The Effects of Task-Based Instruction Using Online Language Games in a Flipped Learning Environment (TGF) on English Oral Communication Ability of Thai Secondary Students
Bibliographic record
Abstract
The purposes of this study were to investigate the effects of task-based instruction using online language games in a flipped learning environment (TGF) in developing the English oral communication ability of Thai secondary students and examine the students’ opinions of the task-based instruction using online language games in a flipped learning environment. The present study employed a mixed-method approach. The two-group pre-test and post-test design was used. The participants were 80 students studying in Mathayomsuksa 3 (grade 9) at a secondary school in Maha Sarakham province in Thailand. Forty students were in the experimental group where the TGF was given as the treatment, and 40 students were in the control group where all instruction was taught only in class. Pre- and post-tests were used to collect the quantitative data. A semi-structured interview was used to collect the qualitative data from the students’ opinions. The findings revealed that the students in the experimental group outperformed those in the control group after the TGF. The different mean score of the experimental group was statistically significantly higher than the control group (p < 0.05), demonstrating that the TGF was effective in improving the students’ English oral communication ability. It appears that combining task-based language instruction, flipped learning, and game-based language learning could help the students improve their oral communication skills in English.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".