June 24, 2017
June 24, 2017
June 28, 2017
NSF Grantees Poster Session
Assessing conceptual understanding in large engineering courses is a challenging task. When we consider that assessment in engineering education is often performed in a deterministic fashion and does not include measures of student uncertainty, the challenge is even greater. Multiple choice questions are some of the most familiar deterministic assessment instruments used in large classes. In this paper, we proposed and implemented a modified version of multiple choice response options that captures quantitative data on student self-confidence in knowing the correct answer. Using the coin distribution response method, students are given a hypothetical allotment of 100 coins that can be distributed across the answer choices for each question. The students are informed that the coin assignments should represent the likelihood that each answer choice is the correct one.
The coin distribution response method was trial-tested and evaluated in consecutive semesters of a statics course in civil and environmental engineering. During a two-week span each semester, three short quizzes were administered in this course using multiple choice questions to evaluate conceptual understanding of vectors. Results show that students develop and use different patterns to assign coins. These patterns can change from question to question. An assignment of 100 coins to a single answer choice was the most frequent response. More than 85% of those responses were correct. When students chose to distribute coins, a split of coins to two answer choices was the most common. This occurred more than twice as often as the combined occurrences of distributing coins to three and four answer choices. Collectively, about 63% of responses with coins assigned to at least two choices were correct.
Madarshahian, R., & Chen, C., & Caicedo, J. M., & McAnally, A. S., & Pierce, C. E., & Terejanu, G. (2017, June), Board # 119 : Using Multiple Choice Responses to Assess Uncertainty in Student Understanding of Vector Concepts Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--27706
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