First-Year Engineering Students' Perceptions of Active Learning
Bibliographic record
Abstract
Active learning is an instructional method that involves students in activities such as discussions, problem-solving, and hands-on tasks, as opposed to traditional lectures where students passively receive information. Research consistently demonstrates that active learning fosters deeper understanding and long-term retention by encouraging students to engage directly with material rather than passively receiving information. These methods may be particularly effective in large, lecture-based courses where traditional methods can limit student interaction and engagement, but research has encountered difficulty in translating active learning methods to large-enrolment courses, such as those at University of Calgary’s engineering department. Despite these benefits, student perceptions of active learning at the University of Calgary (U of C) vary significantly. Some find the approach engaging and motivating, while others find it intimidating, uncomfortable, or challenging. Students’ perceptions of active learning are often influenced by their prior educational experiences and academic backgrounds. These perceptions significantly shape their willingness to engage with active learning strategies, which directly impacts the overall effectiveness of such instructional approaches in avhieving desired learning outcomes. ENGG 204 is a general first-year materials science course at U of C’s engineering department. It was first offered in the Fall of 2023. Since then, both the course and U of C’s first-year program have undergone significant changes. There is a need to understand how students perceive the effect of these changes on their education and to evaluate the overall effectiveness of them in improving learning. A survey has been conducted to evaluate students’ overall attitudes and satisfaction with active learning techniques in their ENGG 204 course. It was also designed to identify perceived
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".