Using Think‐Alouds to Explore Problem‐Solving Procedures for Anatomy Students
Bibliographic record
Abstract
This session presents the use of think‐alouds as a powerful qualitative method to unravel student thought processes as they complete multiple‐choice assessments in anatomy. With this study, we aimed to uncover patterns in the reasoning process that students used when solving multiple‐choice questions. This required participants to verbalize their thought processes as they solved six multiple‐choice questions covering five key areas of anatomy. The multiple‐choice questions targeted three levels of cognitive functioning based off the ICE framework (Fostaty Young & Wilson, 2000), which includes recalling the fundamental facts to integrating information to creating new knowledge. Prior to the think‐alouds, the researchers designed a rubric with prompts corresponding to the different strategies that might be adopted by the students. All students were also asked to complete a practice activity in order to better understand the depth of responses that we were looking for in this study and to make students feel comfortable with this approach. One‐on‐one think aloud interviews were conducted with ten second‐year undergraduate students. Feedback from the initial individual think‐aloud sessions was used to generate a survey that was distributed across anatomy courses at Queen's University. We analyzed and categorized reasoning processes that were used by the 82 students who responded to our survey as well as those 10 students who participated in the think‐alouds. The think alouds were audio‐recorded and the qualitative content analysis protocol (Patton, 1990) was used to identify strategies that students followed when working through the questions. Amongst the challenges experienced with this approach were: students' tendencies to get distracted and go off topic; their ability to vocalize their thoughts or express limited information; finding the “appropriate” level of researcher prompting; as well as the labour intensive analysis process. We identified 16 different strategies that students used to solve multiple‐choice questions. Twelve of these have already been described and supported by the literature as procedures that learners frequently used in problem‐solving. Strategies like Checking, Clarifying, Comparing, Recalling, Relating, Predicting, and Recognizing, and Imitating are associated with the Bloom's taxonomy (Anderson and Krathwohl, 2001) and the ICE Framework (Fostaty Young & Wilson, 2000). In this session we will further describe the strategies used by the students and at the same time we will explore correlations amongst the level of the question, the strategies that were being used, and the likeliness of students getting the answer correct. We will conclude with a discussion of the challenges and benefits of using think‐alouds as a strategy for understanding and supporting student learning. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".