A Comparison Between Teacher-Led and Online Text-to-Speech Dictation for Students’ Vocabulary Performance
Bibliographic record
Abstract
Researchers have long supported the use of dictation as a test for language learners (Fountain & Nation, 2000), and dictation has been used as a test for learners of English as a foreign language (EFL). With the advantages of productive learning and reinforcing short-term memory, dictation is a commonly used technique to develop language skills, and it can be considered to be an assessment of foreign language learning (Kazazoğlu, 2013). However, the previous research has not fully explored how technology, such as text-to-speech (TTS), can be used in EFL classrooms. To address this issue, the researcher explored the use of traditional teacher-led dictation (TLD) and TTS dictation to compare the vocabulary performance of EFL learners. Forty-two college students participated in the study. The results indicated a significant difference between TTS and TLD on the participants’ vocabulary performance. Additionally, there was a correlation between the scores with TTS and TLD: the students who performed better with TLD also obtained higher grades with TTS. Based on the results, future studies and pedagogical suggestions are presented.
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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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".