Mixed-format exams in higher education: Assessment of internal consistency reliability
Bibliographic record
Abstract
In higher education courses, instructors often use mixed-format exams composed of several types of questions such as essays, problem-solving, and multiple-choice to evaluate student performance. It is important to discriminate reliably amongst students according to their performance on final examinations. The lower the reliability of student exam scores, the greater the error associated with making decisions based on them. Why then have we found no previous studies of reliability for this, one of the most common types of exam? We investigated the reliability of student scores on 12 official mixed-format final exams used in 22 classes with 1012 students in six undergraduate courses taught by five professors in three fields of business (finance, accounting, and statistics). We focussed on estimating internal consistency reliability, which is essentially a measure of the reproducibility of test scores. Using coefficient omega, the most appropriate measure of assessing reliability of mixed-format exams, we found that in these 22 classes reliability averaged .85, with over 90% of the classes with reliabilities exceeding .80. These reliabilities are very high, comparable with those reported for professionally developed standarized tests and better than those reported recently for single-format multiple choice exams in higher education. http://dx.doi.org/10.4995/HEAd15.2015.364
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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 | 0.064 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".