Learning from Learners: Wellness seminars and self-reflections for first-year engineering students to enhance their journey in engineering education
Bibliographic record
Abstract
First-year engineering can be an overwhelming experience for students, and it is important to have regular check-in points for students as they transition to post-secondary education. Beginning in 2019, the Schulich School of Engineering (SSE) at the University of Calgary implemented mental wellness and engineering attributes modules across the first-year engineering curriculum. These modules focused on students’ overall development to support their success in the diverse world of engineering. In this paper, we give an overview of the program implementation during 2021-2022 and recommendations for effective implementation of such series based on our experiences. We also briefly present a summary of the students’ self-reflections from the first two years of the program.
 At the end of each module, we ask students a few open-ended questions to reflect on their experiences based on the materials covered in the module. In addition to these responses from different modules, the final self-reflection, where the students are asked to reflect on their journey as a first-year engineering student, is of immense help in enhancing our understanding of students’ perspectives. Analysis and review of these reflections help mold our strategies for future programming to support student wellbeing and academic and professional development.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".