Virtual versus In-Person Case-Based Learning for Lower Year Courses in Engineering Technology Education
Bibliographic record
Abstract
The coronavirus (COVID-19) global pandemic resulted in shifting student learning from an in-person format to an online-based learning environment to ensure the safety of both students and staff. As the government-imposed lockdowns are lifted with the pandemic coming to an end, institutions evaluate whether to continue providing a virtual e-based learning or offer a hybrid learning platform for courses. Thus, this suggests the need to evaluate the effectiveness of online learning as compared to in-person learning, and even more so, how the format of delivery affects active learning strategies including case-based learning (CBL). This study compares the effectiveness of online CBL and in-person CBL in two different undergraduate engineering technology courses offered at McMaster University. The two courses were initially conducted virtually but were switched to the in-person format in the middle of the semester with the university having re-opened, providing the students with a better distinction between the two formats. At the end of the semester, the students in both courses were asked to provide their perceptions on the effect of CBL on their analytical skills (critical thinking and problem-solving), interpersonal skills (communication and teamwork), real-life technical skills, learning experience, self-confidence and performance, and deeper conceptual understanding via an anonymous survey. The survey results demonstrated a high positive response for the in-person CBL, whereas the virtual CBL included varying responses throughout the five-point grading scale. The results obtained from the survey imply that students were more perceptive of the positive effects of the in-person CBL, compared to the virtual CBL. Furthermore, the responses were similar for the two different courses, complying with the trend of favouring the face-to-face CBL format.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".