The Role of Project-Based Learning in Promoting Preservice Teachers’ Communicative Capabilities
Bibliographic record
Abstract
This research aimed to explore the impacts of project-based learning on promoting preservice teachers’ communicative capabilities. A survey research design was employed to collect data, using an online questionnaire consisting of seven closed-ended and four open-ended questions. There were two target groups of preservice teachers who were second-year teacher trainees and enrolled on the course of Teaching Chinese as a Foreign Language. The first target group consisted of 23 preservice teachers from the Faculty of Education, Khon Kaen University, Thailand, while the second target group consisted of 25 preservice teachers from the Faculty of Arts, Southwest University, China. Both target groups attended the project-based learning lessons for 15 cycles in the first semester of the academic year 2021. The quantitative results indicated that all participants’ Chinese language communicative capabilities in listening and speaking skills were increased, from a total score of 45.47 to 68.25. Besides, the quantitative results showed that their vocabulary acquisition and communication skills improved, from a mean score of 2.62 to 3.33 and 2.67 to 3.29, respectively. In addition, the qualitative results of the 25 preservice teachers from Southwest University revealed that project-based learning is a successful teaching approach, because it provides preservice teachers with an opportunity to develop their Chinese language knowledge and skills through the project engagement set around challenges and problems in the real world. Finally, the qualitative results of the 23 preservice teachers from Khon Kaen University revealed that project-based learning has improved their communicative capabilities in understanding questions and using modern media in their teaching management.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".