Digitizing practical production work for high-stakes assessments / La numérisation de travaux pratiques de production pour les évaluations à enjeux élevés / La numérisation de travaux pratiques de production pour les évaluations à enjeux élevés
Bibliographic record
Abstract
High-stakes external assessment for practical courses is fraught with problems impacting on the manageability, validity and reliability of scoring. Alternative approaches to assessment using digital technologies have the potential to address these problems. This paper describes a study that investigated the use of these technologies to create and submit digital representations of practical production work and forms of creative expression for summative high-stakes assessment. The study set out to determine the feasibility of students creating and submitting these digital representations for assessment and to identify which of analytical or comparative pairs scoring generated the more reliable scores. This paper proposes that scoring digital representations of creative practical work submitted by students is a viable alternative to traditional approaches to assessment. L’évaluation externe à enjeux élevés dans les cours pratiques se heurte à des problèmes qui se répercutent sur la gestion, la validité et la fiabilité de la notation. Des approches différentes de l'évaluation utilisant des technologies numériques ont le potentiel de remédier à ces problèmes. Cet article décrit une étude consacrée à l'utilisation de ces technologies pour créer et soumettre des représentations numériques de travaux pratiques de production et de création pour une évaluation sommative à enjeux élevés. L'étude visait à déterminer si la création de ces représentations numériques par les étudiants et leur soumission pour évaluation étaient réalisables. Elle visait aussi à identifier quel système de notation de groupe, analytique ou comparatif, générait les scores les plus fiables. Cet article soutient que noter les représentations numériques de travaux pratiques soumis par les étudiants offre un choix viable aux approches traditionnelles d'évaluation.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".