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Self-regulation, a skillset involving taking charge of one’s own learning processes, is crucial for workplace success. Learners develop self-regulation skills through reflection where they recognize weaknesses and strengths by employing metacognitive strategies: planning, monitoring, and evaluating. Use of anchors assists learners’ engagement in reflection. The purpose of this work was to gain insight into students’ use of anchors when reflecting on their learning. The two research questions were: (1) To what extent do students link their self-evaluation and learning objective (LO) self-ratings to their reflections? and (2) What dimensions and level of metacognitive strategies do students use in their self-evaluation of and reflections on weekly problem-solving assignments? Data were upper-division engineering students’ anchors (self- evaluations, LO self-ratings) and reflection responses for one assignment. Self-evaluations and reflections were analyzed for the presence of references to LOs. The number of students who linked the anchors to their reflection were tabulated. Additionally, a revised a priori coding scheme was applied to students’ written work to determine type and level of metacognitive strategies employed. Few students linked both anchors to their reflections. Students employed low to medium levels of the metacognitive strategies in their self-evaluations and reflections, even when they linked their anchors and reflections. The evaluating strategy dominated in the self- evaluations, while planning and monitoring dominated in the reflections. Students have limited understanding of the use of anchors to guide their reflection responses. Students overall level of engagement in the metacognitive strategies indicates a need for formal instruction on reflection.
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The Reflective Modeling Practitioner: Promoting Selfregulation and Self-confidence in Computational Modeling and Simulation Practices
This study investigated the effects of a team-based modeling intervention that implemented reflective practices to support students’ self-regulated learning in the context of modeling assignments. We used a mixed method design to answer the three research questions: (1) What metacognitive strategies do students, organized in teams, implement when solving computational modeling assignments? (2) What are students’ levels of performance in solving computational modeling assignments in teams? (3) What are the relationships between teams’ level of confidence and their implemented metacognitive strategies and level of performance in the computational modeling assignment? The learning intervention was guided by a reflective modeling practitioner model, bringing together modeling practices with elements of selfregulated learning. The results illustrate students’ levels of self-reported confidence in three levels, showing that from the twelve teams studied, seven reported an increase in confidence as the project progressed, three reported a decrease in their confidence, and two reported an initial struggle, but their confidence increased as they completed the assignment. The implications relate to the learning interventions in the team modeling activity that can influence the teams’ reported selfconfidence, which can impact the skills students acquire and the strategies they use when faced with challenges.
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- Award ID(s):
- 2120200
- PAR ID:
- 10510212
- Publisher / Repository:
- IJEE
- Date Published:
- Journal Name:
- International Journal of Engineering Education
- Volume:
- 40
- Issue:
- 1
- ISSN:
- 0949-149X/91
- Page Range / eLocation ID:
- 179-195
- Subject(s) / Keyword(s):
- computational modeling and simulation engineering education self-regulated learning teamwork
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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