Quantitative and Qualitative Analysis of the Mental Models Deployed by Undergraduate Students in Explaining Thermally Activated Phenomena

Abstrakt

In this contribution we describe a research aimed at pointing out the quality of mental models undergraduate engineering students deploy when asked to create explanations for phenomena/processes and/or use a given model in the same context. Student responses to a specially designed written questionnaire are initially analyzed using researcher-generated categories of reasoning, based on the Physics Education Research literature on student understanding of the relevant physics content. The inferred students’ mental models about the analyzed phenomena are categorized as practical, descriptive, or explanatory, based on an analysis of student responses to the questionnaire. A qualitative analysis of interviews conducted with students after the questionnaire administration is also used to deepen some aspects which emerged from the quantitative analysis and validate the results obtained.
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Reference

Azmita, M. & Montgomery, R. (1993). Friendship, transactive dialogues, and the

development of scientific reasoning. Soc. Dev., 2(3), 202–221.

Bao, L. (1999). Dynamics of student modeling: A theory, algorithms, and application to

quantum mechanics [Dissertation thesis]. College Park: University of Maryland.

Bao, L. & Redish, E.F. (2006). Model analysis: Representing and assessing the

dynamics of student learning. Phys. Rev. ST Phys. Educ. Res., 2, 010103.

Berg, B. (1989). An introduction to content analysis. In B. Berg (Ed.), Qualitative

Research Methods, Boston: Allyn & Baron Press.

Brewer, J. & Hunter, A. (2006). Foundations of multimethod research: synthesizing

styles. Thousand Oaks: Sage Publications.

Brousseau, G. (1997). Theory of didactical situations in mathematics. Dordrecht:

Kluwer Academic.

Carley, K. & Palmquist, M. (1992). Extracting, representing and analyzing mental

models. Social Forces, 70(3), 601–636.

Chittleborough, G. & Treagust, D. F. (2007). The modelling ability of non-major

chemistry students and their understanding of the sub-microscopic level. Chemistry

Education Research and Practice, 8(3), 274–292.

Corpuz, E.D. & Rebello, N. S. (2011). Investigating students’ mental models and

knowledge construction of microscopic friction. I. Implications for curriculum design and

development. Phys. Rev. ST – Phys. Ed. Res., 7, 020102.

Fazio, C., Battaglia, O.R. & Di Paola, B. (2013). Investigating the quality of mental

models deployed by undergraduate engineering students in creating explanations: The

case of thermally activated phenomena. Physical Review Special Topics —- Physics

Education Research, 9, 020101.

Gilbert, J.K. & Boulter, C. (1998). Learning science through models and modelling. In

B. J. Fraser & K.G. Tobin (Eds.), International handbook of science education.

Dordrecht: Kluwer Academic Publisher.

Gilbert, J.K., Boulter, C. & Rutherford, M. (1998). Models in explanations: Part 1,

horses for courses? Int. J. Sci. Educ., 20(1), 83–97.

Gower, J.C. (1966). Some distance properties of latent root and vector methods used in

multivariate analysis. Biometrika, 53(3–4), 325–338.

Hrepic, Z., Zollman, D.A. & Rebello, N. S. (2005). Eliciting and representing hybrid

mental models. In Proceedings of the NARST 2005.

Johnson-Laird, P.N. (1983). Mental Models. Cambridge: Cambridge University Press.

Leech, N. L. & Onwuegbuzie, A. J. (2007). An array of qualitative analysis tools: A call

for data analysis triangulation. School Psychol. Quart., 22(4), 557–584.

Lerman, I.C. (1981). Classification et Analyse Ordinale Des Donn´ees. Paris: Dunod.

Lerman, I.C., Gras, R. & Rostam, H. (1981). Elaboration et évaluation d’un indice

d’implication pour des données binaries. I. Math. Sci. Hum., 74, 5–35.

Lerman, I.C., Gras, R. & Rostam, H. (1981). Elaboration et ´evaluation d’un indice

d’implication pour des donn´ees binaries. 2. Math. Sci. Hum., 75, 5–47.

Maloney, D. & Siegler, R. S. (1993). Conceptual competition in physics learning. Int. J.

Sci. Educ., 15(3), 283–295.

Mantegna, R.N. (1999). Hierarchical structure in financial markets. Eur. Phys. J.,

(1), 193–196.

Marton, F. (1988). Describing and improving learning. In R.R. Schmeck (Ed.), Learning

strategies and learning styles. New York: Plenum Press.

Marton, F. & Booth, S. (1997). Learning and awareness. Mahwah: Lawrence Erlbaum

Associates.

Mercer, N., Dawes, L., Wegerif, R. & Sams, C. (2004). Reasoning as a scientists: ways of

helping children to use language to learn science. Brit. Educ. Res. J., 30(3), 359–377.

Onwuegbuzie, A. J., Leech, N. L., Slate, J.R., Stark, M., Sharma, B., Frels, R., Harris, K.

& Combs, J.P. (2012). An exemplar for teaching and learning qualitative research.

Qual. Rep., 17(1), 16–77.

Sperandeo-Mineo, R. M., Fazio, C. & Tarantino, G. (2006). Pedagogical content

knowledge development and pre-service physics teacher education: a case study. Res.

Sci. Ed., 36(3), 235–268.

Tashakkori, A. & Teddlie, C. (Eds.). (2003). Handbook of mixed methods in social

& behavioral research. Thousand Oaks: Sage Publications.

Vygotsky, L. S. (1986). Thought and Language. Cambridge: MIT Press.

Weber, R.P. (1990). Basic content analysis. Beverly Hills: Sage Publications.

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