A Typology of Tasks for Mobile-Assisted Language Learning: Recommendations from a Small-Scale Needs Analysis
Bibliographic record
Abstract
In response to the research priorities of members of TESOL (Teachers of English to Speakers of Other Languages), this study investigated language learners’ real-world tasks in mobile-assisted language learning (MALL) to inform the future development of pedagogic tasks for academic English as a second language (ESL) courses. The data included initial semistructured interviews with four ESL teachers and four college ESL students followed by an online task-based needs analysis conducted with 23 ESL teachers and 76 college ESL students at a university in the midwestern United States. Through the interviews and surveys, we identified how teachers and students used mobile devices and how they felt mobile devices could be used in language learning, and we categorized their target tasks in MALL according to the four language skills (reading, listening, speaking, and writing). The study found that ESL learners already use various mobile device functions, but that ESL instructors were less inclined to use these for teaching, suggesting that teachers may need further support and ideas before they can help their learners take advantage of their mobile devices for language learning. Both learners and teachers gave high rankings to tasks for listening and speaking as well as to activities integrated with SMS and the Internet. Based on the identified tasks, we created a MALL task typology to provide an initial authentic and sound resource for the future development of MALL tasks, lesson plans, and curricula.En réponse aux priorités de recherche des membres de TESOL (enseignement de l’anglais à des apprenants étrangers), cette étude a porté sur les tâches réelles dans le contexte de l’apprentissage mobile des langues pour ensuite éclairer le développement de tâches pédagogiques pour l’anglais académique dans les cours d’anglais langue seconde (ALS). La collecte des données a inclus des entrevues initiales semi-structurées auprès de quatre enseignants d’ALS et quatre étudiants d’ALS à l’université, ainsi qu’une analyse des besoins basée sur les tâches et accomplie en ligne auprès de 23 enseignants d’ALS et 76 étudiants d’ALS dans une université du Midwest des États-Unis. Les entrevues et les enquêtes ont permis d’identifier l’emploi que faisaient les enseignants et les étudiants des appareils mobiles ainsi que leurs perceptions du rôle que pouvaient jouer les appareils dans l’apprentissage d’une langue. Par la suite, nous avons classé leurs tâches cibles selon quatre compétences linguistiques (lecture, écoute, expression orale et rédaction). Les résultats indiquent que les apprenants d’ALS se servent déjà de diverses fonctions des appareils mobiles mais que les enseignants d’ALS étaient moins portés à s’en servir pour l’enseignement, ce qui porte à croire qu’il faudrait peutêtre leur offrir plus d’appui et d’idées de sorte à ce qu’ils soient en mesure d’aider les apprenants à profiter de leurs appareils mobiles pour apprendre la langue. Tant les apprenants que les enseignants ont attribué beaucoup d’importance aux tâches liées à l’écoute, à l’expression orale, à la messagerie texte et à l’Internet. À partir des tâches identifiées, nous avons créé une typologie des tâches pour l’apprentissage mobile des langues, fournissant ainsi une première ressource authentique et solide pour le développement futur de tâches, de plans de cours et de programmes d’étude dans le domaine.
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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 | 0.047 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".