The Benefits of the Proprioceptive Method Used in Learning English via Facebook by Thai Government Officials
Bibliographic record
Abstract
Good listening and pronunciation skills lead to successes in foreign language learning. The main purpose of this study was to examine the benefits of adopting the Proprioceptive Method in learning English by Thai local government officials with the help of Facebook. A seventeen-day training course was implemented, comprising two days of face-to-face training, fourteen days of online training via Facebook, and one day of course wrap-ups and evaluation. The crucial training instruments used in the study was online conversations, a minimal-pair listening test, a satisfaction survey and a Facebook chat room for participants’ written comments. The statistical results showed that after the training, the trainees’ ability to segment English consonant sounds that were problematic for Thais significantly increased. It is inferred that the Proprioceptive Method tended to be effective for training English via Facebook. However, looking closely to the participants’ perceptions of the sounds in each pair, the rise was statistically significant in certain pairs, but not all. Additionally, the result from a satisfaction survey demonstrated that the training method was perceived to be at the highest level of satisfaction. Nonetheless, participants’ written comments indicate advantages and disadvantages of the training.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".