Task-Based Instruction in Enhancing Conversational Skills of Thai Students at Grade 11, YouTube as Teaching Media
Bibliographic record
Abstract
Students' English communication abilities in Thailand remain poor due to a lack of a wide range of activities to practice English. Learning English in authentic contexts enables students to develop intrinsic motivation and demonstrate acquired language competency. This study aims to enhance English conversational performances and investigate students' motivation after receiving instruction by integrating TBI and YouTube. The design of this study was pre-experimental research, which used the one-group and pretest-posttest analysis. The sample group included 39 eleventh-grade students from a government senior high school in Mahasarakham province, Thailand chosen by a purposive sampling process. The listening and speaking tests, as well as the intrinsic motivation scale, were used in this study. Data were analyzed by the paired-sample t-test and descriptive statistics (i.e., the arithmetic mean, standard deviation, and percentage). The results revealed that combining TBI with YouTube improved students' English communication skills, and intrinsic motivation in learning. Based on the findings of this study, the researchers recommended that teachers employ the TBL approach and YouTube videos in class as instructional mediums. Furthermore, future studies could combine TBL with other forms of multimedia to help students learn more effectively and enjoyably.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".