Using module analysis for multiple choice responses: A new method applied to Force Concept Inventory data

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

Using module analysis for multiple choice responses : A new method applied to Force Concept Inventory data. / Brewe, Eric; Bruun, Jesper; Bearden, Ian.

In: Physical Review Physics Education Research, Vol. 12, No. 2, 2016, p. 1-32.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Brewe, E, Bruun, J & Bearden, I 2016, 'Using module analysis for multiple choice responses: A new method applied to Force Concept Inventory data', Physical Review Physics Education Research, vol. 12, no. 2, pp. 1-32. https://doi.org/10.1103/PhysRevPhysEducRes.12.020131

APA

Brewe, E., Bruun, J., & Bearden, I. (2016). Using module analysis for multiple choice responses: A new method applied to Force Concept Inventory data. Physical Review Physics Education Research, 12(2), 1-32. https://doi.org/10.1103/PhysRevPhysEducRes.12.020131

Vancouver

Brewe E, Bruun J, Bearden I. Using module analysis for multiple choice responses: A new method applied to Force Concept Inventory data. Physical Review Physics Education Research. 2016;12(2):1-32. https://doi.org/10.1103/PhysRevPhysEducRes.12.020131

Author

Brewe, Eric ; Bruun, Jesper ; Bearden, Ian. / Using module analysis for multiple choice responses : A new method applied to Force Concept Inventory data. In: Physical Review Physics Education Research. 2016 ; Vol. 12, No. 2. pp. 1-32.

Bibtex

@article{f0a75c4a1e0444daa0dd063038fb10c7,
title = "Using module analysis for multiple choice responses: A new method applied to Force Concept Inventory data",
abstract = "We describe a methodology for carrying out a network analysis of Force Concept Inventory (FCI) responses that aims to identify communities of incorrect responses. This method first treats FCI responses as a bipartite, student X response, network. We then use Locally Adaptive Network Sparsification\citep{Foti2011} and InfoMap\citep{rosvall2009map} community detection algorithms to find modules of incorrect responses. This method is then used to analyze post-FCI data from one cohort of Danish university students. From this analysis, we find nine modules which we then interpret. The first three modules include: Impetus Force, More Force Yields More Results, and Force as Competition or Undistinguished Velocity and Acceleration. This approach to analysis of FCI results is an alternative to factor analysis and yields results that could be useful for modifying classroom activity. As a methodology, this is a first step and has a variety of potential uses particularly to help classroom instructors in using the FCI as a diagnostic instrument. ",
keywords = "Faculty of Science, networks, physics education research, force concept inventory",
author = "Eric Brewe and Jesper Bruun and Ian Bearden",
note = "Journal skifter navn til Physical Review Physics Education Research",
year = "2016",
doi = "10.1103/PhysRevPhysEducRes.12.020131",
language = "English",
volume = "12",
pages = "1--32",
journal = "Physical Review Special Topics - Physics Education Research",
issn = "1554-9178",
publisher = "APS Physics",
number = "2",

}

RIS

TY - JOUR

T1 - Using module analysis for multiple choice responses

T2 - A new method applied to Force Concept Inventory data

AU - Brewe, Eric

AU - Bruun, Jesper

AU - Bearden, Ian

N1 - Journal skifter navn til Physical Review Physics Education Research

PY - 2016

Y1 - 2016

N2 - We describe a methodology for carrying out a network analysis of Force Concept Inventory (FCI) responses that aims to identify communities of incorrect responses. This method first treats FCI responses as a bipartite, student X response, network. We then use Locally Adaptive Network Sparsification\citep{Foti2011} and InfoMap\citep{rosvall2009map} community detection algorithms to find modules of incorrect responses. This method is then used to analyze post-FCI data from one cohort of Danish university students. From this analysis, we find nine modules which we then interpret. The first three modules include: Impetus Force, More Force Yields More Results, and Force as Competition or Undistinguished Velocity and Acceleration. This approach to analysis of FCI results is an alternative to factor analysis and yields results that could be useful for modifying classroom activity. As a methodology, this is a first step and has a variety of potential uses particularly to help classroom instructors in using the FCI as a diagnostic instrument.

AB - We describe a methodology for carrying out a network analysis of Force Concept Inventory (FCI) responses that aims to identify communities of incorrect responses. This method first treats FCI responses as a bipartite, student X response, network. We then use Locally Adaptive Network Sparsification\citep{Foti2011} and InfoMap\citep{rosvall2009map} community detection algorithms to find modules of incorrect responses. This method is then used to analyze post-FCI data from one cohort of Danish university students. From this analysis, we find nine modules which we then interpret. The first three modules include: Impetus Force, More Force Yields More Results, and Force as Competition or Undistinguished Velocity and Acceleration. This approach to analysis of FCI results is an alternative to factor analysis and yields results that could be useful for modifying classroom activity. As a methodology, this is a first step and has a variety of potential uses particularly to help classroom instructors in using the FCI as a diagnostic instrument.

KW - Faculty of Science

KW - networks

KW - physics education research

KW - force concept inventory

U2 - 10.1103/PhysRevPhysEducRes.12.020131

DO - 10.1103/PhysRevPhysEducRes.12.020131

M3 - Journal article

VL - 12

SP - 1

EP - 32

JO - Physical Review Special Topics - Physics Education Research

JF - Physical Review Special Topics - Physics Education Research

SN - 1554-9178

IS - 2

ER -

ID: 156567601