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The role of cognitive engagement in promoting deep learning is well established. This deep learning fosters attributes of success such as self-efficacy, motivation and persistence. However, the traditional chalk-and-talk teaching and learning environment is not conducive to engage students cognitively. The biggest impediment to implementing an environment for deep learning such as active-learning is the limited duration of a typical class period most of which is consumed by lecturing. In this paper, best practices and strategies for cognitive engagement of students in the classroom are discussed. Several lower level math and aerospace engineering courses were redesigned and offered during the academic year at a historically black university. The learning strategies in these redesigned courses included the “flipped” pedagogical model which allowed the integration of the active-learning strategy in the classroom. The research study is to determine the impact of these redesigned courses on student academic performance and persistence in STEM courses. The efficacy of the design of the flipped approach was also investigated. A between-group quasi-experimental research design was used for comparing student academic performance in traditional classroom (control group) and redesigned classroom (intervention group). A within-subject, repeated measures design was also used to assess the impact on the students’ self-regulated learning. A validated instrument was used to measure the effect of the redesigned learning environment on the motivational beliefs and self-regulated learning. Data on the academic performance of the students were collected. Analyses of these data indicated a significant impact on student academic performance. A positive change in student motivation and self-regulated learning was observed. Data analysis also validated the design of the intervention. This research is supported by NSF Grant# 1712156.
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Be back in 5 minutes: Exploring correlations between short breaks with student performance
This study investigates the influence of short breaks on self-regulated learning within an online learning platform and their impact on student attrition and performance outcomes. The research focuses on 7th-grade students¿½f mathematics performance using an online learning platform in 2016. Building on this goal, we conducted two regression analyses to investigate the number of students who stopped out and returned to finish the assignments and explore the duration of breaks between questions among those students who returned to complete the assignments. Specifically, the study analyzes session durations, break intervals, and their correlation with student performance after stopping out. Results reveal that, despite prevalent breaks between problems, break duration does not significantly affect learning performance. The findings provide correlations between short breaks and the completeness and correctness within self-regulated learning contexts. The study emphasizes the need for an exploration of predicting diverse assignment difficulty, break duration, and completeness. This research contributes valuable insights into self-regulated learning on online learning platform and predicting the potential for student success.
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- Award ID(s):
- 2331379
- PAR ID:
- 10531982
- Editor(s):
- Benjamin, Paaßen; Carrie, Demmans Epp
- Publisher / Repository:
- International Educational Data Mining Society
- Date Published:
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Attribution 4.0 International
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.5281/zenodo.12729948
