Utilising Tiktok Features for Speech Communication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The art of teaching speech communication should be varied according to current trends. Teaching speaking should focus on the practice of the language, which should be done in pairs or groups. However, the COVID-19 pandemic restricted students from attending schools, making speaking in English impossible. Social distancing was enforced and still is now, when they returned to school, ruling out group activities or much speaking in classrooms. This was an issue among English language teachers in Malaysia as the teaching of English uses a modular system that comprises Listening and Speaking, Reading, and Writing. Listening and speaking could not be conducted at will. Pupils were deprived of opportunities to practice the English language in classrooms. Therefore, the suggested course of action was to improve speech communication by utilising the technologically useful application TikTok and its essential functionalities. This study aims to investigate how TikTok features can improve speech communication and examine learners' perceptions after applying the TikTok features to improve their speaking skills. The instruments used for this study are speaking rubrics for speaking tests and a questionnaire. A purposive sampling of 100 students was used. The results show that the respondents scored higher in their post-test than their pre-test after using TikTok to improve their speech communication. All respondents perceived the use of TikTok as positive and encouraging. The implication of the findings includes introducing innovative speaking practice measures such as the development of mobile learning, and a renewed teaching methodology to teach speech communication.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it