British Journal of Educational Technology最新文献

Self-direction has become an important skill in the 21st century. To cultivate learners with a high level of self-direction, it is necessary to diagnose their self-directed learning (SDL) ability. This study diagnosed and predicted learners' SDL ability based on their actual SDL behaviours. The study was performed in a self-directed 3D design class lasting 90 minutes. A total of 193 middle school students participated in the study. The results of the Pearson correlation analysis (p < 0.001) showed that the reported perception of SDL ability was significantly correlated with SDL behaviours. The results of the hierarchical multiple linear regression analysis showed that the SDL behaviours explained 84.9% of the variance in SDL ability (adjusted R² = 0.849, p < 0.001). Therefore, SDL behaviours had significant predictive effects on the reported perception of SDL ability. Moreover, based on the random forest algorithm, the study built an SDL ability prediction model with high performance (accuracy = 0.83, precision = 0.82, recall = 0.84) using SDL behaviours as features. The study provides evidence for the design of effective strategies to enhance SDL ability and promote SDL behaviours.

This study delves into the implications of incorporating AI tools, specifically ChatGPT, in higher education contexts. With a primary focus on understanding the acceptance and utilization of ChatGPT among university students, the research utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) as the guiding framework. The investigation probes into four crucial constructs of UTAUT—performance expectancy, effort expectancy, social influence and facilitating conditions—to understand their impact on the intent and actual use behaviour of students. The study relies on data collected from six universities in two countries and assessed through descriptive statistics and structural equation modelling techniques, and also takes into account participants' gender and study level. The key findings show that performance expectancy, effort expectancy, and social influence significantly influence behavioural intention. Furthermore, behavioural intention, when considered alongside facilitating conditions, influences actual use behaviour. This research also explores the moderating impact of gender and study level on the relationships among these variables. The results not only augment our comprehension of technology acceptance in the context of AI tools but also provide valuable input for formulating strategies that promote effective incorporation of ChatGPT in higher education. The study underscores the need for effective awareness initiatives, bespoke training programmes, and intuitive tool designs to bolster students' perceptions and foster the wider adoption of AI tools in education.

This study investigated the effects of two types of oral explanations (ie, self-explanation vs. instructional explanation) and drawing activity (no drawing vs. drawing) on video learning outcomes. These outcomes were measured by visual attention to the video (indexed by fixation time on text and diagram areas), explanation quality (indexed by personal references, concepts, and elaborations), drawing quality, behaviour patterns and overall learning performance gain. A total of 116 undergraduate and graduate students watched a 4-min video on the human body's respiratory system. They were randomly assigned to one of four conditions (explanation generation: self-explanation vs. instructional explanation × drawing activity: no drawing vs. drawing). Results indicated that without a drawing requirement, students in the self-explanation condition displayed fewer personal references and exhibited a lower learning performance gain than those in the instructional explanation condition. Conversely, when drawing was required, self-explanation students demonstrated higher drawing quality and better learning performance gain. Additionally, students in the drawing condition directed more attention to the diagram area than those in the no drawing condition. These findings suggest that in video learning (1) educators should encourage students to produce oral instructional explanations and (2) if the goal is for students to generate self-explanations, they should also be prompted to draw to bolster their self-explanation efforts.

A new mode of knowledge production, known as the ‘organic growth pattern’, has emerged within the online learning environment. It exhibits several unique features, such as iterative growth, fuelled by collective intelligence, fuzzy complexity, etc. It is more valuable and adaptable to support knowledge innovation within rapidly changing fields. To reveal its new evolutional production pattern and the interaction mechanism of different generative topics, We take a dual perspective of content and behaviour into consideration. A total of 1855 interactive data points were collected from a connectivist massive open online course. The study employed methods, such as latent Dirichlet allocation, content analysis and lag sequential analysis. Results showed that (1) the paths of knowledge innovation were complex and diverse. The process typically began with an ill-structured question and was driven by collective contribution and cognitive conflict. (2) Knowledge production involved interactive evolution and collaborative growth of multiple generative topics. (3) The four stages of knowledge production were cyclic, with sense-making acting as a bridge between way-finding and innovation. Several implications of the findings are recommended to enhance the quality of knowledge growth, such as designing ill-structured questions, providing discussion activities from easy to difficult, introducing relevant topics, taking advantage of various platforms and mobilizing learners' or participants' quality attention-related behaviours.