Developing a Mobile App for Learning English Vocabulary in an Open Distance Learning Context
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
Academic success depends on the comprehension of a language, which is linked to vocabulary learning. Many distance students in South Africa find it difficult to comprehend learning in a language other than their mother tongue. Finding effective strategies for enhancing English vocabulary of university students amidst the spatial, temporal, and pedagogic distance associated with Open Distance Learning (ODL) practices remains a challenge. To address the need for enhancing vocabulary development, mobile application systems (apps) were explored as the best vehicle for the delivery of the vocabulary learning. Mobile learning technologies are ideal in the ODL context because they are flexible, accessible, available, and cater for a myriad of interaction activities. The purpose of the study is to design and implement a mobile-based application aimed at enhancing English vocabulary teaching and learning. Using the Design-Based Research methodology, this study maps the steps taken to develop a vocabulary learning mobile app named VocUp; it describes the architecture, user interface, features of VocUp, and advocates for contextually-conscious and learning-driven app development.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.003 |
| 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