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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".