A Cohort-based Learning Community Enhances Academic Success and Satisfaction with University Experience for First-Year Students
Bibliographic record
Abstract
Assessment of a successful cohort-based learning communities program for first-year undergraduate students shows that students in the program perform better academically and also report a higher level of satisfaction with their university experience than students who are not in the program. Students enrolled in arts and science at the University of Toronto, who take several large-enrolment courses in their first year, may optionally participate in the First-Year Learning Communities (FLC) program, designed to assist with the academic and social transition from high school to university. In this Freshman Interest Group model of learning community, the curriculum across the clustered courses is not linked. The FLC program was assessed over a five-year period, using student academic records and self-reported survey data. This paper also provides details on program design and implementation. L’évaluation d’un programme de communautés d’apprentissage fondées sur les cohortes pour les étudiants de première année du premier cycle qui a obtenu du succès montre que ceux qui sont inscrits à ce programme ont de meilleurs résultats scolaires et sont plus satisfaits de leur expérience universitaire que les autres. Les étudiants inscrits en arts et sciences à l’Université de Toronto, qui suivent plusieurs cours de première année où il y a de nombreux inscrits, peuvent participer au programme de communautés d’apprentissage la première année (CAPA) qui vise à les aider à effectuer la transition entre l’école secondaire et l’université sur le plan scolaire et social. Dans ce modèle de communautés d’apprentissage destiné au groupe d’intérêts particuliers des étudiants de première année, il n’y a pas de lien entre les programmes d’études des participants. Les chercheurs ont évalué le programme pendant cinq ans à partir des dossiers scolaires des étudiants et des données d’un sondage réalisé auprès d’eux. Le présent article fournit aussi des détails sur l’élaboration et la mise en œuvre du programme.
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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 | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.023 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".