Reimagining Student Success through Engagement and Soft Outcomes: Learning from a Capstone Course in a Canadian Polytechnic
Bibliographic record
Abstract
This paper uses a capstone class in the bachelor of technology program at Northern Alberta Institute of Technology (Canada) as a case study for reimagining a “successful” student and promoting growth in a variety of learners. In this course, students, guided by faculty advisors, work in teams to address real-world projects solicited by individuals or organizations. Over two years, feedback was gathered through interviews and surveys with graduating students and alumni to identify opportunities for improvement and to gain deeper insight into students’ learning experiences. The authors analyze these responses through the lens of scholarship of teaching and learning (SoTL), particularly through literature on soft outcomes. In comparison to hard outcomes like grades or completing a degree, soft outcomes capture student advancement toward the goals of a particular course and can include interpersonal, organizational, and internal development. Ultimately, we conclude that our course promotes students’ progress toward soft outcomes through their relationships with their project sponsors, instructors, and teammates. Our findings emphasize the importance of fostering students’ social, emotional, and personal growth and suggest that the students who might be perceived as low-achieving can still advance as much on their learning journey as the ones who would be traditionally lauded as high-achieving. We align our findings with scholarship that investigates students’ emotional growth and wellbeing, which can be difficult amidst pedagogy, research, and government policy that define the value of post-secondary education primarily in terms of its ability to prepare students for the job market. This paper reframes what being a successful student means, contributes to a wider body of research on soft outcomes, and provides valuable insight for educators and researchers who are invested in students’ engagement.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".