant improvement in CT skills and learning motivation among students in the LLC group compared to those in the control group, with effect sizes indicating a moderate magnitude of improvement. Semi-structured interviews with teachers and students supported these findings, indicating higher engagement and perceived relevance of LLC-based courses. However, this study also uncovered challenges related to the time and resources required to develop localized content. Despite these limitations, this study supports the potential utility of LLC, aligning with the sociocultural theory and the information process theory. It also opens new avenues for future research into LLC’s long-term efficacy and logistical feasibility. Given the significant improvements in CT and student motivation, the findings underscore the potential of LLC as a transformative approach in software education.

Writing explanations is widely recognized as an effective strategy to promote meaningful learning outcomes. However, most research focused on writing explanations for fictitious peers, with limited investigation into the benefits of writing for actual peers, particularly considering the influence of peer relationship closeness. To address this gap, the present study examined how explanation type (writing explanations vs. viewing explanations) and peer relationship closeness (close peer vs. distant peer) affect student’s perceived learning experience, attention allocation, behavior patterns, metacognitive judgment, and learning performance (i.e., retention and transfer). Our findings indicate that close peer relationships enhance student motivation and mental effort without increasing perceived difficulty. Furthermore, our results highlight the benefits of writing explanations, especially for a close peer, in enhancing students’ metacognitive judgment, self-monitoring behavior, and learning performance. These results lead to a recommendation to form strategic pairs or small groups in generative learning activities.

The adoption of blended and hybrid Flipped Classroom (FC) models increased dramatically during COVID-19 and is still highly valued and recommended for enhancing the quality, flexibility, and effectiveness of teaching and learning post-pandemic. While meta-analyses indicate a small yet meaningful effect size of the FC approach, examined studies often lack theoretical groundings and/or explicit design frameworks. As a result, there is an ever-increasing need for instructional design guidance for effectively integrating and facilitating online and in-person learning in the FC context. The current paper outlines a framework intended to guide educators, designers, and researchers to maximize the synergy of online and in-person learning as they design and implement FC. Grounded in (Merrill, First principles of instruction: Identifying and designing effective, efficient and engaging instruction, Pfeiffer, 2013) First Principles of Instruction, (Zimmerman and Schunk, Zimmerman and Schunk (eds), Handbook of self-regulation of learning and performance, Routledge, 2011) Self-Regulated Learning process, (Jones, Motivating students by design: Practical strategies for professors, CreateSpace, 2018) MUSIC Model of Academic Motivation, and (Flavell, American Psychologist 34:906, 1979) Metacognition theory, we first present a conceptual framework with flipped learning cycle, self-regulated learning cycle, and metacognitive FC orientation. Informed by these theories and our FC design experience across disciplines, we convert the conceptual framework into a procedural framework by organizing the design aspects and components into a generic FC learning process. Finally, we propose theoretically and empirically grounded design strategies for individual components of the FC process, which can be further validated and refined through iterative educational design research.

Artificial Intelligence (AI) tools are becoming increasingly popular in education, providing teachers with new opportunities to enhance student learning experience and build upon existing teaching practices. This study employs a co-design approach to develop AI-integrated learning materials and explore their implementation in elementary classrooms. In collaboration with researchers, twenty-five in-service teachers co-designed engaging, age-appropriate lesson plans aligned with the national elementary curriculum and adaptable to diverse classroom needs. Qualitative analysis of teachers’ focus groups (n = 25) highlighted the co-design approach's role in empowering teachers, fostering a sense of ownership, encouraging knowledge-sharing, and promoting collaboration and enthusiasm. A pilot study conducted in four elementary classrooms with 62 students evaluated the AI-integrated lesson plans and materials through quantitative survey data (n = 62). Findings indicate that students found the AI-supported learning experience enjoyable, engaging, and meaningful, particularly in deepening their understanding of literacy concepts. This paper presents the co-designed materials and provides insights for practitioners and researchers on the future of AI-powered education, offering potential directions for further research.

In educational settings, gamified learning integrates a variety of game elements to improve the learning experience, but a thorough analysis comparing different combinations of these elements is sparse. This meta-analysis consolidated data from 182 effect sizes across 37 randomized or quasi-randomized trials to assess the impact of gamified learning on student outcomes. Statistical analyses were conducted with gamified learning as the independent variable, learning outcomes as the dependent variable, and learning domain, learning stage, and intervention duration as moderating variables. The study aimed to evaluate the overall effect of gamified learning and to determine the most effective combinations of game elements. The results revealed a medium positive effect of gamified learning on learning outcomes (d = 0.566), with the “Rules/Goals + Challenge + Mystery” combination yielding the highest impact. And significant moderation was observed regarding the learning domain and the duration of the intervention. These findings offer valuable guidance for the design and application of gamified learning strategies, highlighting the need to consider moderating factors in educational practice.

Generative Artificial Intelligence (GAI) has received widespread attention recently, influencing teacher education in various ways. However, there is little discussion on pre-service teachers’ behavioral intention towards GAI. Therefore, this study employs subjective norm, AI self-efficacy, facilitating conditions, and trust to expand the Technology Acceptance Model, understanding pre-service teachers’ adoption of GAI. The study involves 486 undergraduates from a university in Jiangsu Province, China. The Partial Least Squares Structural Equation Model is used to test the research model. Research model proved to be both reliable and valid, confirming nine out of ten hypotheses. The findings indicate that: (1) AI self-efficacy strongly predicts perceived ease of use; (2) The most direct and strongest impact on perceived usefulness is perceived ease of use, followed by facilitating conditions; (3) Perceived ease of use doesn’t directly affect attitude towards use, but perceived usefulness and trust significantly influence this attitude; (4) Attitude towards use greatly predicts behavioral intention, followed by perceived usefulness and subjective norm. This research helps in advancing policy development in educational institutions and the integration of GAI and teacher education.

Computational thinking (CT) skills have become increasingly important in modern education, as they equip students with critical problem-solving skills applicable across various domains. Given the growing emphasis on digital literacy, it is essential to investigate grade- and gender-level differences in CT skills among students to support targeted interventions and to ensure that all students have equal opportunities to succeed in the digital age. This study examined CT skill development among primary school students, taking both grade- and gender-level disparities into account. Using quantitative data from a diverse sample of 517 primary school students, we conducted a comprehensive analysis of their CT scores. The results revealed no significant gender differences in CT scores among primary school students. However, notable age-related disparities emerged, with CT scores rising as students progressed through higher grades. This finding underscores the importance of considering developmental factors in CT education and highlights the need for age-appropriate CT curricula. By investigating both grade- and gender-level differences, this study aims to support educators and policymakers in developing more inclusive and effective strategies for cultivating CT skills among young learners, thereby preparing them for the challenges of the digital age.