E-évaluation dynamique et engagement cognitif en contexte de grand groupe à l’université
Bibliographic record
Abstract
Cognitive engagement is an important part of the learning process, it being connected with deep learning strategies which ensures the mastery of the learning materials (Dinsmore & Alexander, 2012; Greene, 2015). Yet, the use of large classes is noticeably increasing on Western campuses in reaction to the challenges brought by student massification (Maringe et Sing, 2014). This phenomenon is concerning, because it appears that knowledge inherent to learning processes is not considered when choosing teaching practices used in this context (Cuseo, 2007; Hornsby & Osman, 2014). Large classes are in fact associated with shallow learning, which does not benefit Higher Education’s goals (Biggs & Tang, 2011; Maringe & Sing, 2014). However, learning technologies provide accessible tools that can encourage learning-centered practices in large classes and therefore stimulate students’ cognitive engagement (De Matos-Ala & Hornsby, 2015). Thereby, this research aimed to implement a dynamic e-assessment prototype in two large university classes in order to study its influence on students’ cognitive engagement. The design-based-research approach was used in this project, which targets the development of empirical evidence and its experimentation in real-world practices (Brown, 1992). Mixed methodology consisted in measuring students’ cognitive engagement using the Cognitive Engagement Scale (Leduc, Kozanitis & Lepage, 2018) and to examine the e-assessment’s influence on the cognitive engagement’s extent with focus groups and designer logs. Results show that this form of e-assessment was favorable to the students’ cognitive engagement, offering them a chance to develop in-depth processing strategies and to autoregulate their study behaviors in preparation for their final exam.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".