Exploring the structure of misconceptions in the Force Concept Inventory with modified module analysis

Module Analysis for Multiple-Choice Responses (MAMCR) was applied to a large sample of Force Concept Inventory (FCI) pretest and post-test responses ($N_{pre}=4509$ and $N_{post}=4716$) to replicate the results of the original MAMCR study and to understand the origins of the gender differences reported in a previous study of this data set. When the results of MAMCR could not be replicated, a modification of the method was introduced, Modified Module Analysis (MMA). MMA was productive in understanding the structure of the incorrect answers in the FCI, identifying 9 groups of incorrect answers on the pretest and 11 groups on the post-test. These groups, in most cases, could be mapped on to common misconceptions used by the authors of the FCI to create distactors for the instrument. Of these incorrect answer groups, 6 of the pretest groups and 8 of the post-test groups were the same for men and women. Two of the male-only pretest groups disappeared with instruction while the third male-only pretest group was identified for both men and women post-instruction. Three of the groups identified for both men and women on the post-test were not present for either on the pretest. The rest of the identified incorrect answer groups did not represent misconceptions, but were rather related to the the blocked structure of some FCI items where multiple items are related to a common stem. The groups identified had little relation to the gender unfair items previously identified for this data set, and therefore, differences in the structure of student misconceptions between men and women cannot explain the gender differences reported for the FCI.

[1]  David Hammer,et al.  Student resources for learning introductory physics , 2000 .

[2]  Paul W. Holland,et al.  An Alternate Definition of the ETS Delta Scale of Item Difficulty. Program Statistics Research. , 1985 .

[3]  J. M. Hughes,et al.  Nonparametric Sparsification of Complex Multiscale Networks , 2011, PloS one.

[4]  Verena D. Schmittmann,et al.  Qgraph: Network visualizations of relationships in psychometric data , 2012 .

[5]  S. Borgatti,et al.  Analyzing Affiliation Networks , 2011 .

[6]  Ibrahim A. Halloun,et al.  Common sense concepts about motion , 1985 .

[7]  Adrienne Traxler,et al.  Item-level gender fairness in the Force and Motion Conceptual Evaluation and the Conceptual Survey of Electricity and Magnetism , 2018, Physical Review Physics Education Research.

[8]  Andrew V Papachristos,et al.  Network exposure and homicide victimization in an African American community. , 2014, American journal of public health.

[9]  Xin Ma A META-ANALYSIS OF THE RELATIONSHIP BETWEEN ANXIETY TOWARD MATHEMATICS AND ACHIEVEMENT IN MATHEMATICS , 1999 .

[10]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[11]  L. Viennot Spontaneous Reasoning in Elementary Dynamics. , 1979 .

[12]  M. Chi,et al.  From things to processes: A theory of conceptual change for learning science concepts , 1994 .

[13]  Gay Stewart,et al.  Multidimensional item response theory and the Force Concept Inventory , 2018, Physical Review Physics Education Research.

[14]  Philip M. Sadler,et al.  Success in introductory college physics: The role of high school preparation , 2001 .

[15]  Fred B. Bryant,et al.  Science Anxiety, Science Attitudes, and Gender: Interviews from a Binational Study , 2010 .

[16]  A. Caramazza,et al.  Naive beliefs in “sophisticated” subjects: misconceptions about trajectories of objects , 1981, Cognition.

[17]  Susan D. Voyer,et al.  Gender differences in scholastic achievement: a meta-analysis. , 2014, Psychological bulletin.

[18]  Jenessa R. Shapiro,et al.  The Role of Stereotype Threats in Undermining Girls’ and Women’s Performance and Interest in STEM Fields , 2012 .

[19]  Peter Dalgaard,et al.  R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .

[20]  David Hammer,et al.  Misconceptions or P-Prims: How May Alternative Perspectives of Cognitive Structure Influence Instructional Perceptions and Intentions , 1996 .

[21]  Paula V. Engelhardt,et al.  Gender bias in the force concept inventory , 2012 .

[22]  M. Linn,et al.  Gender differences in verbal ability: A meta-analysis. , 1988 .

[23]  Shannon D. Willoughby,et al.  Exploring the preinstruction and postinstruction non-Newtonian world views as measured by the Force Concept Inventory , 2019, Physical Review Physics Education Research.

[24]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Rachel E. Scherr Modeling student thinking: An example from special relativity , 2007 .

[26]  Wendy M. Yen,et al.  Scaling Performance Assessments: Strategies for Managing Local Item Dependence , 1993 .

[27]  Anthony C. Davison,et al.  Bootstrap Methods and Their Application , 1998 .

[28]  Andrew R. Gray,et al.  Exploratory factor analysis of a Force Concept Inventory data set , 2012 .

[29]  Katharina Anna Zweig,et al.  Network Analysis Literacy , 2016, Lecture Notes in Social Networks.

[30]  R. Hake Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses , 1998 .

[31]  Sarah B. McKagan,et al.  Gender gap on concept inventories in physics: what is consistent, what is inconsistent,and what factors influence the gap? , 2013, 1307.0912.

[32]  J. Clement,et al.  Not all preconceptions are misconceptions: finding ‘anchoring conceptions’ for grounding instruction on students’ intuitions , 1989 .

[33]  So Yoon Yoon,et al.  A Meta-Analysis on Gender Differences in Mental Rotation Ability Measured by the Purdue Spatial Visualization Tests: Visualization of Rotations (PSVT:R) , 2013 .

[34]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[35]  Dorothy T. Thayer,et al.  Differential Item Performance and the Mantel-Haenszel Procedure. , 1986 .

[36]  Montserrat Benlloch,et al.  What Changes in Conceptual Change?: From Ideas to Theories1 , 2005 .

[37]  J. Clement Students’ preconceptions in introductory mechanics , 1982 .

[38]  Terry F. Scott,et al.  Students' Proficiency Scores within Multitrait Item Response Theory. , 2015 .

[39]  Janet Shibley Hyde,et al.  Cross-national patterns of gender differences in mathematics: a meta-analysis. , 2010, Psychological bulletin.

[40]  Diane F. Halpern,et al.  Sex differences in cognitive abilities, 2nd ed. , 1992 .

[41]  David Hammer,et al.  More than misconceptions: Multiple perspectives on student knowledge and reasoning, and an appropriate role for education research , 1996 .

[42]  D. Halpern Sex Differences in Cognitive Abilities , 1986 .

[43]  M. R. Semak,et al.  Examining evolving performance on the Force Concept Inventory using factor analysis , 2017 .

[44]  Robert H. Tai,et al.  Gender differences in introductory university physics performance: The influence of high school physics preparation and affective factors , 2007 .

[45]  P. C. Peters Even honors students have conceptual difficulties with physics , 1982 .

[46]  Seth DeVore,et al.  Examining the effects of testwiseness in conceptual physics evaluations , 2016 .

[47]  Jesper Bruun,et al.  Using module analysis for multiple choice responses: A new method applied to Force Concept Inventory data , 2016 .

[48]  Laura McCullough,et al.  Gender, Context, and Physics Assessment , 2004 .

[49]  Jeffry V. Mallow,et al.  Science Anxiety and Gender in Students Taking General Education Science Courses , 2004 .

[50]  E. Fennema,et al.  Gender differences in mathematics performance: a meta-analysis. , 1990, Psychological bulletin.

[51]  Ibrahim A. Halloun,et al.  The initial knowledge state of college physics students , 1985 .

[52]  Jun-ichiro Yasuda,et al.  Validating Two Questions in the Force Concept Inventory with Subquestions. , 2013 .

[53]  Bruce Waldrip,et al.  IMPACT OF A REPRESENTATIONAL APPROACH ON STUDENTS’ REASONING AND CONCEPTUAL UNDERSTANDING IN LEARNING MECHANICS , 2014 .

[54]  Robert C. Hudson,et al.  Re‐score the force concept inventory! , 1996 .

[55]  Zhi-Liang Zheng,et al.  Transcriptome comparison and gene coexpression network analysis provide a systems view of citrus response to ‘Candidatus Liberibacter asiaticus’ infection , 2013, BMC Genomics.

[56]  Ronald K. Thornton,et al.  Assessing student learning of Newton’s laws: The Force and Motion Conceptual Evaluation and the Evaluation of Active Learning Laboratory and Lecture Curricula , 1998 .

[57]  Santo Fortunato,et al.  Community detection in networks: A user guide , 2016, ArXiv.

[58]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[59]  Terry F. Scott,et al.  Conceptual coherence of non-Newtonian worldviews in Force Concept Inventory data , 2017 .

[60]  David E. Meltzer,et al.  Differences in Male/Female Response Patterns on Alternative-format Versions of the Force Concept Inventory , 2001 .

[61]  James D. Slotta,et al.  The Ontological Coherence of Intuitive Physics , 1993 .

[62]  John J. Clement,et al.  Using Bridging Analogies and Anchoring Institutions to Seal with Students' Preconceptions in Physics , 1993 .

[63]  Terry F. Scott,et al.  Central Distractors in Force Concept Inventory Data. , 2018 .

[64]  Adrienne Traxler,et al.  Networks identify productive forum discussions , 2018, Physical Review Physics Education Research.

[65]  Andrew F. Heckler,et al.  Systematic study of student understanding of the relationships between the directions of force, velocity, and acceleration in one dimension , 2011 .

[66]  Richard Gunstone,et al.  Student understanding in mechanics: A large population survey , 1987 .

[67]  Adrienne L. Traxler,et al.  Exploring the Gender Gap in the Conceptual Survey of Electricity and Magnetism , 2017 .

[68]  Jun-ichiro Yasuda,et al.  Analyzing False Positives of Four Questions in the Force Concept Inventory. , 2018 .

[69]  H. Touchette,et al.  A network theory analysis of football strategies , 2012, 1206.6904.

[70]  John J. Clement,et al.  Preconceptions in mechanics : lessons dealing with student' conceptual difficuties , 1994 .

[71]  M. Chi,et al.  Assessing Students' Misclassifications of Physics Concepts: An Ontological Basis for Conceptual Change , 1995 .

[72]  Douglas Huffman,et al.  What does the force concept inventory actually measure , 1995 .

[73]  D. Treagust,et al.  Conceptual change: A powerful framework for improving science teaching and learning , 2003 .

[74]  Nancy S. Cole,et al.  The ETS Gender Study: How Females and Males Perform in Educational Settings. , 1997 .

[75]  Gay Stewart,et al.  Gender Fairness within the Force Concept Inventory , 2017, 1709.00437.

[76]  David P Maloney,et al.  Surveying students’ conceptual knowledge of electricity and magnetism , 2001 .

[77]  J. Schuknecht,et al.  The Nation's Report Card[TM]: America's High School Graduates. Results of the 2009 NAEP High School Transcript Study. NCES 2011-462. , 2011 .

[78]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[79]  Jonas Richiardi,et al.  Graph analysis of functional brain networks: practical issues in translational neuroscience , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[80]  Lillian C. McDermott,et al.  STUDENTS' CONCEPTIONS AND PROBLEM SOLVING IN MECHANICS , 2009 .

[81]  Dinah Sparks,et al.  Gender Differences in Science, Technology, Engineering, and Mathematics (STEM) Interest, Credits Earned, and NAEP Performance in the 12th Grade. Stats in Brief. NCES 2015-075. , 2015 .

[82]  L. McDermott,et al.  Investigation of student understanding of the concept of acceleration in one dimension , 1981 .

[83]  A. diSessa Toward an Epistemology of Physics , 1993 .

[84]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[85]  Andrea A. diSessa,et al.  Knowledge in pieces : An evolving framework for understanding knowing and learning , 1988 .

[86]  Nathaniel Lasry,et al.  The puzzling reliability of the Force Concept Inventory , 2011 .

[87]  Ivica Aviani,et al.  Students’ Understanding of Velocity-Time Graphs and the Sources of Conceptual Difficulties/Učeničko i studentsko razumijevanje grafova vremenske promjene brzine i izvori konceptualnih poteškoća , 2014, Croatian Journal of Education - Hrvatski časopis za odgoj i obrazovanje.

[88]  D. Hestenes,et al.  Force concept inventory , 1992 .