Computerized Summative Assessment of Multiple-choice Questions: Exploring Possibilities with the Zimbabwe School Examination Council Grade 7 Assessments
Bibliographic record
Abstract
The purpose of this study was to develop educational software for online assessment of multiple choice responses (MCQs). An automated assessment software program, duly developed in this study can display assessment items, record candidates' answers, and mark and provide instant reporting of candidates' performance scores. Field tests of the software were conducted on four primary schools located in Bindura town using a previous year summative Grade 7 assessment set by the Zimbabwe School Examination Council (ZIMSEC). Results were that computerized assessment in mathematics has the potential to enhance the quality of assessment standards and can drastically reduce material costs to the examination board. The paper exposes test mode benefits inherent in computer-based assessments, such as one-item display and ease of candidates selecting/changing optional answers. It also informs the ongoing debate on possible enhancement of candidates' performance on a computer-based assessment relative to the traditional pen-and-paper assessment format. The need for the development of diagnostic instructional software to compliment computerized assessments is one of the recommendations of the study.
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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".