Student Perceptions of Performance-Based Assessments for In-Person and Online Courses
Bibliographic record
Abstract
COVID-19 social distancing measures forced many university courses to be offered online. The performance-based assessments originally designed for in-person learning may not work well in online environments. This study investigated students’ perceptions of performance-based assessments, and their associated resources, during a course that was offered both in-person and online. The results from 312 undergraduate education students (n = 248 in-person and n = 64 online) indicated all the resources for one of the two assessments were rated statistically significantly higher by students who attended the course in-person. This indicated students who completed the course in-person had a stronger interaction with the assessment resources and rating having higher cognitive and affective skills needed to perform a similar assessment task in their future classrooms. Online students indicated some resources, such as assessment instructions and scoring rubrics, should be better explained during the course for more clarity regarding expectations. Les mesures de distanciation sociale ont contraint de nombreuses universités à proposer des cours en ligne. Les évaluations basées sur les performances, conçues à l'origine pour l'apprentissage en personne, peuvent ne pas fonctionner correctement dans les environnements en ligne. Cette étude s'est intéressée à la perception qu'ont les étudiants des évaluations basées sur les performances et des ressources associées, dans le cadre d'un cours dispensé à la fois en présentiel et en ligne. Les résultats obtenus auprès de 312 étudiants en éducation de premier cycle (n = 248 en personne et n = 64 en ligne) indiquent que toutes les ressources pour l'une des deux évaluations ont été évaluées de manière statistiquement significative plus élevée par les étudiants qui ont suivi le cours en personne. Cela indique que les étudiants qui ont suivi le cours en personne ont eu une interaction plus forte avec les ressources d'évaluation et qu'ils ont évalué les compétences cognitives et affectives nécessaires pour effectuer une tâche d'évaluation similaire dans leurs futures salles de classe. Les étudiants en ligne ont indiqué que certaines ressources, telles que les instructions d'évaluation et les grilles de notation, devraient être mieux expliquées pendant le cours pour que les attentes soient plus claires.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".