ETR&D

The crucial role that student-related factors play in the effect of flipped learning has been emphasized, and self-regulated mechanisms have been integrated into flipped classrooms to promote students’ learning; however, self-regulatory skills are of no use if learners cannot be stimulated to utilize them. In this study, a differentiated self-regulated flipped learning approach (namely DSR-FL), which integrated differentiated instruction and self-regulation into a flipped classroom, was designed to support EFL students’ learning. Furthermore, a three-group experiment was conducted to evaluate the influence of the three different flipped learning models, namely the DSR-FL approach, the SR-FL (incorporating self-regulation into flipped learning) approach, and the C-FL (conventional flipped learning) approach. The results indicated that both the DSR-FL and SR-FL approaches were capable of promoting students’ class engagement, motivation, and perceptions of self-efficacy, in comparison with the C-FL approach; furthermore, the students who learned with the DSR-FL approach outperformed those who learned with the C-FL approach in terms of improving their learning achievements. This could be a valuable reference for teachers to promote EFL students’ learning.

Mobile learning can positively impact learning in different aspects, but the retention rate of mobile learning applications could be better. Based on the Technology Acceptance Model and the updated DeLone and McLean Information System Success Model, this study develops a novel model to examine the determinants of learners’ acceptance of mobile learning outside the classroom. Learning outside the classroom refers to voluntary learning activities that occur beyond the physical classroom and scheduled instructional time, including activities performed by both students and non-students (e.g., those not currently enrolled in educational institutions). Six hundred eighty-one adults in the U.S. participated in this study. We utilized structural equation modeling for data analysis. Results indicate that two quality dimensions, namely system quality (mobility and compatibility) and service quality, and two learners’ beliefs, namely perceived usefulness and perceived ease of use, play an essential role in m-learning acceptance outside the classroom.

Inquiry-based learning (IBL) is a practice-oriented approach where students pose questions, conduct investigations, and interpret data to develop scientific knowledge and exploratory skills. Learning analytics (LA) holds great potential to capture these dynamic processes, which provides valuable insights to understand student inquiry behaviours and support their practical performance. However, limited studies have systematically examined how LA can be applied to understand and support IBL, limiting its practical applications for both teachers and students. This study synthesises findings from 51 studies to explore research trends, theoretical foundations, LA implementation in understanding IBL processes, and the impacts of LA-supported IBL. The findings reveal that most studies, guided by IBL-related or broader learning theories, focus on tracking students’ general inquiry engagement (individually and collaboratively) and specific investigation behaviours, with limited attention to critical stages of inquiry, such as hypothesis generation, data interpretation, group collaboration, and their interactions among these multistage tasks. Some studies demonstrate that LA-based tools, like dashboards and resource recommendations, have significant potential to enhance students’ inquiry processes and empower teachers in designing and implementing effective inquiry activities, while empirical evidence remains insufficient to understand how these LA-supported IBL shape student inquiry processes and outcomes. This review identifies several research gaps and proposes future directions to advance the integration of LA in understanding and supporting both students and teachers in IBL contexts, aiming to promote more effective and evidence-based applications of LA in inquiry activities.

Situated learning has been widely promoted in educational practice, where students are encouraged to learn by exploring real-world problems in authentic contexts. To expand the opportunities for situated learning, immersive virtual environments have been explored by presenting problem contexts in vivid and interactive formats and enabling a variety of exploration activities. However, there are multiple challenges surrounding situated learning. The challenges can be caused by the complexities of real-world problems, the complexities in exploring real-world problems, and the complexities in reflecting on the exploration experience. This paper presents a conceptual framework outlining three types of complexities surrounding situated learning and six strategies for coping with these complexities. A case of situated learning curriculum in an immersive virtual environment is used to illustrate how the framework works in practice. By presenting a high-level and holistic picture of the challenges in situated learning along with the coping strategies, the proposed framework enriches the understanding of situated learning. It can serve as a guide for designing situated learning curricula, evaluating situated learning practices, and addressing situated learning challenges.

The complex processes of collaborative knowledge construction require a multimodal approach to capture the interplay between learners, tools, and the environment. While existing studies have recognized the importance of considering multiple modalities, there remains a need for a comprehensive framework that explicitly models the dynamics of knowledge representation and construction. Drawing on theoretical perspectives from collaborative knowledge-building, distributed cognition, and multiple representations in science education, we propose a multimodal representation framework that captures the diverse ways in which learners externalize, negotiate, and advance their understanding. We employ Transmodal Ordered Network Analysis to examine the interplay between knowledge representations across three distinct yet interconnected spaces: the virtual space of the digital environment, the conceptual space of internal knowledge, and the physical space of gestures. This approach enables a more granular and accurate modeling of the temporal dynamics and influences associated with different modalities. Investigating 16 groups of college students (n = 77) who utilized an immersive astronomy simulation in their introductory astronomy course, results reveal distinct patterns between high- and low-learning groups. Notably, high-learning groups demonstrated more frequent and stronger cross-modal connections, linking verbal explanations with digital representations within the simulation and with embodied representations through gestures. It extends the theory of multiple representations by demonstrating its importance not only for individual learning but also for collaborative processes. The findings highlight the need for designing learning environments and analytic approaches that can support and capture the rich multimodal interactions through which students co-construct scientific understanding.

Nurturing student artificial intelligence (AI) competency is crucial in the future of K-12 education. Students with strong AI competency should be able to ethically, safely, healthily, and productively integrate AI into their learning. Research on student AI competency is still in its infancy, primarily focusing on theoretical and professional discussions, along with qualitative investigations. This two-stage study aims to propose an AI competency framework for students and confirm the reliability and validity of its scale—student AI competency self-efficacy (SAICS)—in K-12 education. In stage 1, we used a three-round Delphi study to propose the framework and its scale. The framework has eight dimensions: interdisciplinary learning with AI, assessment with AI, decision-making with AI, data, ethics and AI, designing AI, multimedia creation with AI, human-centric learning, and confidence with AI. Each dimension contains four items. In stage 2, we involved 448 students to validate the scale using confirmatory factor analysis and model comparisons. The analyses showed that the scale is consistent across male and female students. The SAICS scale comprises 32 items and addresses eight dimensions of AI competency. Researchers can use the framework and SAICS to design their interventions and correlational research associated with student AI competency. Teachers can use them to develop learning outcomes for AI-based learning activities, and policymakers can use them to establish national AI standards.

This article proposes an instructional model based on psycho-pedagogical theories to serve as a basic structural unit for the creation of educational reinforcement platforms aimed at strengthening quantitative competences with which students enroll mathematics and statistics subjects (or other subjects that draw on this knowledge) at university. Although there are Intelligent Tutoring Systems (ITS) that are beneficial for students, the difficulty of manipulation and programming, together with their high economic cost when lacking programming skills, have prevented a widespread use of this type of interventions. Following the first steps of the ADDIE model, this article develops an instructional model that can be easily replicated by instructors lacking in programming and digital skills, designed to be applied in free and easy-to-handle interactive tutoring platforms, such as Genially.com or Canva, among others. The main foundations on which the pedagogical guideline is based are extracted through an extensive review of academic literature on psycho-pedagogical theories such as scaffolding, effective learning, metacognition, educational reinforcement, or feedback. Through it, students will be able to strengthen their quantitative conceptual foundations and reflect on their own learning process.

In recent years, studies have discussed how to introduce computational thinking (CT) concepts in mathematics education through mobile app development. In this study, the design of mathematics lessons based on the 5E instructional model to extend the idea of CT in a mobile technology environment (i.e., mobile CT) was investigated. Twenty-three primary five students in Hong Kong participated in this study. The teacher taught the students how to develop a mobile calculation game to learn the mathematical concept “area” through paper prototyping and mobile app development activities. Using a design-based research approach, the study examined students’ performance and behavior in the classroom to acquire mathematics knowledge and mobile CT. Qualitative conversation analysis was used to interpret teacher-student interaction, code files, and screen captures of students’ work. The analysis provided evidence on how students constructed mathematics concepts about “area” and built their mobile calculation games using mobile CT concepts, practices, and perspectives. The results propose the use of the 5E instructional model to enhance students’ engagement in and motivation for mathematics learning and strengthen their problem-solving skills, critical thinking, and communication and collaboration skills. Mobile CT-integrated mathematics lessons suggest ways for future educators to incorporate other mathematics topics into CT education. This study recommends that the 5E instructional model could be suitable for the instructional design of primary school CT-integrated mathematics curriculum. A set of design principles for integrating CT into mathematics curriculum is recommended.

In this study, Q methodology was employed to explore instructional designers’ perceptions of integrating ChatGPT in their design practices. Compared with traditional survey-based instruments that rely heavily on Likert-scale items, open-ended questions, interviews, or focus groups, Q methodology has the potential to systematically reveal and study subjectivity within a certain group of participants with both quantitative and qualitative techniques. The participants of this study consisted of 19 practicing instructional designers, who were asked to sort a total of 25 statements regarding the integration of ChatGPT into instructional design practices. Findings revealed three distinct types of factors: (1) Pessimistic Evaluators, (2) Optimistic Advocates, and (3) Wary Thinkers. Characteristics are discussed with direct quotes from representative participants from each of the three factors. The study also revealed that instructional designers mainly used ChatGPT to generate content, help improve writing and problem-solving, communicate, and engage in information searching. Regarding the challenges instructional designers encounter, the study reported that they were primarily bothered by the low quality of the ChatGPT-generated content, the limitation of ChatGPT itself, and their unpreparedness to embrace the tool. Limitations of the current study, as well as recommendations for future studies were also mentioned.

This research presents the results of a questionnaire survey (N = 222) exploring teachers’ experiences with using Wikipedia as a teaching tool, mostly in higher education, across various global contexts. The sample comprised educators from diverse regions, with a focus on those actively integrating Wikipedia and additional Wikimedia projects such as Wikidata, into their curricula. A mixed-methods approach was employed, combining quantitative analysis of structured questions with qualitative thematic analysis of open-ended responses. The findings reveal no significant gender or age biases among educators using Wikipedia; however, there is evidence of a global digital divide, with greater adoption observed in English-speaking countries. Most instructors reported assigning students to write or improve Wikipedia articles, typically accounting for about a quarter of the final course grade. Educators frequently utilized support tools and resources developed by the Wikimedia Community. Overall, participants reported positive teaching experiences, often linked to increased student and instructor motivation, as well as the achievement of multiple learning objectives related to academic and digital literacies. Nonetheless, the assignment was noted to be time-consuming. The study also found that Wikipedia assignments were well-suited for the transition from traditional to distance learning during the COVID-19 pandemic.

Problem-solving strategies are crucial in learning programming. Owing to their hidden nature, traditional methods such as interviews and questionnaires cannot reflect the details and differences of problem-solving strategies in programming. This study uses the Hidden Markov Model to detect and compare the problem-solving strategies of different groups in an interactive programming environment. The results suggest that high- and low-performance students have significant differences in their problem-solving strategies in programming. High-performance students had more “blank behaviors” in programming than low-performance students in video recordings. Low-performance students spent more time “searching teaching materials” than high-performance students. In the transfer task, high-performance students began the task by “identifying the problem,” while low-performance students were involved in the “implementing of strategies.” Additionally, high- and low-performance students improved from basic to transfer tasks. These findings shed light on why students performed differently in programming and how and when teachers needed to provide instructions to students in programming education.

Synchrony, or the timing of information as it is exchanged between participants, has garnered increasing study in online learning. Within this domain is bichronous online learning (BOL), the blending of asynchronous and synchronous elements within one learning environment. Some research has identified mobile instant messaging as quasi-synchronous (able to be both synchronous and asynchronous), but this affordance has been largely unexamined. This study addressed the above gaps by comparing online learning across asynchronous, synchronous, and quasi-synchronous modalities. It was framed by online learner engagement (OLE) which considers affective, behavioral, and cognitive dimensions and various environmental affordances including synchrony. This convergent mixed-method study explored engagement across forums, video chats, and mobile instant messaging (MIM) via qualitative content analysis, text mining, descriptive statistics, and social network analysis. There were several findings: The time students had to prepare their responses before interacting related to all three dimensions of engagement. Affectively, the quasi-synchronous modality appeared the most positive and least negative. Behaviorally, the quasi-synchronous and asynchronous modalities were more like one another than to the synchronous modality. Logistical flexibility afforded by the asynchronous and quasi-synchronous modalities impacted behavioral engagement. The quasi-synchronous debate had a unique dialectical structure compared to the other two modalities. Some students made use of other features within the Zoom debate to create a quasi-synchronous experience toward better cognitive engagement. Although not directly connected to synchrony, navigability seemed related to all dimensions of OLE as well. Finally, evidence among the preceding findings support the proposition that MIM’s quasi-synchronicity supports BOL.

In most physical education courses, teachers generally observe and interact with students face-to-face, so as to assess their learning status and provide immediate and appropriate guidance. However, during the COVID-19 pandemic, practical physical education courses moved completely online. Without physical contact, the safety, assessment, and overall learning quality of students in online physical education courses have become a difficult issue for teachers. Hence, the present study referred to the concept of reflective practice and developed a yoga skill auto-assessment and feedback (Auto-Yoga) system. It was implemented to provide students with instant professional evaluation and feedback, as well as to support the distance teaching of physical education courses. In order to verify the learning effects of the Auto-Yoga system, a class of 45 students was assigned to be the experimental group who adopted the Auto-Yoga system for learning, while the other class of 43 students as the control group adopted the conventional yoga skill learning (C-Yoga) system for learning during the period when face-to-face classes were not feasible due to the pandemic. The results showed that the Auto-Yoga system could significantly enhance students’ yoga skill performance, learning motivation, and critical thinking.

Area measurement is a foundational component across STEM fields. The area formula offers limited insight for children trying to develop their area measurement reasoning skills, particularly when dealing with composite shapes. Spatial structuring, where children explore structural units of a 2D space, is an alternative approach. However, poor manipulation of physical units can reinforce misconceptions about area measurement. To address this issue, we developed MeoGeo, an augmented reality (AR) smartphone application. MeoGeo allows children to readily create virtual structural arrays with units of varying sizes, superimposing them on their surroundings in real-time. We conducted an exploratory multiple-case study over a 7-week program to examine the effect of AR activities. Data included written responses from three elementary students on paper-and-pencil tests, along with in situ videos recordings. Our findings indicate that engagement in AR activities facilitated reasoning skills in area measurement for both basic and composite shapes. Furthermore, students maintained their reasoning skills beyond the intervention period. Our study underscores the importance of developing AR systems that align explicitly with the developmental progression of each child, and it highlights the critical role of the instructor in effective execution of AR activities.

The current systematic review study explores instructional designer practices to develop a comprehensive understanding of strategies employed by instructional designers and the expectations of the field. Using the PRISMA protocol, a total of 98 articles were included in the review consisting of 65 qualitative, 15 quantitative, and 18 mixed-methods studies. Instructional designer practices were analyzed through the lens of 10 different topical areas: (1) Attitudes and perceptions, (2) decision-making and design judgments, (3) design strategies, (4) professional development and professional identity, (5) design heuristics, (6) instructional designer and faculty relationships, (7) instructional design competencies, (8) problem-solving, (9) instructional design teams, (9) instructional design teams, (10) change management and organizational development. Evidence-based recommendations and suggestions for practices of current and future instructional designers were discussed.

Teaching Computer Science concepts, such as graph theory, is often challenging. This study proposes an approach for teaching graph theory using an unplugged game (GraphGame) developed through a participatory design process that includes usage observation, clarifying meaning, prototyping, and implementation. This process was carried out with a group of Brazilian middle school students and involved collecting observations, interviews, ideation activities, and iterative prototyping with quantitative and qualitative tests. The game offers an interactive way to explore graph algorithms, improving abstraction capacity, a skill related to Computational Thinking. The effectiveness of the game in facilitating the learning of fundamental graphs concepts among high school students was evaluated with an independent sample of students. The results pointed out the proposal as a promising alternative for teaching graph theory, a complex computing topic, in an engaging way. By enhancing playful learning, this work offers an alternative to make teaching Computer Science more enjoyable and effective.

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.