ETR&D

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.