Surveying Assessment in Experiential Learning: A Single Campus Study
Bibliographic record
Abstract
The purpose of this study was to determine the methods of experiential assessment in use at a Canadian university and the extent to which they are used. Exploring experiential assessment will allow identification of commonly used methods and facilitate the development of best practices of assessment in the context of experiential learning (EL) at an institutional level. The origins of EL are found in the work of Dewey (1938), later modified by Kolb and Fry (1975). Experiential methods include: experiential education, service learning problem-based learning and others such as action learning, enquiry-based learning, and case studies. Faculty currently involved in EL at the participating university were invited to complete an online survey about their teaching and assessment methods. This paper will share the results and analysis of the EL inventory survey. L’objectif de cette étude était de déterminer quelles méthodes d’évaluation expérientielle sont employées dans les universités canadiennes et dans quelle mesure elles sont employées. Le fait d’explorer l’évaluation expérientielle permettra d’identifier quelles sont les méthodes employées couramment et facilitera le développement des meilleures pratiques d’évaluation dans le contexte de l’apprentissage expérientiel au niveau institutionnel. Les origines de l’apprentissage expérientiel se trouvent dans les travaux de Dewey (1938), modifiés plus tard par Kolb et Fry (1975). Les méthodes expérientielles comprennent : l’éducation expérientielle, l’apprentissage par le service, l’apprentissage par problèmes, ainsi que quelques autres tel que l’apprentissage par action, l’apprentissage par l’enquête et les études de cas. Les professeurs qui pratiquent actuellement l’apprentissage expérientiel dans les universités participantes ont été invités à remplir un questionnaire en ligne portant sur leur enseignement et leurs méthodes d’évaluation. Cet article partage les résultats et les analyses du sondage sur l’apprentissage expérientiel.
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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 | 0.026 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".