Investigating Mobile-Assisted Language Learning Apps: Babbel, Memrise, and Duolingo as a Case Study
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
The market for mobile-assisted language learning (MALL) apps has experienced remarkable growth in recent years, with many learners now relying on these apps to learn languages. However, research on the effectiveness of such language learning tools remains scant. In this study, we provide an adapted app evaluation rubric to fill the gap in the literature. We evaluate three selected apps based on the standards of design, content, and pedagogy, aiming to offer teachers and learners tools and tips for selecting effective language learning apps. We employ qualitative content analysis to examine Babbel, Memrise, and Duolingo. We first analyze the selected apps based on direct contact and then evaluate them using an app evaluation tool adapted for this purpose. The findings show that although they target language learners in general and can help in simply learning basic and intermediate language, MALL apps also offer many features that are beneficial for learners, mainly regarding offline functions, app support, learning goals, learning activities, and gamification. Finally, we propose implications of such results and put forward recommendations for future 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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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