Improving Writing Constructs and Performance Through Vlog-Assisted Language Learning (VALL)
Bibliographic record
Abstract
Utilizing technology to enhance students' writing skills at the higher education level is now the focus of scholars. One of the most effective nontraditional approaches to enhancing pupils' writing abilities is vlog-assisted language learning (VALL). The university professors who instruct pupils on writing skills never use this VALL. Therefore, the purpose of this study is to compare the academic writing skills of first-year university students taught utilizing the methodology of Bog-Assisted Language Learning (VALL) with those who were not. In addition, this research analyzes how students react to using VALL in teaching and learning writing skills. Thirty university English majors in their third year participated in the research. The research took a quantitative approach to data collection by administering pre- and post-writing examinations and a series of questionnaires to both the experimental and control groups. Evaluation of the gathered data was carried out with the use of descriptive statistics. The findings indicated that pupils who were taught writing utilizing VALL improved substantially more than those that were not. In addition, most student responses on using VALL to teach writing skills were favorable. Since this is the case, the English Department at a university might benefit from implementing VALL into their teaching and learning of writing.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".