Students’ approaches to solving R-FCI tasks observed by eye-tracking method

Martina Kekule; Jouni Viiri

doi:10.14712/18047106.1010

Vol. 9 No. 2 (2018): sciED Research Reports

Students’ approaches to solving R-FCI tasks observed by eye-tracking method

Martina Kekule ¹ , Jouni Viiri ²

Univerzita Karlova Praha
University of Jyväskylä Jyväskylä

DOI: https://doi.org/10.14712/18047106.1010

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Kekule, M., & Viiri, J. (2018). Students’ approaches to solving R-FCI tasks observed by eye-tracking method. Scientia in Educatione, 9(2), 117-130. https://doi.org/10.14712/18047106.1010

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Abstract

This study sought to assess the representational format of task options in the representational variant of the force concept Inventory (R-FCI) test, namely its impact on students’ problem-solving approaches. This was done with the help of eye-tracking equipment. 35 high-school students solved four tasks, mainly from the R-FCI test, which sought to assess the student’s understanding of Newton’s 1st and 2nd Law of Motion. As they were trying to solve the problems, their gazes were tracked by TobiiTX300. A comparison between students who provided the correct and incorrect answer was subsequently carried out. The correctly answering students very quickly found the correct solution both in verbal and graph representation. For motion map representation, they usually compared and made decision between two options. The incorrectly answering students did not show any consistent strategy except they paid the least attention to the correct answer. Moreover, two case stud studies of correctly and incorrectly answering students were described.

References

Bojko, A. (2013). Eye tracking the user experience: A practical guide to research. Brooklyn, New York: Rosenfeld Media. Search in Google Scholar
Chen, S., She, H., Chuang, M., Wu, J., Tsai, J. & Jung, T. (2014). Eye movements predict students’ computer-based assessment performance of physics concepts in different presentation modalities. Computers & Education, 74, 61–72. Search in Google Scholar
Duchowski, A. (2006). Eye tracking methodology. Theory and practice. 2nd edition. London: Springer. Search in Google Scholar
Gegenfurtner, A., Lehtinen, E. & Saljo, R. (2011). Expertise differences in the comprehension of visualizations: a meta-analysis of eye-tracking research in professional domains. Educational Psychology Review, 23, 523–552. Search in Google Scholar
Nieminen, P., Savinainen, A. & Viiri, J. (2010). Force concept inventorybased multiple choice test for investigating students’ representational consistency. Physical Review Special Topics – Physics Education Research, 6, 020109. Search in Google Scholar
Hestenes, D. (1997). Modeling methodology for physics teachers. In The changing role of physics departments in modern universities. Proceedings of the International Conference on Undergraduate Physics Education, College Park, 1996, AIP Conference Proceedings No. 399, New York. Search in Google Scholar
Hestenes, D., Wells, M. & Swackhamer, G. (1992). Force concept inventory. The Physics Teacher, 30, 141–158. Search in Google Scholar
Chi, M.T. H., Feltovich, P. J. & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152. Search in Google Scholar
Jouni, V., Kekule, M., Isoniemi, J. & Hautala, J. (2017). Eye-tracking the effects of representation on students’ problem solving approaches In Proceedings of the annual FMSERA symposium 2016 (pp. 88–98). Finnish Mathematics and Science Education Research Association (FMSERA). Retrieved from https://journal.fi/fmsera/article/view/60941/27043 Search in Google Scholar
Just, M.A. & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87, 329–354. Search in Google Scholar
Kekule, M. (2014). Výzkum pomocí oční kamery ve fyzikálním vzdělávání. Scientia in educatione, 5(2), 58–73. Search in Google Scholar
Kekule, M. (2015). Qualitative approach of eye-tracking research in science education. In F. Dabaj (Ed.), Proceedings of International Conference on Contemporary Issues in Education (104–111). Dubai, UAE. Search in Google Scholar
Kozhevnikov, M., Motes, M. & Hegarthy, M. (2007). Spatial visualization in physics problem solving. Cognitive Science, 31, 549–579. Search in Google Scholar
Madsen, A.M. et al. (2012). Difference in visual attention between those who correctly and incorrectly answer physics problems. Physical Review Special Topics – Physics Education Research, 8, 010122. Search in Google Scholar
Mareš, J. & Slavík, V. (1989). Dotazník stylu učení (Learning Style Inventory – LSI). Price Systems, Inc. Search in Google Scholar
Ohno, E., Shimojo, A. & Iwata, Mi. (2016). Analysis of problem solving processes in physics based on eye-movement data in key competences in physics teaching and learning. In Proceedings of GIREP 2015 conference. University of Wroclav. Search in Google Scholar
Smith, A., Mestre, J. & Ross, B. (2010). Eye-gaze patterns as students study worked-out examples in mechanics. Physical Review Special Topics – PER, 6, 020118. Search in Google Scholar

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[1] Bojko, A. (2013). Eye tracking the user experience: A practical guide to research. Brooklyn, New York: Rosenfeld Media. Search in Google Scholar

[2] Chen, S., She, H., Chuang, M., Wu, J., Tsai, J. & Jung, T. (2014). Eye movements predict students’ computer-based assessment performance of physics concepts in different presentation modalities. Computers & Education, 74, 61–72. Search in Google Scholar

[3] Duchowski, A. (2006). Eye tracking methodology. Theory and practice. 2nd edition. London: Springer. Search in Google Scholar

[4] Gegenfurtner, A., Lehtinen, E. & Saljo, R. (2011). Expertise differences in the comprehension of visualizations: a meta-analysis of eye-tracking research in professional domains. Educational Psychology Review, 23, 523–552. Search in Google Scholar

[5] Nieminen, P., Savinainen, A. & Viiri, J. (2010). Force concept inventorybased multiple choice test for investigating students’ representational consistency. Physical Review Special Topics – Physics Education Research, 6, 020109. Search in Google Scholar

[6] Hestenes, D. (1997). Modeling methodology for physics teachers. In The changing role of physics departments in modern universities. Proceedings of the International Conference on Undergraduate Physics Education, College Park, 1996, AIP Conference Proceedings No. 399, New York. Search in Google Scholar

[7] Hestenes, D., Wells, M. & Swackhamer, G. (1992). Force concept inventory. The Physics Teacher, 30, 141–158. Search in Google Scholar

[8] Chi, M.T. H., Feltovich, P. J. & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152. Search in Google Scholar

[9] Jouni, V., Kekule, M., Isoniemi, J. & Hautala, J. (2017). Eye-tracking the effects of representation on students’ problem solving approaches In Proceedings of the annual FMSERA symposium 2016 (pp. 88–98). Finnish Mathematics and Science Education Research Association (FMSERA). Retrieved from https://journal.fi/fmsera/article/view/60941/27043 Search in Google Scholar

[10] Just, M.A. & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87, 329–354. Search in Google Scholar

[11] Kekule, M. (2014). Výzkum pomocí oční kamery ve fyzikálním vzdělávání. Scientia in educatione, 5(2), 58–73. Search in Google Scholar

[12] Kekule, M. (2015). Qualitative approach of eye-tracking research in science education. In F. Dabaj (Ed.), Proceedings of International Conference on Contemporary Issues in Education (104–111). Dubai, UAE. Search in Google Scholar

[13] Kozhevnikov, M., Motes, M. & Hegarthy, M. (2007). Spatial visualization in physics problem solving. Cognitive Science, 31, 549–579. Search in Google Scholar

[14] Madsen, A.M. et al. (2012). Difference in visual attention between those who correctly and incorrectly answer physics problems. Physical Review Special Topics – Physics Education Research, 8, 010122. Search in Google Scholar

[15] Mareš, J. & Slavík, V. (1989). Dotazník stylu učení (Learning Style Inventory – LSI). Price Systems, Inc. Search in Google Scholar

[16] Ohno, E., Shimojo, A. & Iwata, Mi. (2016). Analysis of problem solving processes in physics based on eye-movement data in key competences in physics teaching and learning. In Proceedings of GIREP 2015 conference. University of Wroclav. Search in Google Scholar

[17] Smith, A., Mestre, J. & Ross, B. (2010). Eye-gaze patterns as students study worked-out examples in mechanics. Physical Review Special Topics – PER, 6, 020118. Search in Google Scholar

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