Development of Listening and Linguistic Skills through the Use of a Mobile Application
Bibliographic record
Abstract
The purpose of this article is to present the results of a research that was developed with eight groups of students of undergraduate programs of the Language Institute at Santiago de Cali University. The research was developed with four groups of students who used a mobile application developed jointly by foreign language professors, a software development professor and systems engineering students, as a support tool for individual practice of English level 1 (Test groups) and an equal number of groups of students who did not use the application (Control groups). No mobile applications already available in the market were used because none of them fit the sequence of topics that the course develops along the semester, thus, it was necessary to design an application tailored to the different themes, grammar and vocabulary requirements that were developed by the students. In both cases, a written test was performed at the beginning and end of the course in order to establish the benefit that the application could offer to the students in the test groups. The results indicate that the frequent use of the mobile application might have a positive impact on the development of both listening and linguistic competencies of English.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".