Predictors of Performance in Introductory Finance: Variables within and beyond the Student’s Control
Bibliographic record
Abstract
This study examined variables that are within and beyond the control of students in explaining variations in performance in an introductory finance course. Regression models were utilized to consider whether the variables within the student’s control have a greater impact on course performance relative to the variables beyond the student’s control. Among the particular variables within the student’s control were the student’s inclination to procrastinate as evidenced by the relative delay the students exhibited in commencing online homework assignments. Also, separate measures were constructed to examine the effect of the accuracy of the homework submitted and the student’s actual completion of those assignments. Class attendance was also considered. The variables largely beyond the control of the student examined in this study were a measure of how far along in the undergraduate program the student had progressed when he/she enrolled in the introductory finance course, a measure of the credit load of the student in the semester when the student took the course, the student’s gender, the student’s overall academic ability and the relative strength of the student in comprehending quantitative versus verbal concepts. For the three measures of student performance studied, average homework grade, the mid-term exam grade and the final exam grade, all of the relevant variables within the student’s control demonstrated some impact on the various measures of performance. There was persuasive evidence that the variables within the student’s control were more influential in explaining differences in student performance than the variables beyond the student’s control.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".