A Computerized Dynamic Assessment Approach to Evaluate Critical Thinking Among Psychology Undergraduates
Bibliographic record
Abstract
Psychology undergraduates need critical thinking (CT) skills to evaluate arguments, identify fallacies, and detect unwarranted assumptions amid information and misinformation. However, CT instruction and assessment often fall short due to the absence of robust assessment tools, undermining the effectiveness of CT pedagogy, and leaving students inadequately prepared to critically analyze information. This study aimed to address this gap by developing a diagnostic tool to assess psychology students' CT and explore its correlation with reading proficiency. Inspired by the Watson Glaser Critical Thinking Appraisal, the study developed a psychology-specific CT test with five subscales: Inferences, Assumptions, Deduction, Interpretation, and Arguments. Additionally, a novel "Truth multiple-choice" (TMC) format was introduced, requiring students to indicate their certainty about each option's accuracy. The computerized CT test developed for this study also incorporated dynamic assessment features, integrating multiple attempts and feedback based on Vygotsky’s Sociocultural Theory of Learning. Conducted in fall 2022 with 267 psychology students from a Canadian university, the study employed a 2×2 factorial design: Conventional Multiple-Choice (CMC) with one attempt and no feedback, CMC with multiple attempts and feedback, TMC with multiple attempts, and TMC with multiple attempts and feedback. Students also took the DIALANG Reading Test and Lawson et al.'s (2015) Psychological Critical Thinking Exam (PCTE), a validated psychology-specific CT test. Statistical analyses revealed that the TMC format with feedback and multiple attempts provided a more accurate understanding of students' CT skills through iterative feedback when contrasted with the PCTE. The relationship between reading proficiency and CT skills varied depending on the testing format and feedback mode. The study's findings indicate that the integration of TMC formats with detailed feedback serves as an effective mechanism for assessing students' CT. This approach provides educators with insights into students' CT strengths and weaknesses, enabling them to customize instructional strategies.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.003 | 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".