Intelligent Computer Assisted Language Learning (ICALL) for <i>nêhiyawêwin</i>: An In-Depth User-Experience Evaluation
Bibliographic record
Abstract
Intelligent computer assisted language learning (ICALL) applications for Indigenous languages are a relatively new avenue for computer assisted language learning (CALL). CALL allows language learners to practise a wide range of grammatical exercises and receive feedback on their answers outside of class time. ICALL is essential for dynamically producing these exercises for polysynthetic Indigenous languages with complex morphology. To better understand user perceptions and behaviours within an ICALL setting, an in-depth user evaluation of nêhiyawêtân (a university-level ICALL application for Plains Cree) was initiated. Five second language learners of Plains Cree were recorded using nêhiyawêtân as they completed various grammatical exercises. They were encouraged to report their opinions, thoughts, and observations aloud. Subsequently, observed user reactions and strategies were recorded. This supplied us with potential user errors, strategies, and preferences that allowed us to improve answer feedback and the design and interface of the exercise templates. Moreover, the results of surveys and observations highlighted sociocultural issues that are not seen in mainstream CALL for majority languages. We hope that this evaluation will serve as a guideline for evaluating future ICALL programs for Indigenous and other minority languages.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".