Path-Analytic Study of Class, Gender, Qualification, Subject Taught, Teacher-Student Relationship and Teacher Job Confidence
Bibliographic record
Abstract
Teacher is a central factor in any educational system and the job confidence of any teacher goes a long way to boost educational benefits obtained by learners. Investigating into variables that are related to job-confidence to provide empirical information for better educational planning will be worthy effort. Hence the study investigated “Path-Analytic Study of Class, Gender, Qualification, Subject taught, Teacher-Student relationship and Teacher Job Confidence” The study adopted a Descriptive survey research method using path-analytic approach.The population for the study comprises of all the teachers in public secondary schools in the five Local Government Areas (LGAs) in Ibadan metropolis in Oyo state, Nigeria. Stratified random sampling was used to select one hundred and fifty (150) teachers from each LGA making a total of seven hundred and fifty (750) teachers, teaching in all the six levels (JS1 to SS3) in secondary schools. The selection cut across various subjects being taught in the schools. Two validated instruments were used to gather information for this study. These are: Teacher – Student Relationship Inventory and Teacher Job Confidence Scale with estimated Cronbach reliability coefficients(rs) of 0.80 and 0.95 respectively. Twenty five research assistants were engaged to collect the data (5 per LGA). Data collection lasted one week. The data collected were analyzed by adopting Path analysis, using AMOS 18 software. Class taught and teacher-student relationship were found to be meaningful causal of teacher Job Confidence with beta weights (βs) 0.079 and 0.077 respectively. Effort should be directed to train teachers on the expected relationship in the classroom. Experienced teachers also should be deployed to lower classes too.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".