Development of Examination Behaviour Inventory: An Integrity Measure for Prevention of Cheating in School Exams
Bibliographic record
Abstract
Cheating in examinations is an educational menace that has threatened the very essence of schooling in most countriesof the world. Therefore, it has become imperative for researchers in education to seek alternative strategies for curbingit in order to restore the dignity of school examinations as an instrument for assessing actual educational attainment bystudents. This research study addresses this challenge by developing an inventory that could be used to measure theexamination behaviour of prospective candidates for school certificate examinations. The rationale for developing theinstrument is based on providing a tool for identifying students who have positive tendencies towards engaging incheating behaviour during school examinations. The initial sample used for the validation of the ExaminationBehaviour Inventory was 2000 candidates enrolled for the 2013 Senior School Certificate Examinations in Nigeriawhile the standardization of the instrument involved 4000 candidates. Cronbach Alpha index of the instrument is .843and Factor Analysis delineated 12 principal component factors. Other psychometric properties of the inventory and thedetailed processes involved in the construction, validation and standardization of this valuable educational instrumentis reported. The instrument is recommended to School Counsellors, Psychologists, Teachers, Administrators and otherstakeholders in education who are interested in the identification of prospective candidates who have a tendency toengage in cheating during examinations so as to apply proactive reformation on them.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".