Challenges and opportunities for teaching Italian as a second language: A case study at the University of Toronto Mississauga
Bibliographic record
Abstract
This article analyses the current condition of teaching Italian abroad and the ways in which second-language programmes can engage learners and meet their educational goals. While we are aware of the need for systematic action amongst various actors to ensure the future of Italian-language teaching, we choose to focus on the concept of Experiential Learning (EL). EL is a didactic action (i.e. a linguistic policy) which can be directly implemented into university courses or programmes and represents an innovative method of teaching Italian as a second language. Research on the theme of EL was conducted on Italian L2 courses at the University of Toronto Mississauga and involved instructors and students from various departments. These results lay the foundation for the creation of concrete educational policies, consistent with international literature on the action-oriented approach (CEFR, ACTFL), and for the ‘power of feasibility’ of individual classes (and individual teachers) of L2.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.038 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".