Peer Group Influence, Teacher-Student Interaction, and Indiscipline as Predictors of Students' Dropout Tendency in an Evening Continuing Education Programme
Bibliographic record
Abstract
The aim of this study was to investigate the predictive relationship of peer group influence, teacher-student interaction and indiscipline to students' dropout tendency in evening continuing education programmes. The context of this study is the southern senatorial district of Cross River State, Nigeria. The study adopted a predictive correlational research design, and the sample comprised 554 students randomly selected from 11 centres in the district. This represents 20% of the total population of students. The instrument used for data collection was a questionnaire titled: "Social Indicators and Dropout Tendency Scale" (SIDTS). The researchers collected the data that were analysed using Pearson product-moment correlation and multiple linear regression analyses at the .05 level of significance. The results revealed that peer group influence, teacher-student interaction and level of indiscipline collectively and individually predicted dropout tendency among students in evening continuing education programmes. It was recommended, among others, that the teachers discover diverse ways of making their teaching process lively by devising ways of engaging the students in the learning process by forming discussion groups that will promote healthy peer groups, which will increase their eagerness to come to school.
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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 | 0.007 |
| 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 | 0.000 |
| 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".