Magnitude of Interaction between Language of Instruction of Prior Education and Learning Traits on Academic Achievement Scores of International Students
Bibliographic record
Abstract
This paper investigates the magnitude of difference of academic achievement scores by language of instruction of prior education and the magnitude of interaction between language of instruction of prior education and students’ preferred learning trait on academic performance of a group of international students in two teaching and learning practices. The magnitude of difference and magnitude of interaction were determined by using Cohen’s d with Hedges g correction. Coe’s spread sheet was used for the analysis. The study showed that the magnitude of difference of academic achievement score was found to be very small on Traditional method of Teaching and Learning (TTL) and small on Problem-Based method of Learning (PBL). However, the magnitude of interaction between language of instruction of prior education and learning traits varied from very small to large. It is a challenging task to accommodate international students from different language background and optimize the whole learning process in an English speaking teaching and learning environment.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".