ETR&D

The formation of the digital divide is influenced by both objective factors, such as insufficient digital resources, and subjective factors, such as technology acceptance. This study employs a mixed-methods approach, utilizing the KANO model to analyze learners' demand attributes and the UTAUT model to examine subjective factors influencing technology acceptance. Standardized tests and survey questionnaires are used to assess digital learning outcomes. By collecting and analyzing data from diverse learner groups, this study aims to explore strategies for bridging the digital divide when transforming traditional online education into intelligent education, particularly in technologically and infrastructurally underdeveloped regions, including developing countries. The findings indicate significant differences in demand priorities among learners, as well as notable variations in how different learner groups classify their learning needs. Based on the demand analysis, targeted functional development can be implemented to reduce development costs in developing countries while maintaining the accessibility of digital resources. Moreover, user preferences for digital learning vary across groups; AI-driven identification and personalized recommendations can facilitate a more inclusive and equitable digital learning environment. Additionally, lowering the barriers to technology use through AI, enhancing engagement, and improving perceived effectiveness can significantly strengthen learners' confidence and motivation in bridging the digital divide. Finally, governments, educational institutions, and corporations should establish stronger communication and collaboration mechanisms to jointly address the pervasive digital divide in the era of intelligent education, particularly in developing countries.

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.