The Academic Achievement of Undergraduate Students with Different TOEFL iBT Score Profiles: A Replication Study
Bibliographic record
Abstract
This replication study sought to compare the academic achievement of undergraduate students with different score profiles on the TOEFL iBT. Two-step cluster analysis of TOEFL iBT section scores identified six clusters among 2,347 undergraduate students who met the required cutscore on the TOEFL iBT for admission to a Canadian English-medium university but had different scores on different sections of the test. The largest cluster, comprising one-third of the students, had high scores on all sections of the test. The second largest cluster had lower scores on writing compared to other sections. The six clusters differed in terms of their demographic characteristics and academic achievement. Students with higher listening and reading scores or higher reading and writing scores and lower scores on other sections tended to have comparatively lower academic achievement. This trend was especially noticeable when contrasted with students with high scores on all sections of the test. However, cluster effects were moderated by study major. Finally, the strength and direction of the correlations between TOEFL iBT total scores and academic achievement varied across clusters. The findings suggest that universities should tailor admission criteria and English language support to meet the diverse linguistic needs of students with varying proficiency profiles pursuing different study majors.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".