home - login - register

PERC 2012 Abstract Detail Page

Previous Page  |  New Search  |  Browse All

Abstract Title: Using Cluster Analysis to Identify Intellectually Similar Groups of Students
Abstract: Research on context sensitivity suggests that many students are left in a mixed state of knowledge after an introductory science class. Some students will answer contextually related questions in the same way and some will answer differently based on the context of the question. Seven contextually-related questions were given to two semesters of introductory classes at the University of Arkansas. Model State Estimation is compared to traditional clustering algorithms as mechanisms to separate a class into subgroups with similar answering patterns. The k-means clustering algorithm is used to extract statistically similar subgroups of students. The requirements of the underlying population of students for successful clustering are investigated. For the questions investigated, we find that the class is well represented with only two subgroups, one that answers consistently correctly and one that consistently selects an incorrect answer representing a common misconception.
Abstract Type: Contributed Poster Presentation

Author/Organizer Information

Primary Contact: John Stewart
University of Arkansas
Physics Building
University of Arkansas
Fayetteville, AR 72701
Phone: 479-445-2522