Abstract BackgroundPrevious research has demonstrated that quizzing can improve self‐regulation processes and learning performances. However, it remains unclear whether quizzes in multimedia material bring similar benefits, and whether interindividual differences such as working memory capacity (WMC) modulate quizzing effects.AimsThis study aimed to examine the effects of embedded quizzes (multiple quizzes within the learning sequence) with feedback during a multimedia learning sequence on self‐regulated processing (monitoring and control) and learning performances on repeated (material already tested) and new (untested) questions, and possible WMC moderation on performances.Sample and MethodsParticipants were 59 students who learned a multimedia neuroscience course module in either a no‐quiz or embedded quizzes condition. With regard to self‐regulated processing, control was assessed with eye‐tracking measures (fixation durations and transitions between texts and illustrations), and monitoring by comparing self‐reported judgements of learning with actual performances (calibration). At the end, students' WMC was measured, and learning performances were assessed with repeated and new questions.ResultsQuizzing improved learning performances on repeated and new questions, and enhanced monitoring (better calibration). Students with low WMC benefited more from quizzing for new questions than those with high WMC. However, contrary to our hypothesis, the embedded quizzes group allocated less time to the learning than the no‐quiz group.ConclusionsEmbedded quizzes are relevant tools to help students recalibrate their monitoring process and foster more effective self‐regulated learning. Results confirm that the use of quizzes can be recommended to improve the learning of multimedia material, especially among students with low WMC.

Abstract BackgroundScience, Technology, Engineering, and Mathematics (STEM) education in Asian universities struggles to integrate Knowledge, Skills, and Attitudes (KSA) due to large classes and student reluctance. While ChatGPT offers solutions, its conventional use may hinder independent critical thinking.ObjectivesThis study introduces PA‐GPT, using ChatGPT as a “virtual peer” in peer assessments to promote active learning and enhance knowledge, higher‐order thinking skills (HOTS), and attitudes—the core of KSA in STEM.MethodsA randomised controlled trial involved 61 first‐year engineering students (43 males, 18 females) from a university in Southern Taiwan enrolled in “Network Embedded Systems and Applications.” Participants, all with prior ChatGPT experience but no programming background, were purposively sampled. They were randomly assigned to the experimental group (n = 31) using PA‐GPT or the control group (n = 30) using traditional ChatGPT. Over 8 weeks, data were collected using pre‐ and post‐tests: a knowledge construction test (20 items, α = 0.85); a HOTS scale (α = 0.78–0.83) measuring critical thinking, problem‐solving, and creativity; and the S‐STEM questionnaire (α >0.80) assessing attitudes towards STEM subjects and 21st‐century learning. ANCOVA analysed the data, controlling for pre‐test scores, and Levene's test checked homogeneity of variances.Results and ConclusionsANCOVA results showed that PA‐GPT significantly outperformed traditional ChatGPT in enhancing knowledge construction (F = 9.89, p = 0.002), critical thinking (F = 37.00, p < 0.001), problem‐solving (F = 9.40, p = 0.003), creativity (F = 7.22, p = 0.009), and attitudes towards mathematics (F = 25.52, p < 0.001), engineering/technology (F = 16.06, p < 0.001), and 21st‐century learning (F = 26.38, p < 0.001). These findings demonstrate that PA‐GPT effectively addresses challenges in student engagement and HOTS development in STEM education by simulating peer interactions. Peer Assessment with ChatGPT (PA‐GPT) promotes active learning and self‐reflection, potentially revolutionising AI‐assisted education in large class settings. This study provides pioneering evidence for the effectiveness of AI‐driven peer assessment in enhancing comprehensive STEM competencies, offering a promising direction for future educational technology integration.

Abstract BackgroundThe unprecedented access to information in the 21st century entails a deep understanding of information and communication technology (ICT)‐related factors in education and their impacts on learning and teaching. The role of attitudes towards ICT is a proven factor in student achievement. However, there is no consensus about the direction of this impact.ObjectivesThis study aimed to explore the relationship between student attitudes towards ICT including interest in ICT (INTICT), perceived ICT competence (COMPICT), perceived autonomy related to ICT use (AUTICT) and ICT in social interaction (SOIAICT), and their achievement scores for mathematics, reading, and science by analysing student‐, school‐, and country‐level factors hierarchically in the quartiles of performance.MethodsThe study group was determined as 300,279 15‐year‐old students from 21,903 schools in 53 countries. Descriptive and correlational analyses were carried out in addition to the hierarchical linear modelling (HLM) employed to reveal the relationship between attitudes towards ICT and academic achievement in mathematics, reading, and science through the investigation of student‐, school‐, and country‐level factors hierarchically in the quartiles of performance.Results and ConclusionsThe results detected moderate to weak relationships among the constructs of INTICT, COMPICT, AUTICT and SOIAICT. Based on the HLM findings, complex interactions between ICT attitudes and academic performance were identified with a tendency towards positive associations between specific attitude components and high achievement levels, particularly in mathematics and science. The research identified the significance of considering student‐, school‐, and country‐level factors for high, above‐average, below‐average, and low achievers to comprehensively understand the role of ICT attitudes in shaping educational outcomes.TakeawaysPolicymakers should design educational policies, subject‐specific strategies and ICT‐related interventions by considering the multifaceted factors influencing student performance across different subjects and achievement quartiles. Particularly at the school and country levels, contextual factors should be evaluated when designing interventions to support low achievers.

Abstract Background This study examined the effect of computer‐supported collaborative argumentation (CSCA) on secondary school students' understanding of socio‐scientific issues (SSI). Engaging students in collaborative argumentation is known to help with deepening their understanding of SSI. Methods In this study, a mixed‐method design is used to investigate 84 students' collaborative argumentation processes and outcomes. The statistical analysis, epistemic network analysis and qualitative uptake analysis results showed that CSCA was effective in supporting secondary school students' evidence‐based argumentation skills on SSI. Findings and Conclusion Several cases were presented to show how students engaged in CSCA to explore meaningful learning opportunities and how CSCA helped students' learning on SSI. Implications The findings provided insights for future innovative teaching and learning SSI in authentic classroom settings.

Abstract BackgroundBoth learning and problem solving are major goals of complex problem solving in engineering education. The order of knowledge construction and problem solving in learning through problem solving, however, has not been explained in current literature.ObjectivesTo understand their relationships, this study compared the effects of different goal type orders on collaborative simulation‐based inquiry in engineering problem solving.MethodsIn the study, 24 engineering undergraduate students worked in pairs to solve a wind tunnel problem using a computer simulation application. Process mining and epistemic network analysis were used to model the inquiry process and identify inquiry patterns based on the log data and discussion discourse.Results and ConclusionsThe results showed the goal type of first assigned subtask influenced the inquiry strategies of medium‐performance learners most. In addition, the goal type of first assigned subtask had influence on learners' inquiry process. The learners assigned with a problem‐solving goal subtask first (Problem solving goal‐Learning goal condition) had more simulation trials and centered their inquiry discourse around clarification while the learners assigned with a learning goal subtask first (Learning goal‐Problem solving goal condition) connected reflection with other inquiry phases more often. The findings of this study suggested assigning a learning goal subtask first in simulated‐based complex engineering problem solving could be used as an idea‐centered scaffolding, especially for medium‐performance learners. However, low‐performance learners would experience a “situate knowledge paradox” and needs extra guidance. It is important to monitor the knowledge construction process in complex problem solving, even when seeking a solution is the major task.

Abstract BackgroundLearning analytics dashboards are increasingly being used to communicate feedback to learners. However, little is known about learner preferences for dashboard designs and how they differ depending on the self‐regulated learning (SRL) phases the dashboards are presented (i.e., forethought, performance, and self‐reflection phases) and SRL skills. Insight into design preferences for dashboards with different reference frames (i.e., progress, social, internal achievement and external achievement) is important because the effectiveness of feedback can depend upon how a learner perceives it.ObjectiveThis study examines workplace learner preferences for four dashboard designs for each SRL phase and how SRL skills relate to these preferences.MethodsSeventy participants enrolled in a chemical process apprenticeship program took part in the study. Preferences were determined using a method of adaptive comparative judgement and SRL skills were measured using a questionnaire. Preferences were tested on four dashboard designs informed by social and temporal comparison theory and goal setting theory. Multinomial logistic regressions were used to examine the relationship between dashboard preferences and SRL.Results and ConclusionsResults show that the progress reference frame is more preferred before and after task performance, and the social reference frame is less preferred before and after task performance. It was found that the higher the SRL skill score the higher the probability a learner preferred the progress reference frame compared to having no preference before task performance. The results are consistent with other findings, which suggest caution when using social comparison in designing dashboards which provide feedback.

Abstract Background Traditionally, understanding students' learning dynamics, collaboration, emotions, and their impact on performance has posed challenges in formative assessment. The complexity of monitoring and assessing these factors have often limited the depth and breadth of insights. Objectives This study aims to explore the potential of multimodal learning analytics as a formative assessment tool in math education. The focus is on discerning how collaborative discourse behaviours and emotional indicators interplay with lesson evaluation performance. Methods Using undergraduate students' multimodal data, which includes collaboration data, facial behaviour data, and emotional data, the study explored the patterns of collaboration and emotion. Through the lens of multimodal learning analytics, we conducted exploratory data analysis to identify meaningful relationships between specific types of collaborative discourse, facial expressions, and performance indicators. Moreover, the study evaluated a machine learning model's potential to predict target learning outcomes by integrating data from multiple channels. Results The analysis revealed key features from both discourse and emotion data as significant predictors. These findings underscore the potential of a multimodal analytical approach in understanding students' learning process and predicting outcomes. Conclusions The study emphasizes the importance and feasibility of a multimodal learning analytic approach in the context of math education. It highlights the academic and practical implications of such an approach, along with its limitations, pointing towards future research directions in this area.

Abstract BackgroundExamining student attention in physical classrooms is crucial, but it faces challenges due to the lack of accurate monitoring. Constraints posed by device limitations and the design of educational materials impede the integration of eye‐tracking technology in these settings.ObjectivesThis study aims to (1) develop a wearable eye‐tracking system specifically designed to monitor students' eye movements and gaze points on the projector screen within a physical classroom setting; (2) explore the impact of instructor gestures (by compare live instruction by an instructor and video‐recorded instruction) on student attention and examine the effectiveness of directing students' attention from text to image through instructor intervention.MethodsAn innovative wearable eye‐tracking system was developed to monitor learners' eye movements within the physical classroom. Twenty‐five students participated in the experiment, which included two approaches: classroom lectured by the instructor and by a video presentation.Results and ConclusionsThe results indicate that participants exhibit a stronger inclination to allocate additional time to text content than image content when receiving instruction through video presentations with a laser pointer in the physical classroom. This tendency can be attributed to the participants' requirement for longer reading and comprehension time in the absence of an instructor. Additionally, the instructor's gestures and body movements significantly impacted participants' fixation on text slides compared to the image slides. The heatmap analyses support these findings and further indicate that participants focus on the instructor's face rather than other body parts.TakeawaysThe wearable eye‐tracking technology developed in this study holds promise for future educational research, offering further exploration and analysis opportunities.

Abstract BackgroundThis study presents a Robot‐Inspired Computer‐Assisted Adaptive Autism Therapy (RoboCA3T) focusing on improving joint attention and imitation skills of children with autism spectrum disorder (ASD). By harnessing the inherent affinity of children with ASD for robots and technology, RoboCA3T offers a therapeutic environment designed to maximise engagement and facilitate effective skill development. It harnesses the advantages of Robot‐Assisted Therapies (RATs) by employing robot avatars and integrating them with Computer‐Assisted Therapies (CATs) within a web‐based solution. The integration of automatic gaze and pose detection algorithms within RoboCA3T addresses the challenge posed by potential human error and observation bias in assessing the child's progress, thereby ensuring accurate results. This research responds to the need for more effective, technology driven therapies for autism, filling gaps in existing methods.ObjectivesThe primary goal of this research is to create a robot inspired computer assisted adaptive autism therapy that maximises engagement and enhances joint attention and imitation skills.MethodsThe study involves 11 ASD children with 30 sessions (divided into two halves) per module over eight months, comprising 660 experimental trials, 110 familiarizations, and 110 follow‐up sessions. The joint attention module evaluates the subject's gaze pattern using WebGazer for gaze detection in response to four least‐to‐most robot‐generated cues. The imitation module utilises robot‐generated pose for comparing subjects' imitated actions using Tensorflow Lite for pose estimation.Results and ConclusionsThe effectiveness of therapy was substantiated by comparing Childhood Autism Rating Scale (CARS) scores before and after intervention. Significant improvements were noted between the first and second therapy halves, validated by Wilcoxon signed‐rank tests (p < 0.01) and spearman's correlation analysis, reinforcing the observed improvements in joint attention and imitation skills.