Emotional intelligence and self-concept as predictors of academic achievement among secondary school Chemistry students in South-East Nigeria
Bibliographic record
Abstract
The research investigated the predictive strengths of Emotional Intelligence (EI) and Self-concept (SC), singly and jointly on academic achievement of secondary school chemistry students. The predictive research design was adopted. The sample comprised 300 SS3 students (150female, 150male) drawn through a multistage sampling technique from 10 co-education public schools in South-East, Nigeria. Instrument for data collection was a questionnaire, which has 3 sections; section A is the demographic information, section B is a 33item Emotional Intelligence inventory adapted from Shuttle {1998), while section C is a 60item Self-concept scale adapted from Rastogi (1979). Data was analysed using the standardised multiple linear regression and the hierarchical/stepwise linear regression statistical methods. Results obtained reveal that, EI and SC significantly predict academic achievement of students both singly and jointly. Influence of gender was insignificant with both EI and SC. Based on findings, it was recommended, among others that classroom practices that aid students understanding and management of emotions as well as boost their self-confidence and capability judgement should be explore, initiated and sustained.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".