ETR&D

This study presents the development and validation of a Learning Analytics Rubric for Self-Regulated Learning (SRL) in higher education. The rubric aims to measure students’ SRL processes within learning management systems (LMS) in a scalable, consistent, and explainable manner. The research follows a design-based approach, mapping validated SRL scales to LMS data indicators, developing the rubric for a Canvas LMS, and validating it with postgraduate students. The study identifies challenges in measuring SRL, such as the dynamic nature of SRL and the limitations of translating traditional self-report methods to digital environments. By leveraging learning analytics, the study proposes a novel approach to measure SRL behaviors using LMS data. The validation process reveals that five of the seven indicators accurately reflect students’ SRL skills, with strong alignment between student self-assessments and system-generated scores for indicators related to reviewing content, integrating information from multiple sources, following study schedules, pacing learning, and reading assessment instructions. However, significant discrepancies were observed in indicators measuring completion of extra activities and early semester engagement with the LMS, highlighting the need for further refinement. The findings suggest that integrating learning analytics with rubrics can provide valuable insights into students’ learning processes, particularly measuring SRL, while supporting the development of effective educational interventions from a student-centered approach.

Theories and practices to enhance multiple document comprehension and integration are crucial in both personal and work contexts, especially with the proliferation of printed and online sources. This experimental investigation replicates and extends (Wei et al., Educational Technology Research and Development 72:661–685, 2024) to examine how multiple documents integration is influenced by reading purpose, summary writing, and extra-textual networks (pre-reading, Study 1, and post-writing, Study 2). In Study 1 (N = 102), participants were randomly assigned to a pre-reading purpose set by a prompt (integrative or detailed) and by a network (an integrative or else an intra-text network) and then read three documents about Alzheimer’s disease to complete a writing task with revision (but no feedback). Three days later, they completed a delayed writing task and an inference verification test. In Study 2 (N = 90), the same procedure was used except that the network was used as feedback after writing to support revision. Results from the two studies agree with the previous research that the quantity and structural quality of integration can be improved by external cues and by delayed repeated writing. This research further confirms an innovative approach for evaluating different aspects of knowledge integration and contributes to the literature from the concept network perspective as a measure and an intervention of multiple-text reading.

Unplugged activities (UA) and plugged activities (PA) are two primary teaching approaches used to develop students' computational thinking (CT). However, reaching an academic consensus regarding which approach is more effective remains elusive. This study presents a meta-analysis of 37 studies published between January 2006 and March 2025. These studies are used to compare the effectiveness of UA and PA for developing students' CT. The results indicate that, overall, PA (g = 0.606) is more effective than UA (g = 0.501) in developing students' CT. This study also compares the effects of UA and PA across the four moderator variables of educational level, intervention duration, type of course, and learning model. The results show that UA is better than PA in terms of developing students’ CT in game-based learning. However, PA is better than UA in secondary school and college, in interventions lasting less than four weeks, humanities and arts courses, and problem-based and project-based learning. The effect of UA and PA on students' CT development may be quite similar in the primary education stage, when the intervention duration exceeds four weeks, in information and technology courses, and in natural science courses.

In this paper, I report a critical case study of optimization in online course design within the context of higher education. Through ethnographic work conducted at a university in the United States, I studied an office of online course design, investigating how the office (comprising course designers, administrators, other staff, and the faculty they worked with) enacted optimization as a practical concern. The analysis revealed that optimization was not only the result of interactions between various actors, but also the influence of multiple artifacts that mediated the transformation of educational ideas into concrete learning resources, presumed to be calibrated for a specific purpose. However, since optimization was not a singular construct, course designers regularly found that optimizing along one dimension (perhaps to comply with a policy) caused damage in another (such as providing an engaging learning experience). Furthermore, the practices of course design tended to deemphasize matters purely associated with the quality of learning, while trending towards forms of optimization related to organizational efficiency: streamlining, standardization, reliance on quantified measurements, and developing mechanisms of interchangeability. I conclude by discussing how these findings complicate our understanding of course optimization as well as of course design itself, and what implications this understanding holds for the field.

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...

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 c...

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...

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.