Although prior research has demonstrated the benefits of prequestions for learning, most studies have focused on overall effects or individual design features, leaving the interaction between question depth and placement underexplored. The present study employed a 2 (depth: deep vs. shallow) × 2 (placement: before learning vs. interspersed) between-subjects design. One hundred and twenty college students were randomly assigned to four conditions and learned about the fundamental characteristics of perception through a video lecture. Retention and near transfer tests were used to assess learning outcomes, and cognitive load was also measured. Results indicated that interspersed prequestions significantly enhanced retention performance compared to prequestions presented before learning, whereas deep prequestions significantly enhanced near transfer performance compared to shallow prequestions. A significant interaction between depth and placement was observed for near transfer. The advantage of deep over shallow questions emerged only when the questions were interspersed, and the advantage of interspersed over before-learning placement emerged only for deep questions. No significant differences in cognitive load were observed across conditions. These findings highlight the importance of considering both design features when implementing prequestions and offer preliminary guidance for the design of prequestions in video-based instruction.
Blended learning plays an important role in the digital transformation of higher education and requires students to regulate their learning with greater independence. Although learning analytics studies have increasingly used clickstream data to identify self-regulated learning (SRL) tactics and strategies, many rely on platform-specific behavioral categories whose theoretical meaning is not always explicit. Moreover, less is known about how SRL strategies change across the structured phases of compulsory courses. This study examined the online behaviors of first-year undergraduates in an institution-initiated blended general education course at a Chinese university. Drawing on Pintrich's SRL framework, five learning tactics were identified and consolidated into four strategies, by using sequence analysis and clustering. The findings indicate that students frequently relied on strategies characterized by limited regulation and oriented toward rapid task completion. Strategy use also followed distinct phase-specific trajectories, that is many students began with cognitively demanding approaches, shifted mid-course to lower-order strategies, and then returned to deeper forms of engagement during exam preparation. Although a considerable proportion of students shifted toward strategies characterized by lower levels of regulation, upward shifts toward elaboration-oriented regulation were associated with better academic performance. These findings suggest that SRL dynamics in blended learning are shaped by both learners' regulatory choices and the temporal structure of course design, highlighting the need for instructional scaffolding that supports the sustained use of higher-order strategies.
The increasing adoption of online teaching in higher education (HE), especially in the post-pandemic era and with the rise of artificial intelligence, highlights the need to better understand how online teaching expertise evolves. Recognising that such developments may fluctuate across contexts and time, this study employed an experience sampling method (ESM) to investigate both within-teacher variability and between-teacher differences in time-variant predictors of perceived expertise development in online teaching. The sample consisted of 25 university teachers, representing a substantial size for intensive longitudinal research using ESM in HE. Participants provided repeated diary entries via a mobile app before and after teaching or planning sessions. These entries captured emotional valence, digital skills, reflection and perceived development of online teaching expertise. Bayesian cumulative mixed-effects analyses revealed that state positive emotions during teaching and planning sessions were positively associated with perceived development of online teaching expertise. Momentary perceptions of digital skilfulness during sessions were more influential than stable trait-level differences between individuals. Furthermore, teachers who engaged more frequently in reflective practices across sessions reported greater cumulative expertise development. These findings underline the importance of withinteacher dynamics in emotions and digital skills and between-teacher differences in reflection in shaping the development of online teaching expertise. The implications of the study and future research are discussed.
Conversational agents are shifting from solo tutors to teammates in online project-based learning, yet little is known about how design features shape dynamics. Drawing on self-regulated learning and volitional control theory, we examine how ChatGPT affects team learning. A survey of 356 students nested in 48 teams tests a model linking AI explainability to intention to continue learning via perceived social presence, with team perceived virtuality and cohesion as moderators. Explainability heightens the agent's social presence, and this copresence boosts intention to continue learning. High virtuality and cohesion strengthen this pathway. Social presence is pivotal, and we offer design recommendations for embedding explainable AI in teamwork.
