The Study of the English Preparation Course to Enhance Students’ English Proficiency for the Standardized Test “Your Score is High; my Score is Low. How to Make it Higher?”
Bibliographic record
Abstract
“Your score is high; my score is low. How can I make it higher?” The author heard this in students’ conversations after they received their pre-test scores in the TOEIC training course. This is the major reason the author decided to conduct this study. Using the TOEIC score as part of the graduation requirements, the university designed and provided an English preparation course, namely EG1001: English for Proficiency Preparation, for students before taking the test. This small-scale research aims to investigate the effectiveness of the test preparation course in enhancing students’ English language proficiency and to explore which specific skills non-English major students need to improve for the TOEIC test. The study used pre-and post-tests to collect the language proficiency outputs of 57 higher education students in this course. Additionally, the study used their mini-test scores from each unit of the learning material to track their progress. The findings revealed that the post-test mean score (48.96) was higher than the pre-test mean score (40.58), indicating that after participating in this course, the participants improved their English proficiency. The results also suggested that students needed to practice more on the listening part of the TOEIC test.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".