Analysis of Item Writing Flaws in a Communications Skills Test in a Ghanaian University
Bibliographic record
Abstract
Adhering to the rules governing the writing of multiple-choice test items will ensure quality and validity. However, realizing this ideal could be challenging for non-native English language teachers and students. This is especially so for non-native English language teachers because developing test items in a language that neither they nor their students use as their mother tongue raises a multitude of issues related to quality and validity. A descriptive study on this problem was conducted at a Technical University in Ghana which focused on item writing flaws in a communication skills test. The use of multiple-choice test in Ghanaian universities has increased over the last decade due to increasing student intake. A 20-item multiple-choice test in communication skills was administered to 110 students. The test items were analyzed using a framework informed by standard item writing principles based on the revised taxonomy of multiple-choice item-writing guides by Haladyna, Downing and Rodriguez (2002). The facility and discrimination index (DI) was calculated for all the items. In total, 60% of the items were flawed based on standard items writing principles. The most violated guideline was wording stems negatively. Pearson correlation analysis indicated a weak relationship between the difficulty and discrimination indices. Using the discrimination indices of the flawed items showed that 84.6 % of them had discrimination indices below the optimal level of 0.40 and above. The lowest DI was recorded by an item with which was worded negatively. The mean facility of the test was 45%. It was observed that the flawed items were more difficult than the non-flawed items. The study suggested that test items must be properly reviewed before they are used to assess students’ knowledge.
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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".