Educators use psychological knowledge about categories to adapt their teaching to learners. Learning these categories usually requires understanding the underlying relational structures. To learn relational categories, learning tasks should effectively foster generalization and discrimination processes. Previous research indicates that tasks requiring learners to generate their own examples are less effective for learning relational categories and more likely to elicit overestimation of learning than tasks requiring learners to classify provided examples. We conducted an experiment with N = 104 students in degree programs that encompass educational science to investigate whether the disadvantage of generating examples in contrast to classifying examples can be mitigated by deliberately promoting generalization and discrimination processes during learning. The experiment was designed to reflect an authentic context for learning relational categories and to assess how students apply these categories in real-world settings. The results showed that despite the deliberate promotion of generalization and discrimination processes, classifying provided examples was still more effective for learning relational categories. Classifying examples presumably fosters a high level of cognitive engagement while simultaneously providing instructional guidance. In contrast, generating examples may offer insufficient guidance and thereby restrict learning. Moreover, students who generated examples during learning not only overestimated their learning but the overestimation also persisted throughout the knowledge test and after its completion. Based on the findings, educators should consider using classification tasks rather than generation tasks when teaching relational categories while also taking into account both learning and metacognitive processes.
Generative strategies such as drawing, self-explaining, and explaining to others promote active learning, but evidence on their combined effects is mixed, and the role of prior knowledge remains unclear. The authors investigated the effects of drawing, self-explaining, and explaining to others, alone and in combination, in a multiple representation domain, chemistry. A total of 134 undergraduate students who had completed introductory chemistry were randomly assigned to one of six conditions using a 2 (drawing/no drawing) x 3 (self-explaining, explaining to others, no explaining) experimental design. Participants completed a pretest, studied learning materials while applying their assigned strategies, revised their products against instructor-provided examples, and then completed a posttest. Results showed no significant main effects of drawing or explaining. However, prior knowledge moderated the impact of conditions on the posttest: drawing benefitted students with moderate prior knowledge, while combining drawing with explaining to others was particularly effective for students with lower prior knowledge. These findings highlight boundary conditions for dual generative strategies and underscore the importance of aligning strategy use with learner characteristics.
Controlled experiments are essential for causal reasoning, yet students struggle with the control-of-variables strategy (CVS). Self-generated errors, vicarious errors, or a combination could provide learning opportunities; however, error awareness, motivation, affect, and cognitive load may shape whether students benefit from different types of errors. This study examines the effect of self-generated, vicarious, combined, or no errors on CVS learning, and investigates affect, motivation, cognitive load, and error awareness as potential mediators. 255 seventh- and eighth-grade students (40
The present study investigated the effects of instructor presence in a multimedia learning situation where seeing the instructor was deemed relevant to the learning content and requirements, in this case a language learning setting. Fifty Japanese university students were asked to learn from two videos on the four Mandarin tones, one with the instructor video present and one without. While viewing the videos, participants’ eye movements and skin conductance level were recorded, and measurements of learning performance and perceptions about learning were taken afterwards. Results showed no significant difference in overall learning performance when comparing the conditions with and without instructor presence, but learners indicated that they were more satisfied and perceived more learning when the instructor video was included. This suggests that instructor presence can influence learners’ attention and perceived learning. However, in the present study, videos of instructors modeling tone production without providing other cues to tone production did not lead to measurable improvements in learning outcomes, even though learners attended to the instructor videos and perceived them as helpful. These findings indicate that instructor presence is unlikely to improve learning outcomes unless the instructor’s visible behavior provides information learners can directly use for the task at hand. More broadly, the benefits of instructor presence may depend not only on the alignment between the information conveyed and the requirements of the learning task, but also on learners’ ability to recognize and use that information effectively.
Socially shared regulation of learning (SSRL) can promote high-level collaborative learning. However, the sequential interplay between sociocognitive, socioemotional and motivational group-level challenges and SSRL strategies remains unclear, particularly in comparative analyses of pre-service and in-service teachers engaged in computer-supported collaborative problem-solving tasks. To address this gap, 33 pre-service and 33 in-service teachers were video-recorded in face-to-face settings while working in groups of three on collaborative problem-solving tasks within a virtual simulation on a desktop computer. A total of 25.7 h of video data were analysed using a theory-driven coding scheme that captured sociocognitive, socioemotional and motivational challenges, as well as collaborative regulatory activities grounded in the SSRL framework. Process mining was employed to visualise and compare sequential patterns of challenges and regulation. The findings indicate that pre-service teachers exhibited stronger sequential patterns linking planning, monitoring and controlling activities, whereas in-service teachers demonstrated more pronounced sequences involving monitoring and planning following socioemotional and motivational challenges. These differences reflect distinct tendencies in how collaborative learning activities were organised over time, rather than differences in effectiveness or learning outcomes. This study contributes to the learning sciences by extending SSRL research and offering theoretical and methodological insights into analysing collaborative learning as a dynamic process. From a practical perspective, the findings inform the pedagogical design of collaborative learning and teachers’ professional development by highlighting the need for profile-sensitive scaffolds that support shared planning, monitoring and adaptive regulation.
