Research Project on the Development of CEGEP ESL Students' Digital Literacy
Bibliographic record
Abstract
In a world where artificial intelligence (AI) challenges communications, the critical thinking and ethical skills of humans, as teachers, contributing to the development of the digital literacy of our students becomes essential. Alongside this challenge, teaching practices must be transformed, and this research project aims at proposing a scientifically tested answer, an option, to the teaching community. In this research project, a recently developed digital game (A Journey through the Digital World and English Culture) will be tested and its impact on the development of CEGEP ESL students' digital literacy (i.e., ethical citizenship, information literacy, critical thinking, content production, and communications vis digital technology) will be documented. This presentation will highlight the theoretical foundations, methodology, innovative developments of the project, as well as the richness of the college and university collaborations that support this ambitious project. As part of this research project, Sophie Marier works in collaboration with Maude Bonenfant, Full Professor in the Department of Social and Public Communication at UQÀM, and Patrick Plante, Professor in the Department of Education at TÉLUQ University. The research team also benefits from the valuable collaboration of five colleagues from five colleges in the greater Quebec City area: Sainte-Foy (Charles Lapointe), Garneau (Sandra Cole), Limoilou (Stéphanie Fraser), Mérici (Rachel Tunnicliffe), and Lévis (Amy Pittendreich).
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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".