SOME INFLUENCES ON THE PERFORMANCE OF UNDERGRADUATE BUSINESS STUDENTS IN FINANCE COURSES
Bibliographic record
Abstract
This paper explores the relationship between grades in a first-year required statistics course and a second-year required finance course for undergraduate business students, as well as the relationship between finance grades and overall GPA. Background Although introductory statistics courses have the distinction of being the least favourite course of undergraduate commerce students (Zanakis & Valenzi, 1997), finance courses may be a close second. At a large Ontario university, both Introductory Finance (FIN) and Introductory Statistics (QMS) are required courses for all business students. While QMS has a high failure rate (19%), FIN has a high drop-out rate (30%) and a moderately high failure rate (10%) compared to other required courses. No student may take FIN without having a passing grade in QMS. FIN is normally taken in the first semester of the second year of the program. The usual evaluation framework is one midterm examination, consisting of a combination of multiple choice and open-ended problemsolving questions and a final examination with the same format. The course is structured around a
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".