Reverse Engineering a Multiple-Choice Test Blueprint to Improve Course Alignment
Bibliographic record
Abstract
Large introductory classes typically reflect courses that cover a broad landscape of learning and necessitate assessment formats that are efficient and reliable, making multiple-choice (MC) tests a good option. Designing a good quality MC test requires reflection on the appropriateness of the assessment format, designing a test blueprint, and writing MC questions following item-writing guidelines that align with course learning objectives and teaching/learning strategies. It is a time-consuming endeavour that often requires a group of question writers who are experts in the domain—both of which can be challenging considering the demands of teaching in higher education. Sometimes we want to adopt or adapt existing MC tests that we have access to. I suggest a five-step “reverse engineering” analytical process for selecting questions from existing MC tests to utilize in our course assessment, emphasizing the alignment process. In one of the steps, we will refer to the Taxonomy Table, a practical two-dimensional aid proposed by Anderson and Krathwohl (2001), to select the questions that cover a cognitive dimension landscape. The outcome of this process provides an effective visual representation of the alignment and enables us to decide on the utility of questions for the context of our unit and assessment objectives. Keywords: course alignment, assessment, multiple-choice test, taxonomy table.
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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 | 0.040 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".