Active Learning Strategies in a First-Year Engineering Materials Course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Active learning approaches in engineering education have been gaining popularity in recent years. This method of engaging students in the learning process has been shown to improve student outcomes during assessment and narrow the achievement gap for racialized students. However, active learning changes the typical classroom structure from the traditional lecturing approach and so there is a need to consider how these relatively contemporary approaches are affecting different groups of students. This research aims to understand the effects of active learning on the diverse populations of first-year engineering students. The first phase of this study will be presented herein, which will include a reflection on the active learning methodologies used in a first-year common-core materials engineering course, both during lectures and longer active learning sessions. A variety of active learning strategies used will be examined for their accessibility benefits and challenges.
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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 | 0.002 | 0.005 |
| 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