An Investigation of Factors that Influence the Academic Performance of Undergraduate Students of Public Universities in Ghana
Bibliographic record
Abstract
Abstract The aim of this study was to assess the extent to which background characteristics, students’ attitudes to learning, and students’ use of social media influence academic performance among undergraduates in Ghana. It was hypothesized that previous performance, hours of study, family income, having a personal study schedule, attending lectures regularly, participating in class discussions, taking notes during lectures, use of alcohol, and use of social media, among other factors will influence a student’s grade point average (GPA). Questionnaires were distributed to 1,500 students across four universities, of which 626 completed questionnaires were returned (N = 626). Correlation analysis showed that only hours of study was strongly related to GPA (r = .1, p = .05). Independent-samples t tests showed that students who had personal study schedules, attended lectures regularly, participated in class, took notes, chatted on Facebook, did not use alcohol, regarded a higher GPA as important, and who lived Off-campus, respectively, had a higher mean GPA. The study has contributed to the literature on factors that affect undergraduate academic performance in Ghana by investigating the effect of several demographic and attitudinal factors on student GPA. The findings indicate that to enhance academic performance it is important to influence students’ attitudes and dispositions toward learning, including lecture attendance, participation in class, self-initiated or independent learning, use of social media, and abstinence from alcohol.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".