Influence of Experience and Motivation on Job Performance among Business Studies Teachers in Junior Secondary Schools in Rivers State
Bibliographic record
Abstract
The study investigated the influence of experience and motivation on job performance among business studies teachers in junior secondary schools in Rivers State. The study adopted a descriptive survey design. The population comprised of 39,560 business studies students drawn from 282 secondary schools across the 23 local government areas. A sample size of 1,329 was derived using simple random sampling technique. A structured questionnaire titled “Business Studies Teachers’ Experience and Motivation Performance Scales (BSTEMS) was used as instrument for data collection. Three experts validated the instrument while Pearson Product Moment Correlation Coefficient (PPMC) was used to obtain the reliability coefficient of 0.98. Two research questions and two null hypotheses tested at 0.05 level of significant guided the study. A total of 1329 copies of the questionnaire was retrieved and used for the study. The items were rated on four (4) point rating scale; mean and standard deviation were used to analyze the research questions while z-test was used in testing the formulated hypotheses. The findings revealed that gender has no influence on teachers’ job performance. The researchers recommended that Teachers need to be motivated through workshops and conferences for effective job performance in business studies and Government should establish business studio to make teaching and learning of business studies more concrete and creative.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".