The Effect of Two Scoring Methods on Multiple Choice Agricultural Science Test Scores
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study investigated the effect of two scoring methods on multiple choice agricultural sciences test scores, to find the most favourable method to be used, the interaction effect of two methods of scoring in the schools, types of school and the states. The research design used was combination of survey type and one short experimental design. A sample of 1,200 students was selected by stratified random sampling techniques in south western Nigeria. Two hypotheses were generated and tested at 0.05 level of significance using t - test and correlation analysis. The result of the analysis showed that, there was significant relationship between the performance of students whose scripts were marked with number right scoring method and those marked with logical choice weight scoring method. The study revealed that logical choice weight scoring method was a better method that favoured the scoring of the students in multiple choice Agricultural Science test. Based on this findings, it was recommended that logical choice weight should be introduced to teachers for use in the classroom as a new method of scoring multiple choice tests in both Junior and Senior Secondary Schools.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it