Graded Response Method: Does Question Type Influence the Assessment of Critical Thinking?
Bibliographic record
Abstract
Graded Response Method (GRM) is an alternative to multiple-choice testing where students rank options accordingto their relevance to the question. GRM requires discrimination and inference between statements and is acost-effective critical thinking assessment in large courses where open-ended answers are not feasible. This studyexamined critical thinking assessment in GRM versus open-ended and multiple-choice questions composed fromBloom’s taxonomy in an introductory undergraduate course in anthropology and archaeology (N=53students).Critical thinking was operationalized as the ability to assess a question with evidence to support or evaluatearguments (Ennis, 1993). We predicted that students who performed well on multiple-choice from Bloom’staxonomy levels 4-6 and open-ended questions would perform well on GRM involving similar concepts. Highperforming students on GRM were predicted to have higher course grades. The null hypothesis was question typewould not have an effect on critical thinking assessment. In two quizzes, there was weak correlation between GRMand open-ended questions (R2=0.15), however there was strong correlation in the exam (R2=0.56). Correlations wereconsistently higher between GRM and multiple-choice from Bloom’s taxonomy levels 4-6 (R2=0.23,0.31,0.21)versus levels 1-3 (R2=0.13,0.29,0.18). GRM is a viable alternative to multiple-choice in critical thinking assessmentwithout added resources and grading efforts.
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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 | 0.125 | 0.373 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".