When Curriculum Comes to Life: Making French Language ( L2 ) Acquisition a Lived Experience and the Effect of Students' Progression towards Proficiency
Bibliographic record
Abstract
The main purpose of this study was to investigate the Interactive Comprehensive (IC) Method in World Language pedagogy as it is expressed in the DJ DELF IC Curriculum, created by Steven ÉTIENNE Langlois. The IC Method and DJ DELF readers and curriculum were created in response to the 2013-2014 Ontario French as a Second Language (FSL) curriculum reform. This study conducts a qualitative case study to gain a better understanding of how the IC Method through the DJ DELF readers and curriculum affects curriculum effectiveness, language acquisition, and student engagement. These codes were taken into consideration to gain a deeper understanding of how the DJ DELF IC curriculum can be used as a tool for teachers to more efficiently and effectively implement the desired results of the Ontario FSL curriculum reform and the Common European Framework of Reference for Languages (CEFR) into their classrooms. The data showed that students responded positively to the DJ DELF IC Curriculum which was observed through students meeting learning goals, increased language acquisition, and increased student engagement.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".