The Impact of Distance Assessment on Students’ Academic Results and Perseverance in Higher Education
Bibliographic record
Abstract
In Canada, the Covid-19 pandemic has led to a rethinking of the way students' learning is assessed. This raised a host of questions for their teachers, who had to turn to distance assessment: what assessment activity should students carry out? What format should be used to present instructions? What support should be provided during the assessment activity? What feedback should be given to students at the end? To inspire teachers in the implementation of distance assessment, our team categorized assessment activities at TELUQ University, a leading institution in distance teaching in Canada. To this end, more than 1,600 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. Among these assessment activities, we know among other things that: the case studies and exams are the most frequent types of assessment activities, the assessment activities include mostly instructions in text format on the course website, the teachers offer mostly on-demand support to their students, the feedback is mostly given to students in text format. Nevertheless, two important questions remain, and will be the focus of this presentation. Do certain assessment activities lead students to better academic results? Are there assessment activities that lead students to persevere more in their studies? These are the questions we will be answering in this presentation to inspire participants in setting up their own assessment activities.
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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 | 0.019 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".