Student Selection Framework for work-integrated learning experiences: Enhancing the decision-making process for assigning students
Bibliographic record
Abstract
INTRODUCTION: Approximately 50 % of post-secondary students attend work-integrated learning (WIL) experiences. In an undergraduate nursing program, the student selection team assigns students to each available WIL experience, but there are no known frameworks to guide this decision-making process. This paper describes the Student Selection Framework (SSF) developed by nursing faculty to support the decision-making process. The purpose of sharing this work is to fill a gap in the literature on structured processes for assigning students to WIL experiences. BACKGROUND LITERATURE: While there is some literature about the placement process from a broader perspective, there is a gap in the literature about the decision-making process of selecting students for WIL experiences. DISCUSSION: The Student Selection Framework highlights several factors to consider, including student placement preference; student placement history; student supporting statement; instructor feedback and recommendation; cumulative grade point average; and student self-reflection. This initiative has been modified over four terms to enhance the selection process. CONCLUSION: Implementing this framework has resulted in refined objectivity of student selection, enhanced transparency of the selection process, and efficiency of student placement decisions. The Student Selection Framework can be used as a guide and modified by post-secondary faculty who assign students to WIL experiences in practice disciplines.
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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 | 0.041 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".