The Effects of Task-based Instruction Using a Digital Game in a Flipped Learning Environment on English Oral Communication Ability of Thai Undergraduate Nursing Students
Bibliographic record
Abstract
The growth of Thailand’s medical tourism industry has inevitably made English oral communication skills become increasingly important to Thai medical personnel, especially to nurses who have to act as medical mediators between doctors and patients. Thus, in order to prepare nursing students for their future career, it is necessary that English teachers find a way to help students improve their oral communication ability. Thus, in this study, as a means to overcome the students’ difficulties in learning English and to enhance their English oral communication ability, the task-based instruction using a digital game in a flipped learning environment (TGF) was developed by integrating three language learning approaches, namely task-based language teaching, flipped learning, and digital game-based language learning. The development of the instructional framework for the TGF was described first. Then, to investigate its effectiveness in improving the students’ oral communication ability, an experimental study, using a one-group pretest posttest design, was conducted with 23 second-year nursing students at a private university in Thailand for 11 weeks. The effects of the TGF on the students’ oral communication ability were assessed by the participants’ pre- and post-test. The finding revealed that the participants’ average post-test score was statistically significantly higher than their average pre-test score (p < 0.05), indicating that the TGF was successful in enhancing the students’ oral communication ability. Lastly, the factors contributing to this success were discussed.
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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) | 0.001 | 0.001 |
| 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".