The Impact of Demographic and Academic Characteristics on Academic Performance
Bibliographic record
Abstract
The purpose of this study is to explore students’ demographic and academic characteristics that are associatedwith students’ academic performance during their undergraduate studies. Demographic and academiccharacteristic such as age, gender, nationality, high school major and high school GPA were studied as potentialdeterminants of academic performance. A sample of 700 students from the College of Business Studies at thePublic Authority for Applied Education was examined. Descriptive statistics, T-test and multiple regressionswere used. The results of the study reveal that students’ age, gender, high school major and high school GPA aresignificantly related to students’ academic performance. Our research has some implications. The findings revealthat student’s age, gender, high school major and high school GPA are significantly related to business students’academic performance. Interestingly, the findings highlight the positive and significant influence of sciencebackground on the academic performance of business students. This study contributes to the literature of theundergraduates business students academic performance. The findings of this study may be useful for educationsector, educators, college’s management and future researchers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".