ETR&D

Experiential learning and online learning platforms are increasingly being recognized as important in contemporary vocational language education and research. However, vocational language education has been criticized for its lack of connection with current technological developments. To address this issue, the study aims to determine the effects of “Experience-Based Cyclical E-Curriculum Design” on learners’ vocational second language performance and language learning experiences. The study utilized a design-based research approach. Quantitative data from the Vocational Second Language Skills Assessment Test were analyzed using ANCOVA, and qualitative data from Reflection Form were analyzed using Strauss and Corbin’s coding technique. The results indicate that the experiential learning curriculum, developed using the E-Curriculum Design framework, had a positive impact on learners’ vocational second language performance and vocational second language learning experiences. Thus, this study suggests that E-Curriculum Design, when integrated with an online learning environment, can effectively support learners in constructing knowledge through practical experience.

This study aimed to examine writing performance, critical thinking tendency, and research writing reflection among postgraduates with different reading levels across different subject professional backgrounds. A problem-based flipped classroom learning environment was implemented to enhance graduates' research writing performance and perceptions. A total of 28 first-year postgraduates participated. The result revealed significant differences in research writing performance and critical thinking tendency between students with low reading levels (LRL) and those with high reading levels (HRL). Additionally, an interaction effect was observed between reading level and subject professional background on graduates' critical thinking tendencies. In the LRL group, the critical thinking tendency of students in this major is significantly higher than that of students in non-majors. Furthermore, Epistemic Network Analysis (ENA) indicated significant differences in research writing reflections between students with different reading levels and pre-test and post-test. However, no significant differences were found across different subjects professional backgrounds. These findings demonstrate that problem-based flipped classroom learning can improve research writing performance and perception. From the teaching perspective, it provides a reference for writing instruction.

Advances in educational research and technology provide educators and learning and development (L&D) specialists with powerful tools to innovate health professions education (HPE) and address the relentless growth of content information. Yet, traditional practices often hinder the critical appraisal and application of evidence-based educational methods. While systematic models ground the instructional design of learning experiences in research and theory, they are frequently perceived as too rigid, time consuming, and resource intensive for widespread adoption in clinical education settings. This article introduces an agile, evidence-informed approach to instructional design tailored for HPE. The Agile eVidence-Informed Design (AVIDesign) model was developed to streamline the design and continuous improvement of educational interventions. AVIDesign aligns with evidence-based medicine by offering a systematic yet flexible process for designing instruction, incorporating design sprints, transdisciplinary collaboration, and contextually relevant appraisal of research evidence. We present the core principles and practices of AVIDesign, including strategies to right-size instructional design initiatives, formulate targeted LICO (Learner, Intervention, Context, Outcome) questions, appraise evidence using QSR (Quality, Strength, Relevance) criteria, and evaluate outcomes through formative and summative assessments. The application of AVIDesign is illustrated through three real-world design projects in HPE involving curriculum development, branching scenarios, and ePortfolio systems. Each case highlights how AVIDesign promotes compatibility, trialability, and reduced complexity to facilitate adoption. Key lessons learned from the projects are distilled to inform future applications of AVIDesign and guide continued research on agile, evidence-informed instructional design practices in health professions education.

Developing digital literacy (DL) is essential for older adults to keep pace with the rapid advances of technologies. This review took an evidence-based approach to examine the effectiveness of digital training programs designed for older adults. Adopting the PRISMA guideline, a systematic search was conducted on Web of Science, Scopus, and EbscoHost, which yielded a total of 4552 empirical articles. Of these, 46 were further examined based on the DigComp 2.2 Framework to identify digital competencies for older adults. Results showed that wealthier nations have greater technological adoption, with their older adults having better access to learning resources and training. Despite various barriers faced by some older adults, it would be beneficial to design exemplary solutions that group learners with similar levels of digital competency. This review identified that providing step-by-step instructions and pacing the lessons carefully can greatly enhance the learning experience. Other approaches, such as intergenerational learning, personal tutoring, game-based learning, dialogic learning, and peer learning, can effectively address the cognitive, social, attitudinal, and health-related needs of older adults. In future lesson design, it is worth implementing training on a long-term basis, personalizing the learning experience, and eliminating any practical barriers that might hinder the learning process. Moreover, future research should consider geographic diversity when recruiting participants, customize lessons for different cultural backgrounds, integrate technologies further into the daily life of older adults, and examine how emerging technologies could enhance the health of older adults.

Generative Artificial Intelligence (AI) has introduced a new tool to the educational environment that can be used in multiple ways by both teachers and students. This study employed a within-subjects design to verify whether Generative AI can enhance students’ learning satisfaction, self-efficacy, and learning outcomes. Twenty college students participated in two rounds of experiments: the Teacher Assistance Group, where 20 students completed the test with the assistance of one teacher; the Generative AI Assistance Group, where 20 students could all have a Generative AI tool to assist in completing the test. The results showed that there was no significant difference in students’ learning satisfaction between the Teacher Assistance Group and the Generative AI Assistance Group. Furthermore, students in the Teacher Assistance Group demonstrated higher self-efficacy. However, students achieved higher learning outcomes with the assistance of Generative AI. This study highlights the potential of Generative AI in enhancing students’ learning and test completion and provides insights into the future application scenarios of Generative AI in education.

This investigation considers the relationship between test scores and a sorting task conceptual macrostructure measure based on topic-level term-term distances as Pathfinder networks. In Study 1 (n = 255), grade 7 Chinese students completed a sorting task 1 month after the traditional in-class lessons and exam. In Study 2 (n = 220), grade 8 students completed the sorting task immediately after self-directed study of a history text. In addition, a month later 68 of the students in Study 2 were further instructed to write a short essay about this content. Study 1 results showed significant correlations between the sorting task macrostructure network measures and both lesson and unit test scores. Study 2 obtained the same significant correlations between sorting task macrostructure network measures and performance on tests. In addition, in Study 2, essay conceptual networks of historical content were better for the high prior knowledge students. Both the sorting task and the essay writing task measures can complement traditional exam measures so that conceptual knowledge structure aspects of students’ learning can be identified for formative and summative purposes.

Virtual reality (VR) is very promising for educational purposes but also presents learners with difficulties regarding orientation. Accordingly, VR environments should be designed to facilitate orientation, for example, by cueing. In a pre-registered laboratory experiment (between-subject design, 91 participants), we investigated the effects of a pedagogical agent cue compared to a light cue and a control condition without cues on search time, learning, mental representation, and perceived presence in a VR learning environment. Participants were tasked with locating tools in a virtual workshop environment, accompanied by a narration providing information about each tool. In the condition with the pedagogical agent cue, the agent was positioned close to the search objects and performed occasional gaze shifts to the object, whereas objects in the light cue condition were illuminated by a slightly reddish light. Both cueing methods significantly decreased search time but did neither affect learning outcomes nor the acquisition of the mental spatial representation of the learning environment. Additionally, the pedagogical agent cue reduced physical presence compared to the control condition and self-presence compared to both other conditions. In summary, these results imply that even with successful attention guidance, both types of cueing did not facilitate learning outcomes. It is an open question whether these effects generalize to larger VR environments and if different design choices regarding the pedagogical agent might influence presence positively, which might, in turn, lead to better learning outcomes.

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.