Predictive Roles of Personality Traits and Self-efficacy in Academic Performance of Secondary School Students in Oyo State, Nigeria
Bibliographic record
Abstract
The study examined the predictive roles of personality traits and self-efficacy on the academic performance of Oyo State secondary school students. The study determined the relative and joint influences of the two variables on the academic performance of secondary school students. The study employed a descriptive survey design of the ex-post-facto approach. Nine hundred (900) respondents were drawn from 12 schools in Oyo-south senatorial district through a multistage sampling technique. Four valid instruments and students’ first-term scores in Mathematics and English provided data for the study. Data were analysed using Bi-variate and Multiple regression statistics. Results revealed that: 32.3% and 29.2% of variations in the academic performance of secondary school students were due to personality traits and self-efficacy respectively; personality traits (t = 14.268, p<0.05) and self-efficacy (t = 12.481, p<0.05) were jointly responsible for 42.3% variation of students’ academic performance, with personality traits exercising more influence. The study is significant to teachers, parents, students and counsellors. Recommendations given in the study included: praising students for the smallest achievements; parents and educators to encourage their children or students to set clear achievable goals, directions and purposes for themselves; and teachers were encouraged to maintain good and effective communication with students as teaching quality can affect students’ self-belief and self-efficacy which impact on students' achievement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".