Self-Regulated Learning Strategies of Engineering College Students While Learning Electric Circuit Concepts with Enhanced Guided Notes
Why this work is in the frame
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Bibliographic record
Abstract
The current study evaluated engineering college students’ self-regulated learning (SRL) strategies while learning electric circuit concepts using enhanced guided notes (EGN). Our goal was to describe how students exercise SRL strategies and how their grade performance changes after using EGN. Two research questions guided the study: (1) To what degree students’ grade performance change after using the EGN?; and (2) To what degree students’ SRL profiles change after using the EGN? The subjects for this study were 97 engineering students enrolled in the Fundamental Electronics for Engineers course at a university in Utah. A survey instrument developed using Butler and Cartier’s SRL model was used to capture SRL strategies with a focus on the sixth feature which includes including planning, monitoring, and regulating. Regular examinations and the DC/AC conceptual inventory were used to assess grade performance. Descriptive statistics, independent and paired t-tests, and a cluster analysis technique were used to analyze survey data. A phenomenological data analysis was used to analyze interview transcripts to support findings from questionnaire data. The findings revealed an improvement on students’ grade performance. Data analysis of the SRL survey revealed that students’ had different SRL profiles. Students in the improved group reported a greater awareness of planning, monitoring, and regulating strategies. On the other hand, those in the declined group showed a lower awareness of the SRL strategies at the end of semester. In addition, emergent themes related to students’ SRL and learning experience while using the EGN were found. This article will also discuss the potential implications for electric circuit concepts instructions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it