Investigating How Digital Technologies Can Support a Triad-Approach for Student Assessment in Higher Education / Étude des technologies numériques comme appuis à l’évaluation tripartite des étudiants universitaires
Bibliographic record
Abstract
The purpose of this research study was to investigate if and how digital technologies could be used to support a triad-approach for student assessment in higher education. This triad-approach consisted of self-reflection, peer feedback, and instructor assessment practices in a pre-service teacher education course at a Canadian university. Through online surveys, journal postings, and post-course interviews the study participants indicated that digital technologies could be used to effectively support such a triad-approach only if students were more actively involved in the assessment process and the course instructor placed a greater emphasis on formative assessment practices. Le but de cette recherche était d’étudier dans quelle mesure les technologies numériques pouvaient être utilisées pour soutenir une approche triadique de l’évaluation des étudiants universitaires. Cette approche tripartite consistait dans l’autoréflexion, la rétroaction par les pairs et les pratiques d’évaluation de l’enseignant dans un cours de formation des futurs enseignants au sein d’une université canadienne. À l’aide de sondages en ligne, d’annonces de journaux et d’entrevues post-formation, les participants à l’étude ont indiqué que les technologies numériques pouvaient être utilisées pour soutenir efficacement une telle approche triadique, à condition que les étudiants soient impliqués plus activement dans le processus d’évaluation et que l’enseignant mette davantage l’accent sur les pratiques d’évaluation formative.
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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 | 0.048 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".