The instinct fallacy: the metacognition of answering and revising during college exams
Autor: | Kathryn Feather, Shaun J. Zmuda, Tina Schwartzmeyer, Justin J. Couchman, Noelle E. Miller |
---|---|
Rok vydání: | 2015 |
Předmět: |
Fallacy
Higher education business.industry media_common.quotation_subject 05 social sciences 050301 education Metacognition 050105 experimental psychology Education Instinct Feeling Metacognitive Monitoring Metamemory 0501 psychology and cognitive sciences business Psychology 0503 education Social psychology Competence (human resources) Cognitive psychology media_common |
Zdroj: | Metacognition and Learning. 11:171-185 |
ISSN: | 1556-1631 1556-1623 |
Popis: | Students often gauge their performance before and after an exam, usually in the form of rough grade estimates or general feelings. Are these estimates accurate? Should they form the basis for decisions about study time, test-taking strategies, revisions, subject mastery, or even general competence? In two studies, undergraduates took a real multiple-choice exam, described their general beliefs and feelings, tracked their performance for each question, and noted any revisions or possible revisions. Beliefs formed after the exams were poor predictors of performance. In contrast, real-time metacognitive monitoring – measured by confidence ratings for each individual question – accurately predicted performance and were a much better decisional guide. Measuring metacognitive monitoring also allowed us to examine the process of revising an answer. Should a test-taker rely on their first choice or revise in the face of uncertainty? Experience seems to show that first instincts are correct. The decision-making literature calls this the first-instinct fallacy, based on extensive analysis of revisions, and recommends revising more. However, whereas revisions have been analyzed in great detail, previous studies did not analyze the efficacy of sticking with an original choice. We found that both revising and sticking resulted in significantly more correct than incorrect outcomes, with real-time metacognition predicting when each was most appropriate. |
Databáze: | OpenAIRE |
Externí odkaz: |