The Development and Use of a Multiple-Choice Question (MCQ) Assessment to Foster Deeper Learning: An Exploratory Web-Based Qualitative Investigation
Bibliographic record
Abstract
This paper reports on the development and piloting of a new model of multiple-choice question (MCQ) assessment used in two undergraduate degree modules at a tertiary university. The new model was purposefully designed to promote deeper learning closely aligned with the SOLO taxonomy. Students were invited to participate in an exploratory qualitative study exploring their experience of learning using this new assessment. In total, 13 students completed an online open-ended qualitative questionnaire. Data was analyzed thematically. Four themes were generated: (a) empowered choice, (b) iterative reading, (c) forcing comparison, and (d) justified understandings. Findings suggest that the new model MCQ assessment promoted wider and more prolonged engagement with learning materials and fostered critical comparisons resulting in deeper learning. Limitations in study design mean that further research is merited to develop our model of MCQ assessment and enhance our understanding of students' learning experience.
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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 | 0.055 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".