ABSTRACT Undergraduate research experiences (UREs) and course-based UREs (CUREs) promote students' scientific self-efficacy growth. Yet, how self-efficacy develops during research is not understood. Furthermore, what students do during research varies in ways that likely affect self-efficacy development. We sought to address these knowledge gaps by collecting scientific self-efficacy data from CURE and URE students at nine universities at the beginning, middle, and end of a single term of research. We leveraged a theoretical advancement, latent state-trait theory-revised, to disaggregate the components of students' self-efficacy into stable or trait-like self-efficacy and dynamic or state-like self-efficacy. We determined that students' scientific self-efficacy was moderately stable during their research, with the most malleable component being beliefs in their abilities to figure out data collection and explain results. We also surveyed students ∼45 times throughout their research experience to test the extent to which research hours and types of research tasks contributed to self-efficacy development. We found that students who completed more analytic tasks experienced significantly more self-efficacy growth than students who completed other types of tasks, while time spent on research was not influential. Our results illustrate the importance of engaging students in analytic tasks during CUREs and UREs for fostering their self-efficacy development. Highlight for table of contents Using latent state-trait theory-revised, we found that students' scientific self-efficacy was more stable than malleable over one research term. Beliefs about data collection and explaining results were most dynamic. Conducting more analytic tasks fostered self-efficacy, while the time spent and completion of other tasks had no effect.

We tested two potential ways to help students learn from feedback on their problem-solving errors in physics: (a) design the feedback to align with established principles of multimedia learning (Experiment 1), and/or (b) explicitly prompt students to generate self-explanations of their errors (Experiment 1 and 2). Experiment 1 (n = 131) found no effect of feedback design and limited effects of self-explaining: Self-explaining improved error correction and near (but not far) transfer performance for only one of the feedback conditions. In Experiment 2 (n = 110), we tested a more explicit form of self-explanation support. Students who received scaffolded self-explanation prompts generated higher quality explanations, corrected more errors, and performed better on the near (but not far) transfer test than those who received standard self-explanation prompts or a control group who received no prompts. Students receiving standard self-explanation prompts did not significantly outperform the control group. Overall, this study suggests scaffolded self-explanation prompts help students correct and avoid similar problem-solving errors in the future. © 2024 The Author(s)

This study explored why students rarely create drawings when learning from science texts despite potential learning benefits. Undergraduates (n = 114) studied a 10-part text on the human respiratory system and took notes by choosing their own strategies (free choice group) or by choosing to create a drawing or write a verbal summary (forced choice group). Other students were instructed to create drawings (draw group) or write summaries (summarize group). All students then completed a series of post-tests. The forced choice group chose to draw significantly more frequently than the free choice group; however, both groups still overwhelmingly chose summarizing. Participants across all groups reported lower prior experience, lower expectancies for success, lower perceived value, and higher perceived cost of drawing compared to summarizing. Students' prior experiences and beliefs about drawing were also associated with how frequently they chose to draw, providing implications for future instructional interventions.

Errors are inevitable in most learning contexts, but under the right conditions, they can be beneficial for learning. Prior research indicates that generating and learning from errors can promote retention of knowledge, higher-level learning, and self-regulation. The present review proposes an integrated theoretical model to explain two major phases of learning from self-generated errors: the Generating Errors (GE) phase, which contributes to learning via semantically related prior knowledge activation, and the Detecting and Correcting Errors (DCE) phase, which contributes to learning via self-explanation when processing and comparing one's responses with provided reference information to promote high-quality internal feedback. Our model identifies general design principles that support each phase based on prior empirical research. We conclude by identifying research gaps and future directions regarding specific design features of the GE and DCE phases and the role of students' emotion, motivation, and individual differences in learning from errors.