Using Digital Flashcards to Enhance Thai EFL Primary School Students’ Vocabulary Knowledge
Bibliographic record
Abstract
There is growing evidence in L2 vocabulary research that digital flashcards facilitate learners’ vocabulary learning and development. Several studies also suggest that deliberate vocabulary teaching is critical for successful language learning. Hence, the primary goal of this study was to investigate whether the use of digital flashcards could improve English vocabulary knowledge of the form-meaning link in Thai primary school children in an EFL context. The study also explored Thai primary school students’ attitudes toward using digital flashcards in vocabulary learning. The participants were 120 Thai primary school students who were divided into experimental and control groups. The experimental participants were taught using digital flashcards, while the control group received no unique treatments. Four measures of vocabulary knowledge were used to assess the participants’ vocabulary learning. A five-point Likert scale questionnaire was also used to explore the students’ attitudes toward digital flashcards. Descriptive and inferential statistics were used to analyze the data. The results showed that although both cohorts significantly improved their receptive and productive knowledge of L2 vocabulary, the students in the experimental group performed significantly better than students in the control group. These results indicate that digital flashcards are an effective method to facilitate vocabulary learning in Thai primary school learners. The analysis of the questionnaires also showed that learners had a very positive attitude towards the use of digital flashcards. These findings reaffirm the efficacy and value of visual and sound images, such as digital flashcards, in vocabulary learning. Implications for practitioners and suggestions for further studies are also addressed.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".