Utilizing Facebook Input to Enhance Vocabulary Knowledge in Young EFL Learners
Bibliographic record
Abstract
This mixed-methods study investigated the role of Facebook in enhancing Thai EFL primary school learners’ vocabulary knowledge. The study's primary goal was to examine how using Facebook as an instructional platform improves the written form of English vocabulary knowledge. Twenty-four primary school students, aged 11-12, participated in this study. Two tests were designed and validated to measure students’ receptive and productive knowledge of word form. A focus group was also used to gain deeper insight into students’ perspectives on the impact of Facebook input on vocabulary learning. Descriptive and inferential statistics were performed to analyze quantitative data, while content analysis was used to analyze qualitative data. The results showed that students significantly improved their receptive and productive vocabulary knowledge, and the knowledge of word form (written) developed along the receptive-productive continuum. Additionally, the qualitative findings showed the usefulness of Facebook input. Indeed, the participants viewed the Facebook input as an inviting and stimulating atmosphere and a helpful platform to acquire vocabulary by engaging and sharing with their peers in the learning activity. Overall, the study indicates that Facebook input is an efficient alternative platform for vocabulary teaching. Implications for practitioners and suggestions for future studies are also provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".