An Investigation of the Round Robin Brainstorming in Improving English Speaking Ability Among Nakhonphanom University’s Second Year Students in Thailand
Bibliographic record
Abstract
As a second-year English teacher at Nakhonphanom University, it has become important to put more emphasis on the student’s ability to understand and speak the English language. The main objective as a teacher is to help the students learn the English language by utilizing the best methods. Thailand is rapidly becoming part of the global economy where university students require English speaking skills to get recognition in the global job market. As such, English teachers must double their efforts to ensure students reach international standards of communicating. Like any other university in Thailand, Nakhonphanom University has prioritized its efforts in improving the English-speaking abilities of its students. This research, therefore, aims at exploring the use of Round Robin Brainstorming as a technique to help Thai students understand and speak English through oral presentations. The researcher arranged the students in groups each having eight participants in a circular manner. Each group contained high English performing students and low English performing students. Each participant was given 5 minutes to talk about each other’s weaknesses and to brainstorm ideas that would help improve these weaknesses. The study combined both qualitative and quantitative methods. The study used a pre-test and post-test design as well as observing the students and questionnaires as data collection instruments. The overall pre-test scores showed a mean 4.03 which was low but went to a high of 11.97 in the post-test. The SD in the pre-test was 0.83 and the post-test was 0.13 meaning an overall improvement in the speaking abilities of the participants after implementing the Round Robin technique. The findings from the research showed that the pronunciation accuracy, language appropriateness, grammatical accuracies, and English fluency had been significantly higher after the implementation of the Round Robin Brainstorming technique. The students were extremely satisfied with the technique and they also felt relaxed and comfortable in discussing any of the topics given. The Round Robin Brainstorming method facilitated teamwork and enjoyment while at the same time improving the students' ability to speak English. The findings and results of this study will be used to improve the English-speaking process for Thai students. More specifically, the findings can be used as a reference while implementing the Round Robin Brainstorming technique as well as solving problems in the classrooms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".