The Link Between Academic Success and L2 Proficiency in the Context of Two Professional Programs
Bibliographic record
Abstract
This article reports on two studies conducted at the same university, one investigating the link between ESL scores on an advanced ESL test and the grade point average (GPA) obtained over two semesters and the other investigating the link between French second language (FSL) scores on an advanced L2 test and both the number of courses failed and the first semester GPA. Graham's (1987) review of the literature pointed out the problems associated with research that attempts to delineate the relationship between language proficiency and academic performance, including the nature of the measures used to define L2 proficiency; the definition of academic success, especially when the GPA reported may be based on unequal numbers of courses or on dissimilar courses; and the possible influence of other variables in determining academic success. Language proficiency in our two studies consisted of measures of listening (FSL only), reading comprehension, and writing ability based on communicative principles previously validated in other work. The participants in the ESL study were 34 overseas Chinese students who had all participated in a pre-study English for Academic Purposes (EAP) program and were subsequently enrolled in a Master of Business Administration (MBA) program, where they were tracked for 18 months. Those in the French study were Canadian L1 and L2 French speakers (N = 100, N = 36) enrolled in a three-year program in civil law. In both programs, all students took the same courses. In the ESL study, other data were obtained from interviews with professors and students, a questionnaire on reading practices, and final exam marks. In the FSL study, comparisons were made between L1 and L2 profiles on the measures outlined above and the students' incoming college grades. From these data it has been possible to provide more complete answers regarding the relationship of language proficiency to academic success than was possible in much previous work.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".