Item Parameter Drifts Across School Type in Educational Psychology Examination for 2022
Bibliographic record
Abstract
The study assessed item parameter drifts across school type in institution's examination body for educational psychology examination for 2022.Two research questions guided the study and two hypotheses were tested.The study adopted a descriptive survey research design.The population consisted all the 6,742 first year students from the 9 public and 5 Government approved private universities that enrolled for 2022/2023 academic session.A sample of 820 educational psychology students from the 14 schools approved to study educational psychology were selected through multistage sampling.The 2022 educational psychology multiple choice examination questions were adapted as instruments for the study.The instrument did not pass through validation and reliability as they were already good questions used by WAEC.Bilog MG software was used to answer the research questions while Analysis of Variance (ANOVA) was used to test the null hypotheses at 0.05 level of significance.The findings of the study among others showed that in 2022, seven items were deemed to have drifted significantly among the examinees.Of these seven drifting items, the drift parameter was positive for six items, indicating they became more difficult for the subsequent group of examinees.The one remaining items that exhibited significant drift became easier for the subsequent group of examinees.The study recommended among others that institution examination bodies should ensure that as items are re-used or repeated, response parameter must be updated and made more accurate to stated criteria before use.
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 | 0.037 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".