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Freshman Engineering’ Reasoning Strategies When Answering FCI Questions: A Case Study

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Abstract

Force Concept Inventory (FCI) is a questionnaire commonly used to assess students’ conceptual understanding of Newtonian Mechanics. We show that Cluster Analysis methods can be used to study student answers to FCI by finding their reasoning strategies on Newtonian Mechanics. Our analysis is performed to data obtained by a sample of freshman engineering students just at the beginning of their first General Physics course. The analysis takes into account the decomposition of the force concept into the conceptual dimensions suggested by test authors and successive researches. We identified groups of students with similar answering strategies, characterised by correct answers, as well as by non-correct answers showing student misconceptions/nonnormative conceptions. Such answering strategies give insights into the relationships between the student force concepts and their ability to describe and/or explain motions.

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Notes

  1. 1.

    We included questions involving Newton’s first or second law in the same subtest. Such a view can be also pointed out from the analysis of other interview studies (Brokes and Etkina 2009).

  2. 2.

    In this case the distance between two students, is: \( {\mathrm{d}}_{ij}=\sqrt{2\bullet \left(1-{R}_{\mathrm{bin}}\right)} \), where Rbin is the correlation coefficient. A distance dij between two students equal to zero means that they are completely similar (Rbin = 1), while a distance dij = 2 shows that the students are completely dissimilar (Rbin = −1). When the correlation between two students is 0 their distance is \( \sqrt{2} \).

References

  • Bao L, Redish EF (2006) Model analysis: representing and assessing the dynamics of student learning. Phys Rev ST Phys Educ Res 2:010103

    Article  Google Scholar 

  • Bao L, Hogg K, Zollman D (2002) Model analysis of fine structures of students models: an example with Newton’s third law. Am J Phys 70:766

    Article  ADS  Google Scholar 

  • Battaglia OR, Di Paola B (2015) A quantitative method to analyse an open-ended questionnaire: a case study about the Boltzmann Factor. Nuovo Cimento Soc Ital Fis C 38(3)

    Google Scholar 

  • Battaglia OR, Di Paola B, Fazio C (2017a) K-means clustering to study how student reasoning lines can be modified by a learning activity based on feynman’s unifying approach. EURASIA J Math Sci Technol Educ 13(6)

    Google Scholar 

  • Battaglia OR, Di Paola B, Fazio C (2017b) A quantitative analysis of educational data through the comparison between hierarchical and not-hierarchical clustering. EURASIA J Math Sci Technol Educ 13(8)

    Google Scholar 

  • Battaglia OR, Di Paola B, Adorno DP, Pizzolato N, Fazio C (2019) Evaluating the effectiveness of modelling-oriented workshops for engineering undergraduates in the field of thermally activated phenomena. Res Sci Educ 49(5):1395–1413

    Article  Google Scholar 

  • Borg I, Groenen P (1997) Modern multidimensional scaling. Springer, New York, NY

    Book  MATH  Google Scholar 

  • Brewe E, Bruun J, Bearden IG (2016) Using module analysis for multiple choice responses: a new method applied to Force Concept Inventory data. Phys Rev Phys Educ Res 12:020131

    Article  Google Scholar 

  • Brookes DT, Etkina E (2009) “Force,” ontology, and language. Phys Rev Spec Top Phys Educ Res 5(1)

    Google Scholar 

  • Calinski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat 3(1):1–27

    MathSciNet  MATH  Google Scholar 

  • Di Paola B, Battaglia OR, Fazio C (2016) Non-Hierarchical Clustering as a method to analyse an open-ended questionnaire on algebraic thinking. S Afr J Educ 36(1):1–13

    Article  Google Scholar 

  • Ding L, Beichner R (2009) Approaches to data analysis of multiple-choice questions. Phys Rev ST Phys Educ Res 5:020103

    Article  Google Scholar 

  • Everitt BS, Landau S, Leese M, Stahl D (2011) Cluster analysis. John Wiley & Sons, Ltd, Chichester

    Book  MATH  Google Scholar 

  • Fazio C, Battaglia OR, Di Paola B (2013) Investigating the quality of mental models deployed by undergraduate engineering students in creating explanations: the case of thermally activated phenomena. Phys Rev Spec Top Phys Educ Res 9(2)

    Google Scholar 

  • Fulmer GW (2015) Validating proposed learning progressions on force and motion using the Force Concept Inventory: finding from Singapore secondary schools. Int J Sci Math Educ 13(6):1235–1254. https://doi.org/10.1007/s10763-014-9553-x

    Article  Google Scholar 

  • Gilbert JK, Boulter CJ (1998) Learning science through models and modelling. In: Frazer B, Tobin K (eds) The international handbook of science education. Kluwer, Dordrecht, pp 53–66

    Chapter  Google Scholar 

  • Gower JC (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika Trust 53:3–4

    Article  MathSciNet  MATH  Google Scholar 

  • Grunspan DZ, Wiggins BL, Goodreau SM (2014) Understanding classrooms through social network analysis: a primer for social network analysis in education research. Cell Biol Educ 13:167

    Google Scholar 

  • Hake RR (1998) Interactive engagement versus traditional methods: a six-thousand-student survey of mechanics test data for introductory physics courses. Am J Phys 66:64

    Article  ADS  Google Scholar 

  • Hestenes D, Halloun I (1995) Interpreting the force concept inventory: a response to March 1995 critique by Huffman and Heller. Phys Teach 33:502–506

    Article  ADS  Google Scholar 

  • Hestenes D, Jackson J (2007) Revised table II for the Force Concept Inventory (Unpublished). http://modeling.asu.edu/R&E/Research.html

  • Hestenes D, Wells M, Swackhammer G (1992) Force concept inventory. Phys Teach 30:141–151

    Article  ADS  Google Scholar 

  • Jammer M (1957) Concepts of force. Harvard University Press, Cambridge, MA

    Google Scholar 

  • MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: LeCam LM, Neyman J (eds) Proceedings of the 5th Berkely symposium on mathematical statistics and probability 1965/66, vol I. University of California Press, Berkeley, CA, pp 281–297

    Google Scholar 

  • Mantegna RN (1999) Hierarchical structure in financial markets. Eur Phys J B 11:193197

    Article  Google Scholar 

  • Rouseeuw PJ (1987) Silhouttes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  Google Scholar 

  • Savinainen A, Viiri J (2008) The force concept inventory as a measure of students conceptual coherence. Int J Sci Math Educ 6(4):719–740

    Article  Google Scholar 

  • Scott TF, Schumayer D (2017) Conceptual coherence of non-Newtonian worldviews in Force Concept Inventory data. Phys Rev Phys Educ Res 13:010126. https://doi.org/10.1103/PhysRevPhysEducRes.13.010126

    Article  Google Scholar 

  • Scott TF, Schumayer D, Gray AR (2012) Exploratory factor analysis of a Force Concept Inventory data set. Phys Rev ST Phys Educ Res 2012:020105

    Article  Google Scholar 

  • Semak MR, Dietz RD, Pearson RH, Willis CW (2017) Examining evolving performance on the Force Concept Inventory using factor analysis. Phys Rev Phys Educ Res 13:019903. https://doi.org/10.1103/PhysRevPhysEducRes.13.010103

    Article  Google Scholar 

  • Springuel RP, Wittmann MC, Thompson JR (2007) Applying clustering to statistical analysis of student reasoning about two-dimensional kinematics. Phys Rev ST Phys Educ Res 3:2

    Article  Google Scholar 

  • Stewart J, Miller M, Audo C, Stewart G (2012) Using cluster analysis to identify patterns in students’ responses to contextually different conceptual problems. Phys Rev Spec Top Phys Educ Res 8(2)

    Google Scholar 

Download references

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Correspondence to Onofrio R. Battaglia .

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Battaglia, O.R., Fazio, C. (2021). Freshman Engineering’ Reasoning Strategies When Answering FCI Questions: A Case Study. In: Sidharth, B.G., Murillo, J.C., Michelini, M., Perea, C. (eds) Fundamental Physics and Physics Education Research. Springer, Cham. https://doi.org/10.1007/978-3-030-52923-9_15

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