The Development of English Competency-Based Curriculum Integrated with Local Community for High School Students
Bibliographic record
Abstract
The purposes of this research were to study needs of high school students and teachers on competency-based curriculum integrated with local community for high school students, develop a curriculum based on the needs, implement the developed curriculum, and evaluate the developed curriculum. The samples of the needs study stage consisted of 244 high school students, and 82 teachers in schools located in Ubon Ratchathani, and Warinchamrab Municipalities, gained by quota sampling, and those for the curriculum implementation consisted of 34 high school students studying at Luekamhan Warinchamrab School, in the second semester of academic year 2018, gained by cluster sampling. The research instruments were the developed curriculum, questionnaires for the students and the teachers, a test of English expression, a test of writing, and an attitude evaluation form. The findings revealed the students and the teachers rated their needs on competency-based curriculum at a higher level, the developed curriculum consisted of vision, mission to achieve the students’ competency through the aims, contents, and instructional procedures concentrated on practicing and the evaluation focused on performances, the students had significantly higher learning achievement and writing skills after the curriculum implementation than those before the implementation at the level .01. The developed curriculum was evaluated by the students at medium level of its feasibility.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".