I Can’t Program! Customizable Mobile Language-Learning Resources for Researchers and Practitioners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 it