Students’ Proficiency and Textual Computer Gloss Use in Facilitating Vocabulary Knowledge
Bibliographic record
Abstract
Learning vocabulary forms a major part for any language learner. Apart from direct teaching of vocabulary, language teachers are always searching for ways to increase their students’ vocabulary to enable them to use the language more effectively. Therefore, this study sets out to investigate whether the use of computer textual glosses can aid vocabulary development. With a sample of 99 English as second language students, this study examines whether a computer-aided textual glosses embedded in a narrative text is able to aid students in developing their vocabulary knowledge. Using ANOVA and descriptive statistics, it was found out that students with different language proficiency levels used the gloss in a similar pattern. The similarity was that there were gains after immediate use of the glosses but the gains were not maintained over time. High proficiency students made the most gains followed by mid and low proficiency students. What can be learnt from this study is that computer textual glosses can be used to develop students’ vocabulary knowledge in the short term. However, this should be supplemented with other vocabulary teaching/learning activities for more robust vocabulary knowledge development. The implications of measuring vocabulary knowledge by using vocabulary tests in the study could have resulted in the students having more gain in productive vocabulary knowledge compared to receptive vocabulary.
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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 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".