Taking Social Entrepreneurship Education to the Next Level – A Teaching and Learning Project at Renaissance College (University of New Brunswick)
Bibliographic record
Abstract
In this paper we present the results of a scholarship of teaching and learning research project we conducted in 2016/17 at Renaissance College, University of New Brunswick. Our case study describes the challenges and successes of the student-centered re-design of a course about social entrepreneurship, a mandatory component of an undergraduate interdisciplinary leadership program. In particular, the project aimed at improvingStudents' engagement, andStudents' satisfactionparticularly for students who start the course at a lower engagement level.We first systematically evaluated pre-existing data on earlier runs of the course. Then we conducted interviews and a focus group with graduates in 2016 which provided additional information. The analysis of this dataset informed our comprehensive and systematic evidence-based redesign of the course for the offering in the winter of 2017. Finally, we used targeted surveys in March 2017 that provided data on the results of the course redesign and on student learning.In summary, the data suggested that the redesign of this course has significantly improved students’ learning experience, the clarity of course requirements, and students’ self-directed learning. This paper may be helpful also for scholarship of teaching and learning projects in other fields of study aiming at adult learner oriented and evidence-based course redesign.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".