Integrating a critical reflection framework for experiential learning activities into a large first-year engineering course
Bibliographic record
Abstract
First-year university is an ideal time for students to begin the development of critical thinking and self-directed learning skills. Structuring critical reflection with experiential learning activities can provide opportunities for students to develop and learn how to apply these skills in the future. Previous research has identified considerations for implementing critical reflection into the first-year engineering curricula in ways that students will meaningfully engage. The primary goal of this work was to integrate critical reflection learning outcomes, activities, and assessments into the first-year engineering curriculum connected to experiential learning activities. Critical reflection activities were scaffolded to a critical reflection framework, adapted to the first-year engineering context. Three critical reflection assessments were mapped to experiential learning activities to further the development of these skills in parallel, such as teamwork, problem solving, and communication. One assignment involved a peer review process, where students had the opportunity to learn from each other’s experiences, and give constructive feedback through learning of students’ shared university experiences. The benefits of developing student reflection skills are obvious, and the improvements witnessed are encouraging. However, there remain many challenges, particularly with respect to assessment methodologies, and student motivation. Meaningful integration of critical reflection remains an iterative learning experience for the instructors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".