Collaborative Assessment: Using Self-assessment and Reflection for Student Learning and Program Development
Bibliographic record
Abstract
Collaborative Assessment: Using Self-assessment and Reflection for Student Learning and Program DevelopmentAbstractAs program-level assessment increasingly becomes an integral part of the higher-education landscape, so does the debate regarding the efficacy of current assessment methods. Traditionally, students do not participate in assessment—neither of their own learning nor of institutional or program efficacy. Our assessment process presents an alternative to traditional program-level assessment and is meant to improve student learning in two ways: (1) by asking students to reflect on their achievement of learning outcomes using evidence-based methods; (2) by providing assessment practitioners with authentic, contextualized data on which to make claims about curricula. This collaborative assessment process was designed to address the complex needs of a cross-curricular rhetoric program but responds to many general concerns about traditional assessment methods.Au fur et à mesure que l’évaluation de programmes fait de plus en plus partie intégrante du paysage de l’enseignement supérieur, il en va de même du débat sur l’efficacité des méthodes d’évaluation actuelles. Traditionnellement, les étudiants et les étudiantes ne participent pas à l’évaluation, ni à celle de leur propre apprentissage ni à celle de l’efficacité des programmes ou de l’établissement. Notre processus d’évaluation présente une alternative à l’évaluation traditionnelle de programmes et a pour but d’améliorer l’apprentissage des étudiants et des étudiantes de deux manières : 1) en demandant aux étudiants et aux étudiantes de réfléchir à la manière dont ils et elles ont atteint les résultats d’apprentissage, à l’aide de méthodes basées sur l’évidence, et 2) en fournissant aux praticiens de l’évaluation des données authentiques et contextualisées sur lesquelles ils peuvent se prononcer sur les programmes d’études. Ce processus d’évaluation en collaboration a été conçu pour répondre aux besoins complexes d’un programme de rhétorique transdisciplinaire mais il répond également à de nombreuses préoccupations générales concernant les méthodes d’évaluation traditionnelles.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
| grok | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
| opus | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.057 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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, unvalidatedLabeled directly by 3 models reading the full record.
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".