Pedagogical guide – A journey through the digital world and english culture
Bibliographic record
Abstract
With the arrival of conversational agents available to the general public, such as ChatGPT 3.5 in the fall of 2022, along with many other developments stemming from artificial intelligence, I decided, in February 2023, to submit a grant application to the ECQ in order to create a digital game with two main educational objectives: 1) To develop the mastery of English among college-level students through gamified (Bonenfant, 2023, p. 77) open educational resources (UNESCO) in the form of a digital serious game (Plante, 2016, p. 72) 2) To encourage the development of students’ digital skills (ministère de l’Éducation du Québec, 2019). These pedagogical resources would be made available to all teachers in the college network as an Open Educational Resource (OER) licensed under CC BY-NC-SA. Please note that two versions of this investigation game were developed, one of which is in a video game format (Unity version).1 Finally, the idea to create a digital serious game was accepted by the ECQ and I am proud to present – A Journey through the Digital World and English Culture! In addition to offering rich academic and cultural content, this OER can be adapted, remixed, and transformed by all teachers wishing to integrate this game into their teaching. I therefore invite you to engage in the game of experimentation and innovation for the common good and the development of English digital literacy among college students. Enjoy your digital journey, and I look forward to meeting you on the road to creative collaboration!
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.019 |
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".