I Can’t Program! Customizable Mobile Language-Learning Resources for Researchers and Practitioners
Bibliographic record
Abstract
Combining insights from Activity Theory (Engeström, 2014), mobile-assisted language-learning (MALL) (Stockwell and Hubbard, 2013), and computer-assisted language learning (CALL) research (Chapelle, 2001), this paper proposes three levels of teacher involvement in the adaptation and/or creation of MALL resources to enhance learner interaction with the target language and potentially contribute to the field of learner-computer interactions. Specifically, this paper (1) proposes three levels of teacher involvement in MALL material creation, moving from easily adaptable pre-made materials (e.g., Duolingo) to customizable materials (e.g., Quizlet) and finally to teacher-created materials (e.g., Moodle); (2) demonstrates how these levels of design can be implemented in a MALL context to increase target language interaction according to Activity Theory (e.g., how teachers can incorporate gaming features into their online courses); and (3) concludes with recommendations as to how MALL “engineers” can work together to enhance the overall L2 learning experience and potentially collaborate in research and in the design of pedagogical materials. From a pedagogical standpoint, through these three levels of teacher involvement in material creation, teachers can extend the reach of their classrooms by mobilizing the target L2 environments, depending on their MALL/CALL proficiency and/or interests. This approach also invites second language acquisition scholars from a wide range of technological abilities to contribute to CALL research.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".