British Journal of Educational Technology最新文献

A core focus of self-regulated learning (SRL) research lies in uncovering methods to empower learners within digital learning environments. As digital technologies continue to evolve during the current hype of artificial intelligence (AI) in education, the theoretical, empirical and methodological nuances to support SRL are emerging and offering new ways for adaptive support and guidance for learners. Such affordances offer a unique opportunity for personalised learning experiences, including adaptive interventions. Exploring the application of adaptivity to enhance SRL is an important and emerging area of research that requires further attention. This editorial introduces the contributions of seven papers for the special section on adaptive support for SRL within digital learning environments. These papers explore various themes related to enhancing SRL strategies through technological interventions, offering valuable insights and paving the way for future advancements in this dynamic area.

There is a global trend in the increased adoption of e-assessment in school classrooms to enhance learning. Teachers, as classroom-based assessment designers and implementers, play a vital role in such assessment change. However, little is known about school teachers' classroom-based e-assessment practices and the underlying reasons. To address this research gap, this study identified the factors influencing Hong Kong primary school teachers' e-assessment practices underpinned by the theory of planned behaviour (TPB). A large-scale survey was issued to 878 teachers via Qualtrics. Structural equation modelling (SEM) analysis shows that primary school teachers' intentions of using e-assessment and perceived behavioural control of it were the two strongest factors predicting their e-assessment practices in a general way. Specifically, teachers' intentions outweighed perceived behavioural control in determining their use of alternative e-assessment tasks and e-feedback, but this reversed for e-tests/exercises. The impact of perceived behavioural control was consistent across the three types of e-assessment practices. Furthermore, teachers' attitudes significantly influenced their intentions to use alternative e-assessment tasks, while subject norms primarily predicted their intentions to use e-feedback. The findings have implications for primary schools to take countermeasures to facilitate the successful implementation of e-assessment at the classroom level.

Even highly motivated undergraduates drift off their STEM career pathways. In large introductory STEM classes, instructors struggle to identify and support these students. To address these issues, we developed co-redesign methods in partnership with disciplinary experts to create high-structure STEM courses that better support students and produce informative digital event data. To those data, we applied theory- and context-relevant labels to reflect active and self-regulated learning processes involving LMS-hosted course materials, formative assessments, and help-seeking tools. We illustrate the predictive benefits of this process across two cycles of model creation and reapplication. In cycle 1, we used theory-relevant features from 3 weeks of data to inform a prediction model that accurately identified struggling students and sustained its accuracy when reapplied in future semesters. In cycle 2, we refit a model with temporally contextualized features that achieved superior accuracy using data from just two class meetings. This modelling approach can produce durable learning analytics solutions that afford scaled and sustained prediction and intervention opportunities that involve explainable artificial intelligence products. Those same products that inform prediction can also guide intervention approaches and inform future instructional design and delivery.

Gamification, characterized by the integration of game design elements into non-game environments, has gained popularity in classrooms due to its potential for increased engagement and enjoyment compared to traditional lecture-based teaching methods. While students generally exhibit positive attitudes towards gamification, its impact on academic achievement remains a subject of debate. This study employed a meta-analysis approach to examine the overall influence of gamification on students' academic performance. The sample comprised 22 experimental studies conducted between 2008 and 2023, comparing the effects of gamified and non-gamified classes. Utilizing a random effects model, the results revealed a moderately positive effect of gamification on student academic performance (Hedges's g = 0.782, p < 0.05). The paper further discussed the outcomes of various moderator analyses, providing valuable insights into the selection and utilization of game design elements, as well as considerations specific to different educational stages.

Single-case experimental designs (SCEDs) may offer a reliable and internally valid way to evaluate technology-enhanced learning (TEL). A systematic review was conducted to provide an overview of what, why and how SCEDs are used to evaluate TEL. Accordingly, 136 studies from nine databases fulfilling the inclusion criteria were included. The results showed that most of the studies were conducted in the field of special education focusing on evaluating the effectiveness of computer-assisted instructions, video prompts and mobile devices to improve language and communication, socio-emotional, skills and mental health. The research objective of most studies was to evaluate the effects of the intervention; often no specific justification for using SCED was provided. Additionally, multiple baseline and phase designs were the most common SCED types, with most measurements in the intervention phase. Frequent data collection methods were observation, tests, questionnaires and task analysis, whereas, visual and descriptive analysis were common methods for data analysis. Nearly half of the studies did not acknowledge any limitations, while a few mentioned generalization and small sample size as limitations. The review provides valuable insights into utilizing SCEDs to advance TEL evaluation methodology and concludes with a reflection on further opportunities that SCEDs can offer for evaluating TEL.

Deep neural networks are increasingly employed to model classroom dialogue and provide teachers with prompt and valuable feedback on their teaching practices. However, these deep learning models often have intricate structures with numerous unknown parameters, functioning as black boxes. The lack of clear explanations regarding their classroom dialogue analysis likely leads teachers to distrust and underutilize these AI-powered models. To tackle this issue, we leveraged explainable AI to unravel classroom dialogue analysis and conducted an experiment to evaluate the effects of explanations. Fifty-nine pre-service teachers were recruited and randomly assigned to either a treatment (n = 30) or control (n = 29) group. Initially, both groups learned to analyse classroom dialogue using AI-powered models without explanations. Subsequently, the treatment group received both AI analysis and explanations, while the control group continued to receive only AI predictions. The results demonstrated that teachers in the treatment group exhibited significantly higher levels of trust in and technology acceptance of AI-powered models for classroom dialogue analysis compared to those in the control group. Notably, there were no significant differences in cognitive load between the two groups. Furthermore, teachers in the treatment group expressed high satisfaction with the explanations. During interviews, they also elucidated how the explanations changed their perceptions of model features and attitudes towards the models. This study is among the pioneering works to propose and validate the use of explainable AI to address interpretability challenges within deep learning-based models in the context of classroom dialogue analysis.