Analysis of Mobile Apps for Learning Grammar through Mobile Assisted Language Learning Approach
Bibliographic record
Abstract
The objective of the current study is to evaluate the recently developed mobile apps for learning and improving English grammar. This study selected 10 grammar apps ranked 3 and above by online users with a large number of installations. The study adapted a framework proposed by Supyan Hussin (2013) with four criteria: System, Program, Curriculum, and Language & Culture. Furthermore, information provided in Google Play Store App assessment in terms of App users’ Reviews, Ratings, and Downloads also was used to assess the selected grammar apps. First, the findings show that the grammar apps tend to teach grammar out of context, second, apps minimally adapt to the user’s skill sets. Third, the grammar apps rarely offer explanatory corrective feedback to the users. Fourth, some grammar apps have accuracy issue in terms content and typo. Despite pedagogical paradigm shift to communicative approaches to language learning, the assessed grammar apps are more behaviorists in nature where features of cognitivism and constructivism, and particularly connectivism approaches are rare. To better align with MALL, contextualized language, explanatory feedback, and adaptive technology need to be incorporated into these apps.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".