Examining English Language Learning Apps from A Second Language Acquisition Perspective
Bibliographic record
Abstract
This study set out to explore dedicated language learning apps pedagogically while focusing mainly on aspects of second language acquisition. A total of 20 English language learning apps were collected for analysis. The study took one model of analysing course book materials and another, computer-assisted language learning model and combined them into one analytical framework with bespoke criteria, ensuring the analysis was most suitable for our case. The analytical framework which was developed reached a number of conclusions about dedicated language learning apps (DLLAs). The findings revealed that DLLAs tend to provide mechanical forms-focused practice without facilitating collaborative learning nor focusing on developing users’ communicative competence, which suggests that DLLAs reflect a behaviouristic view of language learning. The conclusion offers some suggestions to improve DLLAs and proposes that, for the time being, educators should look beyond DLLAs and instead investigate how can apps that are not designed for language learning (generic apps) be used in the manner of DLLAs to avoid the issues that this paper identifies with them.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".