ETR&D

This study aims to validate the existing Elementary Student Coding Attitudes Survey (ESCAS) in the Chinese context and explore the influencing factors of elementary students’ coding attitudes. ESCAS (Chinese) was created, and two survey distributions were administered, involving 1539 elementary students (Grades 3–6) from northern China. First, scale validation was performed with data from 808 students. Based on the validation results, the scale was adapted. The updated scale yielded adequate validity and reliability evidence, with 22 Likert-scale questions covering five factors (i.e., coding confidence, coding interest, utility, social influence, and perception of coders). Second, to investigate the influencing factors of coding attitudes, 731 survey responses were collected. Structural equation modeling (SEM) was conducted, and multiple factors were examined for predicting elementary students’ coding attitudes, embracing demographic features, coding participation, social influence, and cross-disciplinary effects. Affordances of ESCAS (Chinese) for research and education were discussed. Differences across studies were compared, and implications for computational thinking and coding education were suggested.

Generative Artificial Intelligence (GenAI) stands as a cornerstone of the technological revolution, significantly impacting the global educational landscape. This prompts worldwide governments and educational institutions to craft strategic frameworks. This study aims to analyze GenAI’s influence on the education system, particularly focusing on transformations in educational paradigms, modalities, pedagogical logics, and educational contexts. It seeks to establish a transformation action framework for the education system in the GenAI era. Utilizing Meta-ethnography, the research synthesizes, analyzes and interprets 11 policy and guideline documents from UNESCO, OECD, ministries of education and universities, which reveal trends towards personalized and interactive educational forms, shifts in the role of the teacher, and updates in student learning modes. The study explores GenAI’s integration into education at macro, meso, and micro levels. At the macro level, the framework identifies how GenAI drives a productivity revolution and reshapes human resource demands, alongside societal attitudes and educational actions adapting to this transformation. At the meso level, it reflects on educational pattern and logic shifts, delving into the evolution of educational modalities, entities, media and content. At the micro level, it deconstructs new teaching and learning scenarios in the GenAI era, closely examining the evolution of the role of the teacher and student learning modes, scrutinizing the core value of education as a fundamental human right and constructing a vision for future education in the GenAI era. The findings underscore the need for comprehensive transformation in the education system to adapt to GenAI-driven changes, updating educational content and methods to enhance teaching efficiency and quality as well as fostering holistic student development. These insights offer theoretical and practical guidance for the educational sector to respond to GenAI-driven technological changes, aiming to equip the education system to overcome challenges, seize opportunities and prepare talents needed for the future society.

This qualitative single case study examined to what extent two White female preservice teachers’ attributions to struggle during computational thinking tasks rely on stereotypical beliefs. Video recordings of participants’ discourse during computational thinking and transcribed one-on-one interviews served as data sources. Attribution theory served as the framework to guide this study. Critical discourse analysis was adopted with two overarching goals: first, to examine participants’ attributions based on the three dimensions of locus, controllability, and stability; and second, to examine the underlying assumptions in their attributions and assess whether they reinforce or oppose the dominant system of stereotypical beliefs about who can succeed in computer science. An intersectional approach was adopted to discuss the findings about stereotypical attributions as they pertain to participants’ age, gender, race, and socioeconomic status. Results showed prevalence of negative stereotypical attributions to causes of struggle that were dispositional, uncontrollable, and permanent. Fewer non-stereotypical attributions were identified, and those focused on situational, controllable, and temporary causes of struggle. Further, negative dispositional attributions persisted even after successful task completion. Results also pointed out that participants’ dispositional attributions often reinforce a system of stereotypical beliefs that has persistently excluded minoritized populations from computer science. Implications for computer science education are presented.

The integration of technology in the learning landscape has precipitated the need to understand its relationship with students’ cognitive processes. However, there is a gap in understanding how learning engagement interacts with two approaches to using technology and how these, in turn, impact higher order thinking skills (HOTS). This study aimed to explore the mediating role of learning engagement between approaches to using technology and HOTS within the technology-enhanced inquiry-based learning (T-IBL) framework. Data were collected from a sample of 160 college students experienced in T-IBL environments. Structural equation modeling was used to analyse the relationship between these key variables. The results showed that students’ deep approach to using technologies had direct and significant positive impacts on learning engagement and HOTS. While students’ surface approach to using technologies had direct negative influences on learning engagement and HOTS, they were not significant. What is more, learning engagement had direct and significant positive impacts on HOTS. In other words, learning engagement act as a mediator between students’ deep approach to using technologies and HOTS, but not between surface approach and HOTS. This research fills an existing gap by elucidating the intricate relationship between technology use, engagement, and cognitive processes in a T-IBL setting. The findings underscore the importance of fostering deeper engagement and mindful technology use to enhance HOTS in learners, offering invaluable insights for educators and curriculum developers in the digital age.

The study examines how Saudi Arabian higher education professionals use social media for informal learning and skill accession, exploring the effect on skill development, professional networking, and career highness. This study aims to recognize how these platforms contribute to professional development outside traditional educational contexts by inquiring about the skills and competencies obtained the role of professional networks, and the connected provocations. Social media platforms are understood as costly tools for informal learning, allowing Saudi Arabian professionals to acquire knowledge, build networks, and build up skills over conventional education. Professionals in Saudi higher education utilize social media for industry insights, digital literacy, and knowledge sharing through barriers like information overload and content quality arrest formal learning, encouraging institutions to help informal learning. The study employs a mixed methods approach; data were collected from higher education professionals in universities of Saudi Arabia through surveys, social media content analysis, and case studies. The study contributes to providing a foundation for developing effective strategies to enhance informal learning and professional development through digital education. The study findings reveal that professionals utilize social media to obtain different skills, increase competencies, and create valuable networks. However, it experiences barriers such as information excess and differing content quality. Social media programs assist informal learning and career evolution but need strategic support from higher education institutions to direct barriers and optimize welfare. The research provides actionable guidance for higher education institutions and enriches informal learning via social media, boosting professional development strategies in Saudi Arabian higher education settings.

Well-prepared medical first responders (MFRs) are indispensable for effectively managing mass casualty incidents (MCIs). Still, the gold standard for training, high-fidelity real-life exercises (RLEs), is infrequently implemented due to high organizational effort and costs. Mixed reality (MR), where MFRs train in a virtual environment with haptic feedback from manikins, may be a viable training alternative. This study aimed to explore strengths, limitations, and potentials for improvement of MR-MCI training in relation to two RLEs. Thirty-four MFRs (Mage = 29.7, SDage = 7.7, 82% male) participated in MR training, 14 MFRs in RLEs (RLE1, akin to MR: n = 4, Mage = 32.0, SDage = 9.5; RLE2, near-ideal: n = 14, Mage = 26.9, SDage = 6.7; 100% male). Stress, exhaustion, self-efficacy, presence, and perceived learning gain were assessed using questionnaires and analyzed descriptively. Participants further answered open-ended questions about perceived opportunities and limitations of virtual training. The MR and RLE groups reported similar stress, exhaustion, and self-efficacy levels. The MR group reported slightly lower physical presence but considerably lower social presence than the RLE groups. Perceived learning gains were moderate for MR participants and high for RLE participants. Qualitative data indicated a need to improve interaction opportunities with virtual patients. Also, participants viewed virtual training as a resource-efficient supplement, not a replacement for RLEs. Future studies should explore which content and groups benefit most from MR and further evaluate it through larger, experimental studies. MR-MCI training shows promise in preparing MFRs for MCIs and seems to be a valuable addition to RLEs, with the potential to increase training frequency and practice scenarios otherwise difficult to simulate.

The recent surge in the use of learning analytics in education has led to the development of more adaptive and personalized learning environments (APLE). A key feature of APLE is its capability to support learning tailored to various student needs and goals. Although educational studies emphasize goal setting as essential for effective student learning and self-regulation, current empirical research on APLE lacks clarity on how different learning activities (such as text reading and interacting with various task types) vary among students with different learning goals and outcomes, as well as what the specific thresholds and values for these activities are. To address this gap and support further research in APLE, this study aimed to examine how students with different learning outcomes (mastering, passing, and non-passing the course) differ in their learning behaviors and cognitive engagement with course materials, as indicated by their digital trace data obtained from APLE. Conducted within a formal asynchronous distance higher education program, the study grouped students based on their final exam scores and analyzed their digital traces. The findings highlight which aspects of digital trace data correlate effectively with student performance and identify parameters of various indicators that can be useful for guiding students’ behaviors towards desired academic goals. Additionally, the study offers valuable insights by challenging conventional assumptions about the uniform efficacy of different learning tasks (quiz tasks, self-assessment tasks and expert corrected tasks) in assessing student learning progress and outcome. It prompts a discussion about the role of student self-assessment, suggesting that while it is crucial for the self-regulation and learning process, it may not be the best indicator for students’ goal attainment.

In an educational landscape marked by diversity, from district mandates to curriculum, teachers’ needs vary based on school and classroom contexts as well as their experiences, necessitating tailored support. This study investigates the efficacy of a hybrid Lesson Study with Video Clubs (LSVC) professional development (PD) model over a year-long period. LSVC leveraged technology to address the distinct requirements of teachers across varying experience levels. Traditional PD modalities often struggle to accommodate the nuanced demands of educators in specialized contexts. Recognizing the pivotal role of technology in reshaping professional development, this study stresses the imperative of targeted, sustainable initiatives for bolstering teacher professionalism and improving student outcomes amid increasing classroom diversity. The LSVC hybrid model emerges as a promising framework, catering to educators' needs across the experience spectrum within specialized teaching contexts through the intentional use of technology. This study illustrates how novice and seasoned teachers experienced transformative professional learning, through synchronous and asynchronous collaboration with peers of diverse experiences, facilitated by the technology-enhanced PD format of LSVC. This model, characterized by adaptability, sustainability, and affordability through the strategic integration of technology, fosters the establishment of vibrant professional communities that propel long-term career development pathways for educators and administrators.

Virtual reality (VR) has been widely adopted in natural science education for learners to engage in inquiry-based learning in a safe and immersive environment. Also, the Predict-Observe-Explain-Evaluate (POEE) strategy is often used in inquiry-based activities to guide learners to understand and delve into their acquired knowledge during the inquiry process. However, the evaluation phase in conventional inquiry-based activities only provides feedback and solutions based on learners’ answers to the learning questions. Researchers have pointed out that without analysis and feedback on learners’ misconceptions, the learning effects of inquiry-based learning activities may be worse than expected. As a result, the present study proposed a Predict-Observe-Explain-Diagnose (POED)-based VR approach which could diagnose misconceptions and provide guidance. In VR learning activities, in addition to judging whether students’ answers are based on accurate reasons, it is helpful to further diagnose the possible misconceptions due to their wrong judgments so as to provide learning guidance. To explore the effectiveness of the proposed approach, the present study adopted a quasi-experimental design and recruited two classes of eighth graders as participants. One class was the experimental group adopting the POED-based VR approach, while the other class was the control group adopting the conventional POEE-based VR approach. The results showed that the experimental group had significantly better performance in learning achievement, problem-solving tendency, critical thinking tendency, and metacognition tendency than the control group. Besides, based on the behavioral analysis results, the POED-based VR approach could help students better understand their own misconceptions in learning, and then have more learning behaviors of reading supplementary materials, which was conducive to constructing accurate knowledge and improving learning performance.

The current study aimed to investigate the effectiveness of a technology-integrated hybrid literacy program on children's language abilities. One hundred fifty-nine kindergarteners from low SES backgrounds participated in the study. The intervention combined face-to-face and online lessons, delivered to 27 kindergarteners during the COVID-19 isolation period. Their results were compared to those of 71 children who learned in a face-to-face program before the pandemic. The performance of each intervention group, technology-integrated hybrid and face-to-face, was compared with that of a control group matched by demographic background and method of learning (27 for the technology-integrated hybrid, and 34 for the face-to-face). The findings indicated no significant differences in the positive changes observed in the language abilities of the technology-integrated hybrid and the face-to-face intervention programs. However, the score change in the examined language abilities of the comparison group was greater when the program was delivered face-to-face than in the technology-integrated hybrid program. Educational implications regarding the effectiveness of tailored intervention programs development, that take into account the use of technological tools, on children’s language development, are discussed.

Decision-makers are challenged by the inherent complexity and dynamic nature of human-induced changes when dealing with environmental issues. The process of thinking about the future and developing a ‘proactive strategy’ can better inform sustainability decision-making in the present. The use of AI-based models, particularly machine learning algorithms, may enable us to more accurate forecasting and response to future environmental change through the development of a series of scenarios. Therefore, we propose the application of AI technology in the formal school geography curriculum as a means of envisaging options for the future and evaluating such options to develop a set of alternative plans. Through the design experience of pedagogical ideas and learning activities, we identify how AI can be used to present options for the future, thereby engaging different teaching modes that encourage high school learners to make data-informed decisions and be more proactive in regard to anthropogenic environmental changes.

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.