Teachers’ test construction competencies in examination-oriented educational system: Exploring teachers’ multiple-choice test construction competence
Bibliographic record
Abstract
This study explored the relationship between multiple choice test construction competence and the quality of multiple-choice tests among senior high school teachers in Ghana. In all, 157 teachers were selected from four senior high schools in the Kwahu-South District. Participants responded to self-designed questionnaire developed to assess teachers’ multiple-choice items construction competencies. A three-factor structure emanated from the exploratory factor analysis on teachers’ multiple choice test construction competence—content validity, item “options” handling, and test items assembling. Teachers in this study perceived more competence in ensuring content validity, followed by test item assembling, and handling of “options” (that is, alternatives) of the test items. The study also found serious problems with copies of multiple-choice items teachers have constructed for the students. Findings from this study provide unique and compelling evidence regarding teachers’ perceived test construction competence and analysis of their multiple-choice tests. Implications for policy and practice are discussed.
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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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".