Building Connections in the First-year Undergraduate Experience
Bibliographic record
Abstract
Concerned about the success rate of new students in our program we designed and implemented a compulsory set of experiences which aim to support students in their transition from high school to university by 1) developing their sense of belonging to a community of learners and by 2) articulating with them the interrelationships among their first year core courses. We initiated various strategies which we have refined in response to student feedback over the past three years. In this paper we describe the pedagogical moves that constitute our initiative and the lessons we learned. We explore essential academic and personal issues that first-year students in all programs face. We share our research findings and address the big ideas that could be applied to any discipline or multi-disciplinary program. Préoccupés par les taux de rétention des étudiants de première année, les auteurs ont conçu et mis en oeuvre un ensemble d’expériences obligatoires dont le but est d’aider les étudiants à effectuer la transition entre l’école secondaire et l’université. Ils ont établi deux objectifs : (a) développer chez les étudiants le sens d’appartenance à une communauté d’apprenants et (b) démontrer les corrélations qui existent entre les cours de base de première année que les étudiants suivent. Diverses stratégies ont été entreprises et plus tard améliorées, à partir des rétroactions fournies par les étudiants au cours de l’étude de deux ans. Cet article présente une explication des actions pédagogiques de cette initiative et explore les questions essentielles académiques et personnelles auxquelles sont confrontés les étudiants de première année dans tous les programmes. Les résultats de cette étude sont résumés et les idées générales qui peuvent s’appliquer à n’importe quelle discipline ou à des programmes multidisciplinaires sont présentées en détail.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".