Predicting College Math Success: Do High School Performance and Gender Matter? Evidence from Sultan Qaboos University in Oman
Bibliographic record
Abstract
The objective of this study was to evaluate the performance of students of college of Science of Sultan Qaboos University (SQU) in Calculus I course, and examine the predictive validity of the student’s high school performance and gender for Calculus I success. The data for the study was extracted from students’ database maintained by the Deanship of Admission and Registration office of SQU. The study considered a sample of 615 students who took Calculus I course during 2014 Spring semester. Both descriptive and inferential statistical techniques were used for data analysis. Predictive validity of selected factors was analyzed using Hierarchical regression analysis. The analysis revealed that female students entered in SQU with a higher average high school scores than male students, and many boys with lesser scores than girls were succeeded in getting admission in SQU. The results indicate that female students outperformed male students in both high school and college Calculus course. About 30% of the students obtained grades lower than C, of which 20% failed in the course. The proportion of students with F grade significantly higher among male students than female students (28% vs. 7%). The analysis revealed that gender, high school math score and overall high school score showed significant positive association with the performance in Calculus course. Differences among gender and high school performance should also be taken into consideration during the admission process to allow for more equal opportunities to all applicants and have fairer admission decisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".