The Development of a Task-Based Chinese Speaking Instructional Model for Chinese as a Foreign Language Learners in Thailand
Bibliographic record
Abstract
This research aimed to (1) develop a task-based Chinese speaking (TBCS) instructional model to enhance the Chinese speaking skills of beginner-level Thai learners studying Chinese as a foreign language (CFL), (2) implement the developed TBCS instructional model with beginner-level Thai CFL learners for refinement, and (3) evaluate the suitability of the developed model by seeking experts’ opinions. The participants of this research included 5 experts specialized in instructional model design and teaching Chinese as a foreign language, as well as 22 beginner-level Thai CFL learners enrolled in a Chinese program at a public high school in Thailand. Research instruments included the TBCS instructional model with 12 lesson plans and the expert evaluation form. The collected data were analyzed by using mean, standard deviation, and percentage. The research findings revealed that (1) the developed TBCS instructional model consisted of 7 key elements: goals and assumptions, teaching procedures, learning environment, principles and reactions, support system, application, and instructional and nurturant effects, with the teaching procedures comprising of 4 major steps: activate prior experience, build language skills, carry out tasks, and deepen understanding, (2) the students had successful speaking performance after studying through the developed model, and (3) the suitability of the developed TBCS instructional model was evaluated by experts at the highest level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".