Investigating Mobile-Assisted Language Learning Apps: Babbel, Memrise, and Duolingo as a Case Study
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.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".