Teachers as CALL Customizers: exploring teachers’ perceptions of conceptualizing and customizing CALL materials
Bibliographic record
Abstract
A common issue in the second/foreign language (L2) classroom is that teachers’ insights are not often included in the development of CALL materials. To understand how teachers can customize CALL materials that meet the needs of their students, our previous conceptual paper proposed a three-level approach to show how teachers can leverage basic computers skills to categorize pre-existing digital materials to stimulate L2 interaction, namely by using them as-is, modifying them, and/or using online tools to create them from scratch. This one-group mixed-methods study implements these ideas by: (1) examining the types of CALL materials teachers can customize to stimulate L2 interaction, and (2) assessing participants’ perceptions of how they used the approach. The study took place in a CALL teacher training course for eight weeks at an English-speaking university in Montréal and featured readings, lectures, lab sessions, and projects directed at customizing CALL materials. To understand the types of CALL materials participants could customize (goal #1), mixed-methods data were collected in the form of online ESL courses built by participants and a design-choice log where they recorded details about the tools they used, providing an overview of each activity. A reflective discussion was held using the Socratic-Wheel technique to understand how participants used the approach (goal #2). The results indicate that participants customized CALL materials at all three levels, targeting L2 interaction with (e.g. online flashcards) and through (e.g. synchronous messages) computers. Responses from the reflective discussion revealed that participants perceived becoming aware of their abilities in CALL, thus endorsing the pedagogical effectiveness of the approach.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".