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.
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
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".