Abstract
We describe Module Analysis for Multiple Choice Responses (MAMCR), a new methodology for carrying out network analysis on responses to multiple choice assessments. This method is used to identify modules of non-normative responses which can then be interpreted as an alternative to factor analysis. MAMCR allows us to identify conceptual modules that are present in student responses that are more specific than the broad categorization of questions that is possible with factor analysis and to incorporate non-normative responses. Thus, this method may prove to have greater utility in helping to modify instruction. In MAMCR the responses to a multiple choice assessment are first treated as a bipartite, student X response, network which is then projected into a response X response network. We then use data reduction and community detection techniques to identify modules of non-normative responses. To illustrate the utility of the method we have analyzed one cohort of postinstruction Force Concept Inventory (FCI) responses. From this analysis, we find nine modules which we then interpret. The first three modules include the following: Impetus Force, More Force Yields More Results, and Force as Competition or Undistinguished Velocity and Acceleration. This method has a variety of potential uses particularly to help classroom instructors in using multiple choice assessments as diagnostic instruments beyond the Force Concept Inventory.10 MoreReceived 13 January 2016DOI:https://doi.org/10.1103/PhysRevPhysEducRes.12.020131This article is available under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAssessmentCollective behavior in networksCommunity structureInstructional materials developmentPhysics Education ResearchNetworks
Highlights
Conceptual inventories have arguably played an important role in the transformation of science courses [1]
This paper introduces Module Analysis for Multiple Choice Responses (MAMCR), a novel method using network analysis and techniques of community detection to identify the structure of response patterns in conceptual inventories
We illustrate the utility of MAMCR by first comparing the method with factor analysis, by suggesting how MAMCR could be used in the context of a classroom, and by expanding the use of the methodology
Summary
Conceptual inventories have arguably played an important role in the transformation of science courses [1]. In physics the Force Concept Inventory (FCI) is the best known and most widely used conceptual inventory. It is a 30 question multiple choice assessment, with each question having a normative response (which is consistent with Newtonian mechanics) and several non-normative responses (often referred to as “distractors”) based on student responses to conceptual questions [2]. This format is common among a number of conceptual inventories.
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