The Effectiveness of a CLIL Basketball Lesson: A Case Study of Japanese Junior High School CLIL
Bibliographic record
Abstract
This article outlines a junior high school physical education class which teaches basketball in English using the CLIL framework as a case study. The purpose of the article is to consider how and what students learned from the class through students’ class results, basketball skills test, post lesson questionnaire and pre and post lesson teacher interviews. It examines how the teacher’s attitude toward CLIL changes from pre and post lesson interviews. Through this CLIL class led not by English teachers but by a physical education teacher the qualities and abilities necessary for competent CLIL teaching are considered. Regarding students, this CLIL lesson was conducted for the acquisition of physical basketball skills, English expressions and situational English ability. It also aimed to teach 21st-century skills defined by global education. The lesson resulted in students being able to understand both the English target structures and the basketball terms and strategies taught. In addition, students not only learned the content of the lesson, but also co-operated well with the teacher and worked well in teams which made the lesson successful. Before the lesson, the teacher felt that the CLIL lesson would be difficult for the students. However, the lesson was well received and had a great effect on the students and the teacher herself gained confidence. The experience they gained will lead to skills that will help them succeed in a global society in the future.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".