Predictors of student’s engagement and persistence in an innovative PBL curriculum: applications for engineering education
Bibliographic record
Abstract
The objective of this paper is to present the overall results of a study focusing on the engagement and persistence of undergraduatestudents in two PBL engineering curricula (Electrical Engineering and Computer Engineering) at the Universite de Sherbrooke inCanada. We will also discuss the results in terms of applications for engineering education. There were 192 undergraduate engineeringstudents who volunteered to participate in this study. First, they completed a questionnaire to measure the best predictors of students’engagement and persistence in their respective programs. Second, we met with 15 students who volunteered to participate in interviews.Results from the questionnaire show that the best predictor in both programs regarding students’ engagement and persistence is theprovided ‘support,’ which reduces stress. Results from the interviews reveal that the support most effective for students proves to be thestable learning environment (PBL tutoring sessions) as well as the scaffolding measures for managing time and organizing learningpractices. Taking into consideration the results from both the questionnaire and the interviews, it appears essential to limit these risksby taking measures that will reduce stress factors and increase strong support.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".