Bibliographic record
Abstract
This article examined self-concept, social network and study habit as predictors of students' educational success using some selected post-primary schools in Lagos metropolis, Nigeria, as a case study.Questionnaires were used to collect information from 150 students in five secondary schools in the state.Three hypotheses were formulated and tested using Pearson Product Moment Correlation Statistics.The results revealed a significant connection between social network and students' self-concept, based on the fact that r-cal (0.206) was greater than the r-crit value (0.120) @ 0.05 level of significance.Further to that, it was found that students' study habits showed a significant correlation with both students' academic performance and their self-concept using the value of r-cal (0.184) to be significantly greater than r-crit (0.024) at 0.05 level of significance and 148 degree of freedom.Lastly, the study revealed a significant relationship between students' study habits and their academic performance judging from the value of r-cal (0.139) that was greater than r-crit (0.089) at 0.05 level of significance and 148 degree of freedom.Based on these findings, recommendations were made Keywords: Self-concept, social network, study habit, students, academic performance Introduction: Wikipedia (2016) views academic performance as the outcome of education; it is the degree to which lecturers / tutors / teachers, students or institutions of learning have achieved their educational goals.Education according to Omonijo, Uche, Rotimi and Nwadialor (2014) provides humanity with understanding, knowledge, wisdom and information which could result in desired changes in their ways of life.Nonetheless, the accomplishment of these qualities rests on the innards of education, teaching methods and inclination of students to learn, coupled with positive character.However, our emphasis in this paper is on students' academic performance or accomplishments.In different institutions of learning worldwide, success of students in all level of education is determined by academic performance.Thus, the extent to which a student meets the criterion set out by his or her institution goes a long way to determine his or her success in the school environments.Examination is one of such criterions and it is being organised at a regular interval, using different expressions such as terms, semesters and sessions, depending on the level of education in question.Hence, pupils or students prove their worth via engaging in oral and written tests, presenting papers, spinning in assignments and participating in class discussion.When students must have done their parts, teachers commence their own part by marking scripts, assignments, term papers, scoring them, recording them and submitting them for scrutiny.In other words, the combination of students and teachers efforts, mostly, determines students' success in academia.This is usually observed from the elementary to the highest level of education worldwide.Previously, students' educational success was frequently determined through "ear than today" Bella, (2016, p.1).At that point in time, tutors used to engage in observation to assess students' performance.In recent times, Cumulative Grade Point Average (CGPA) calculated numerically is often used to measure students' performance (Bella, 2016), probably due to the advancement in science and
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.967 | 0.944 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".