Evaluation of an Internship Assessment Grid for Francophone Physical and Health Education Student Interns
Bibliographic record
Abstract
The objective of the present study is to analyze four metric qualities of an assessment grid for internship placements used by professionals to evaluate a sample of 110 Franco-Ontarian student interns registered between 2006 and 2009 at Laurentian University in the School of Human Kinetics. The evaluation grid was composed of 26 criteria. The four metric qualities that were analyzed were: the degree of difficulty, the degree of discrimination, the internal consistency, and the concurrent validity. Each intern’s performance was assessed by three individuals: the professional supervisor, the intern (self-assessment) and the university professor who coordinates the internship placement. The analysis of the three assessments based on the Education Testing Service Method indicates that the assessment of the professional supervisors and intern self-assessment are too high (difficulty index, pi = 20) and produced a discrimination power of zero between the interns (discrimination index, Di = 0). The analysis of the internal consistency of the criteria indicates that a number are too highly interrelated (Cronbach’s alpha = 0.97) and that ten criteria can be removed from the evaluation grid, as they are redundant. Concurrent validity, determined by calculating three correlations between the three dimensions of the evaluation grid (before, during, and after the teaching session) and the overall rating of the intern, was demonstrated insofar as the lowest correlation between the assessment of the intern’s performance and the measurement criterion (overall rating of the intern’s performance) was significant (r (106) = .76, p < .001). L'objectif de la présente étude est d'analyser quatre qualités métriques de l'évaluation d’un échantillon de 110 étudiants stagiaires franco-ontariens inscrits entre 2006 et 2009 à l'Université Laurentienne à l'École des sciences de l'activité physique. La grille d'évaluation était composée de 26 critères. Les quatre qualités métriques sont: le degré de difficulté, le degré de discrimination, la consistance interne et la validité concomitante des critères. Les stages ont été évalués par trois personnes: le superviseur de stage, le stagiaire (auto-évaluation) et le professeur d'université qui coordonne le stage. L'analyse des items selon la méthode ETS (Educational testing service) indique que les évaluations des superviseurs de stage et l’auto-évaluation des stagiaires sont trop élevées (indice de difficulté, pi = 20) et ne sont pas discriminantes (indice de discrimination Di = 0). L'analyse de la consistance interne des critères indique qu'un certain nombre sont trop fortement corrélées entre eux (coefficient alpha de Cronbach = 0,97) et que dix critères peuvent être retirés de la grille d'évaluation car ils sont redondants. La validité concomitante, déterminée par le calcul de trois corrélations entre les trois dimensions de la grille d'évaluation (avant, pendant, et après la session d'enseignement) et la note globale du stagiaire, a été démontrée dans la mesure où la plus faible corrélation entre l'évaluation de la performance du stagiaire et le critère de mesure (note globale de la performance du stagiaire) était significative (r (106) = .76, p < .001).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".