Assessment Activities in a Distance Teaching University
Bibliographic record
Abstract
Distance teaching has been growing continuously for several years and accelerated with the COVID-19 pandemic. This has led to many educators re-evaluating how they assess their students’ learning. \n \nFor fifty years, TELUQ University has been a leading institution in distance teaching in Canada. Since its founding in 1972, TELUQ’s multidisciplinary teams and pedagogical design processes of courses have allowed the implementation of various assessments and digital tools. \n \nHow students’ learning is assessed at TELUQ University? The purpose of this presentation is to share findings related to data collection conducted through 175 online courses at TELUQ University. To this end, 937 assessment activities were analyzed using an analysis tool developed by Gérin-Lajoie, Beaupré, Contamines, Hébert, and Paquette-Côté (2020) which provides a detailed characterization of 20 components of assessment activities. \n \nA compilation of the data for each variable analyzed revealed, among others, that very few assessment activities were diagnostic (2%). It was also found that almost all assessment activities were carried out individually by the students (99.8%) and that the product was more frequently assessed (89%) than the process. Furthermore, automated scoring was used in only 7% of the 937 assessment activities. \n \nThe results of this study could certainly be of interest to the community of both researchers and teachers working in distance teaching. The latter will certainly find in this inventory something to think about in their assessment activities and consider new approaches in these times of rapid educational changes.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".