Self-Regulated Learning Theory to Help with Test Taking Why does this learner perform poorly on tests? Using self-regulated learning theory to diagnose the problem and implement solutions. Andrews MA, Kelly WF, DeZee KJ. Acad Med. Nov 1, 2016. Epub ahead of print. Reviewed by Ann S. Botash Tags: GME, Assessment – MCQ, clinical reasoning, innovation report
Self-Regulated Learning Theory to Help with Test Taking
Why does this learner perform poorly on tests? Using self-regulated learning theory to diagnose the problem and implement solutions. Andrews MA, Kelly WF, DeZee KJ. Acad Med. Nov 1, 2016. Epub ahead of print.
Reviewed by Ann S. Botash
Tags: GME, Assessment – MCQ, clinical reasoning, innovation report
What was the study question?
Can intentional self-directed learning, using an author designed innovative method (Self-Regulated Learning Microanalytic Assessment and Training –SRL-MAT), improve test scores on multiple-choice in-training examinations?
How was the study done?
Diagnosing test-taking problems with SRL-MAT was performed with ~20 struggling second year internal medicine residents. Microanalytic questioning (self-evaluation) was applied to learners using practice multiple-choice, clinical vignette questions. The learners worked one-to-one with faculty through test questions and the Question Review Form (QRF) until enough data was collected to determine test-taking weaknesses. The authors targeted their questions to specific aspects of test-taking SRL subprocesses: task strategies; metacognitive monitoring and self-evaluation; causal attributions; and adaptive inferences. The task strategy questions were designed to assess the extent to which the learner uses well-developed disease scripts to arrive at the correct diagnosis. Task strategies and self-evaluation occur during the question/answer process, before the learner knows the answer to the question. Then causal attributions and adaptive inferences are determined once the learner knows the answer. The authors describe their faculty development and share the QRF.
What were the results?
The authors categorize the most common test-taking problems into six categories: lack of script recognition, lack of script specificity, premature closure, underconfidence, maladaptive causal attributions, and inappropriate adaptive inferences. The authors provide links to supplemental digital videos to demonstrate these learner categories (http://links.lww.com/ACADMED/A389) They report doubling of the average in-training exam score improvement from second to third year in residency compared to historical data.
What are the implications?
Validated methods to help poor test-takers are desperately needed. This program shows promising results and ease of implementation that will enable use at other institutions. This method should be further studied at multiple institutions, across disciplines, health care professions, and levels of learners.
Editor’s Note: The reliance on illness scripts within this model works particularly well for second-year residents. It will be interesting to see how it applies to medical students, many of whom have not developed any scripts yet. (JG)