ETR&D

Digital distraction in education describes the interruption of learner’s concentration during academic tasks. With the increase in digital learning, there is a need for a review to synthesize research on digital distraction. This systematic review examined 26 articles using the DISCAR process (design, inclusion/exclusion, searching and screening, coding, analyzing/synthesizing, reporting) and was guided by the Technology-Personal-Environment (TPE) framework. The review examined causes and consequences, and strategies to prevent/reduce digital distraction. Measures used, modalities studied, and devices used in digital distraction research were also synthesized. Causes for digital distraction were technology distractors (51.95%), personal needs (37.66%), and instructional environment (10.39%) factors. Consequences for digital distraction included personal performance issues (66.67%), ineffective classroom instruction (23.33%), and problematic technology use (10%). Prevention strategies included classroom environment regulations (41.03%), technology controls (30.77%), and personal behavioral interventions (28.21%). The findings have implications for instructors, students, administrators, instructional designers and researchers. This systematic review adopted a multi-faceted approach to effectively mitigate digital distractions.

Good teaching requires a professional vision of the relevant dimensions of teaching quality and their interrelationships. For example, classroom management is necessary but insufficient for providing effective instructional support. Thus, teacher education should foster a multiperspective professional vision of these dimensions of teaching quality as a basis for implementing appropriate teaching actions. Research shows that professional vision can be promoted when preservice teachers analyze classroom videos. However, acquiring a multiperspective professional vision is more complex than a single perspective. Furthermore, preservice teachers have different entry levels and developmental trajectories. Individual learning requirements and the more complicated task demands can potentially be met through virtual learning environments and additional support tools implemented during video analysis. We used a video-based assessment with an open response format and investigated (1) the effect of a video-based virtual learning environment on promoting multiperspective professional vision in elementary science education and (2) the effect of additional support tools (modeling videos and prompts) implemented during video analysis. A quasi-experimental pre-post-follow-up study with 145 preservice teachers showed that a basic virtual learning environment improved participants’ multiperspective professional vision compared to an untreated control group in the short and long term. The additional support tools in the enriched virtual learning environment did increase preservice teachers’ performance even more while training professional vision but not in the post- and follow-up tests. Therefore, teacher education programs should consider the benefits of video-based virtual learning environments for an individualized promotion of professional vision. Further research on effective digital support tools is needed.

Hybrid Flexible (HyFlex) instruction offers a promising approach to enhancing flexibility and student engagement in undergraduate education. Yet, challenges related to technology, faculty readiness, and equity remain. This systematic review serves to explore current trends in the peer-reviewed literature on HyFlex learning between 2013 and 2023, specifically within the context of undergraduate education. The PRISMA principles were used as a guide to complete this review. Researchers conducted a broad search of HyFlex instruction research using five electronic databases. A total of 1,512 articles were screened as part of the systematic review. A total of 46 articles met the inclusion criteria. The results of the systematic review revealed that between 2013 and 2021 there were very few publications per year related to HyFlex instruction in undergraduate education. However, the number of publications increased significantly in 2022 and 2023. The results of the systematic review also revealed that research in HyFlex instruction in undergraduate education is a global and highly collaborative endeavor. In terms of major research trends, the systematic review also served to better understand the context in which the implementation of HyFlex instruction in undergraduate education was explored including the subject–matter of the instruction, number of participants, and research methodology. Of the 46 studies reviewed, the majority reported neutral findings, indicating that HyFlex instruction had neither a distinctly positive nor negative impact. Key findings from the research focused on HyFlex instruction design and learning strategies in undergraduate education are also discussed.

The research focus on teacher questions is justified by previous research emphasizing the essential role of questions in facilitating meaningful learning in science. Analysis of teacher questions has traditionally been based mainly on manual coding, which is extremely labour intensive. In this study we explore how both machine learning and large language models can be used for this purpose. Whereas machine learning approaches involve supervised training with extensive data, pre-trained large language models operate through prompt engineering. ​​The​ automatic speech recognition text outputs of 23 physics lessons on the same topic from 23 science teachers were analysed with variety of techniques. The results revealed that the large language model approaches improved with few-shot approaches compared to zero-shot ones. Furthermore, few shot approaches outperformed the supervised machine learning approaches, yet human- and hand-crafted approaches continue to demonstrate their relevance. ​​Implications for​ science teaching and learning are discussed.

Native language (L1) reading studies have established that text type, or genre, strongly influences reading comprehension, and narrative, or story-based, texts are easier to recall and understand than expository, or informational, texts, indicating that the comprehensibility of the content may differ depending on the genre in which it is presented. However, the effects of text genres on reading comprehension have rarely been the focus of target language (L2) reading mainly due to methodological difficulty because because L2 reading is a highly complex process that involves additional cognitive demands beyond those of L1 reading. For this problem, this investigation proposes a recent “knowledge structure (KS)” network analysis approach to visually describe and distinguish the reading processes and outcomes that may be triggered by the use of text genre in L2 reading. University mixed proficiency Korean English language learners (n = 616) were randomly assigned to one of 8 conditions that all involved a pre-reading task in L1 or L2 (as a sorting task), reading a text (either narrative or expository), then from memory a post-reading task in L1 or L2 (as a summary writing), and finally a comprehension posttest. All of the participants’ sorting and essay artifacts were converted into Pathfinder Networks, a graph-theoretic psychometric networks scaling measure, that were visually and statistically compared with each lesson text’s Pathfinder Networks they read. The findings have practical implications for L2 reading instruction. Narrative texts would be more beneficial to L2 readers who do have lower L2 proficiency because of its greater easy of processing, while expository texts would be more beneficial to L2 readers who have enough prior knowledge of to-be-learned content because of its tendency to integrate content with prior knowledge.

This paper describes the design principles and impact of an online asynchronous short course “Key Ideas in Mentoring Mathematics Teachers”, contributing to the professional development (PD) of prospective and practicing school-based mentors of mathematics teachers. The course was designed to empower mentors with knowledge about research informed practice and instil in them a welcoming stance towards mathematics education research. An Architecture of Online Engagement and a Vignette Activity Sequence approaches were employed in the design of the course, as means of supporting the participants to critically reflect on their teaching practices through engagement with topic-specific mathematics education research and on the implications for their mentoring practices. With a focus on the Vignette Activity Sequence (VAS) in particular, we discuss the potential impact and value of this approach to designing an online asynchronous professional development course in general, but also in the context of mentors of mathematics teachers. This paper’s contribution lies in the discussion of design principles of a PD course that successfully engage mentors with mathematics education research literature, while allowing them to reflect on their own practices and experiences.

This article presents the results of a multiple case study that explored the ways learning, design, and technology (LDT) scholars negotiate issues of social justice in their practice and research. Using Stake’s (2009) multiple case study, we examined the design practices of 4 scholars, looking across contexts to understand participants’ design practices, as well as the connections between those practices, their perspectives, and the local context. We found that attending to social justice requires a reflexive, reflective approach situated within understandings related to designer positionality and power. Design practices were employed to address justice-based tensions that emerged around project goals, perceived roles, and power dynamics. This study provides insight into the ways current scholars are taking up issues of social justice by engaging in a critical, responsive approach to design.

To promote deeper cognitive interactions and positive socio-emotional interactions among group members, thereby achieving high-quality collaborative outcomes, researchers have endeavored to develop conversational agents (CAs) that provide adaptive support to small groups. However, existing CAs for supporting collaborative learning struggled to integrate and analyze multi-source learning data throughout the collaborative learning process and to offer comprehensive and personalized scaffolding based on diagnostic results. To address these issues, this study designed a CA named CollaBot, based on the contingent teaching model, which has been used in the past to guide teachers on how to provide adaptive scaffolding to small groups. CollaBot integrates AI technologies, including retrieval-based models, generative AI models, and retrieval-augmented generation techniques, to offer adaptive cognitive, metacognitive, and social scaffolding to groups engaged in online collaborative learning. A randomized controlled experimental design was employed, recruiting 78 undergraduate students who were randomly divided into two groups: the experimental group (n = 39) utilized a co-writing platform with CollaBot, while the control group (n = 39) used the platform with task scripts. Results indicate that students supported by CollaBot demonstrated significantly better learning performance. In addition, both CollaBot and task scripts significantly enhanced students’ self-efficacy for writing. Furthermore, analysis of the interview data revealed both positive perceptions about CollaBot, such as aiding group members in regulating their own and the group’s learning processes and supporting the development of writing skills, as well as negative perceptions, including causing anxiety and providing ambiguous feedback. This research provides guidance for the design of CAs and offers insights into harnessing hybrid intelligence between teachers and GAI to support collaborative learning.

Badminton is one of the most popular student sports, but it is challenging to increase learning efficiency by observing learners using the naked eye without assistive tools. Therefore, this study proposes an auto-feedback badminton teaching app integrated with an auto-feedback-based WISER model. Learners could conduct self-learning with the functions of the badminton teaching app, including automatic grading, automatic feedback, and professional player demonstration videos. This study adopts a quasi-experimental design. The proposed App and model were applied in the experimental group, while the control group used traditional teaching and mobile devices with a video recording function. Both groups of learners learned serve and clear skills for 6 weeks each. The increase in post-test scores of the experimental group was significantly higher than that of the control group, though post-test scores of both groups were significantly higher than pre-test scores. The proposed method demonstrates its efficiency for self-learning, as confirmed by interviews. Future work can apply the integration of motion recognition and the auto-feedback-based WISER model across diverse educational disciplines to personalize students’ learning.

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.