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Learning experience assessment of flipped courses

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Abstract

The flipped classroom has shown a positive effect on academic performance and student satisfaction. However, there are relatively few studies that help us understand when or why this method has a positive effect on students, so as to aid the design of a flipped class. In this study, an instrument for assessing the student learning experience in flipped courses is proposed and validated, comparing the results of their application over two semesters in a university-level programming class with three flipped sections (n = 151) and four conventional ones (n = 226). We found that with a similar score in terms of learning experience, the achievement in a flipped course is slightly higher than in a lecture-based course, F(1,108) = 4.20, p = 0.04, d = 0.19. A multiple regression analysis was conducted for the effect on achievement of GPA, level of challenge, feedback and enjoyment. By controlling for these factors, students in flipped classes achieved higher scores than students in lecture-based courses, β = 0.1, t(370) = 2.4, p = 0.02. The expected improvement in achievement increases when features of the flipped classroom improve the student experience. Our results suggest that the design of a flipped class should consider the effect of different implementation features on student experience in order to select the most appropriate ones for a particular context.

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References

  • Adams, E. (2010). Fundamentals of game design (2nd ed.). Berkeley, CA: Pearson Education.

    Google Scholar 

  • Baepler, P., Walker, J. D., & Driessen, M. (2014). It’s not about seat time: Blending, flipping, and efficiency in active learning classrooms. Computers & Education, 78, 227–236.

    Article  Google Scholar 

  • Bergmann, J., & Sams, A. (2012). Flip your classroom: Reach every student in every class every day. Washington, DC: International Society for Technology in Education (ISTE).

    Google Scholar 

  • Bishop, J. L., & Verleger, M. A. (2013). The flipped classroom: A survey of the research. In ASEE National Conference Proceedings, Atlanta, GA (Vol. 30, No. 9).

  • Campbell, J., Horton, D., Craig, M., & Gries, P. (2014). Evaluating an inverted CS1. In Proceedings of the 45th ACM technical symposium on computer science education (pp. 307–312). ACM.

  • Chen, Y., Wang, Y., & Chen, N. S. (2014). Is FLIP enough? Or should we use the FLIPPED model instead? Computers & Education, 79, 16–27.

    Article  Google Scholar 

  • Clark, R. C., & Mayer, R. E. (2011). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. San Francisco, CA: Wiley.

    Book  Google Scholar 

  • Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York, NY: Harper Perennial.

    Google Scholar 

  • Deci, E. L., Eghrari, H., Patrick, B. C., & Leone, D. (1994). Facilitating internalization: The self-determination theory perspective. Journal of Personality, 62, 119–142.

    Article  Google Scholar 

  • Esteban-Millat, I., Martínez-López, F. J., Huertas-García, R., Meseguer, A., & Rodríguez-Ardura, I. (2014). Modelling students’ flow experiences in an online learning environment. Computers & Education, 71, 111–123.

    Article  Google Scholar 

  • Fang, X., Zhang, J., & Chan, S. (2013). Development of an instrument for studying flow in computer game play. International Journal of Human-Computer Interaction, 29(7), 456–470.

    Article  Google Scholar 

  • Felder, R. M., & Brent, R. (2005). Understanding student differences. Journal of Engineering Education, 94(1), 57–72.

    Article  Google Scholar 

  • Field, Andy, Miles, Jeremy, & Field, Zoe. (2012). Discovering statistics using R. London: SAGE Publications Ltd.

    Google Scholar 

  • Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., et al. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415.

    Article  Google Scholar 

  • Fu, F. L., Su, R. C., & Yu, S. C. (2009). EGameFlow: A scale to measure learners’ enjoyment of e-learning games. Computers & Education, 52(1), 101–112.

    Article  Google Scholar 

  • Gross, D., Pietri, E. S., Anderson, G., Moyano-Camihort, K., & Graham, M. J. (2015). Increased preclass preparation underlies student outcome improvement in the flipped classroom. CBE-Life Sciences Education, 14(4), ar36.

    Article  Google Scholar 

  • Haak, D. C., HilleRisLambers, J., Pitre, E., & Freeman, S. (2011). Increased structure and active learning reduce the achievement gap in introductory biology. Science, 332(6034), 1213–1216.

    Article  Google Scholar 

  • Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice-Hall Inc.

    Google Scholar 

  • Hattie, J. (2013). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. New York, NY: Routledge.

    Google Scholar 

  • Hattie, J., & Timperley, H. (2007). The power of feedback. Review of educational research, 77(1), 81–112.

    Article  Google Scholar 

  • Hayashi, Y., Fukamachi, K. I., & Komatsugawa, H. (2015). Collaborative learning in computer programming courses that adopted the flipped classroom. In 2015 International conference on learning and teaching in computing and engineering (LaTiCE) (pp. 209–212). IEEE.

  • Horton, D., & Craig, M. (2015). Drop, fail, pass, continue: Persistence in CS1 and beyond in traditional and inverted delivery. In Proceedings of the 46th ACM technical symposium on computer science education (pp. 235–240). ACM.

  • Hox, J. J., Moerbeek, M., & van de Schoot, R. (2010). Multilevel analysis: Techniques and applications. New York, NY: Routledge.

    Google Scholar 

  • Jenkins, T. (2002). On the difficulty of learning to program. In Proceedings of the third annual conference of the LTSN centre for information and computer sciences (Vol. 4, pp. 53–58).

  • Jensen, J. L., Kummer, T. A., & Godoy, P. D. D. M. (2015). Improvements from a flipped classroom may simply be the fruits of active learning. CBE-Life Sciences Education, 14(1), ar5.

    Article  Google Scholar 

  • Karahoca, A., Karahoca, D., & Yengin, İ. (2010). Computer assisted active learning system development for critical thinking in history of civilization. Cypriot Journal of Educational Sciences, 5(1), 4–25.

    Google Scholar 

  • Kim, M. K., Kim, S. M., Khera, O., & Getman, J. (2014). The experience of three flipped classrooms in an urban university: An exploration of design principles. The Internet and Higher Education, 22, 37–50.

    Article  Google Scholar 

  • Koulouri, T., Lauria, S., & Macredie, R. D. (2014). Teaching introductory programming: A quantitative evaluation of different approaches. ACM Transactions on Computing Education (TOCE), 14(4), 26.

    Google Scholar 

  • Law, E. L. C., van Schaik, P., & Roto, V. (2014). Attitudes towards user experience (UX) measurement. International Journal of Human–Computer Studies, 72(6), 526–541.

    Article  Google Scholar 

  • Mason, G., Shuman, T., & Cook, K. (2013). Comparing the effectiveness of an inverted classroom to a traditional classroom in an upper-division engineering course. IEEE Transactions on Education, 56(4), 430–435.

    Article  Google Scholar 

  • McLaughlin, J. E., Roth, M. T., Glatt, D. M., Gharkholonarehe, N., Davidson, C. A., Griffin, L. M., et al. (2014). The flipped classroom: A course redesign to foster learning and engagement in a health professions school. Academic Medicine, 89(2), 236–243.

    Article  Google Scholar 

  • Nakamura, J., & Csikszentmihalyi, M. (2014). The concept of flow. Flow and the foundations of positive psychology (pp. 239–263). Berlin: Springer.

    Google Scholar 

  • Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom: Applying self-determination theory to educational practice. Theory and Research in Education, 7(2), 133–144.

    Google Scholar 

  • O’Flaherty, J., & Phillips, C. (2015). The use of flipped classrooms in higher education: A scoping review. The Internet and Higher Education, 25, 85–95.

    Article  Google Scholar 

  • Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction. Fort Worth, TX: Harcourt Brace College Publishers.

    Google Scholar 

  • Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223–231.

    Article  Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68.

    Article  Google Scholar 

  • Shernoff, D. J., & Csikszentmihalyi, M. (2009). Flow in schools: Cultivating engaged learners and optimal learning environments. In R. Gilman, E. S. Huebner, & M. Furlong (Eds.), Handbook of positive psychology in schools (pp. 131–145). New York, NY: Routledge.

    Google Scholar 

  • Shernoff, D. J., Csikszentmihalyi, M., Shneider, B., & Shernoff, E. S. (2003). Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly, 18(2), 158.

    Article  Google Scholar 

  • Stevens, J. P. (2009). Applied multivariate statistics for the social sciences. New York, NY: Routledge.

    Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Upper Saddle River, NJ: Pearson Education Inc.

    Google Scholar 

  • Van Gog, T., Ericsson, K. A., Rikers, R. M., & Paas, F. (2005). Instructional design for advanced learners: Establishing connections between the theoretical frameworks of cognitive load and deliberate practice. Educational Technology Research and Development, 53(3), 73–81.

    Article  Google Scholar 

  • Vygotsky, L. S. (1978). Mind in society. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Walker, D. A. (2003). JMASM9: Converting Kendall’s tau for correlational or meta-analytic analyses. Journal of Modern Applied Statistical Methods, 2(2), 26.

    Google Scholar 

  • Wirkala, C., & Kuhn, D. (2011). Problem-based learning in K–12 education is it effective and how does it achieve its effects? American Educational Research Journal, 48(5), 1157–1186.

    Article  Google Scholar 

Download references

Acknowledgements

This work was funded by grant CONICYT-PCHA/doctorado Nacional/2013-21130045, FONDECYT-CONICYT 11150231 and the MOOC Maker EU Project (561533-EPP-1- 2015-1-ES-EPPKA2-CBHE-JP).

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Authors and Affiliations

  1. School of Engineering, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Santiago, Chile

    Pablo Schwarzenberg, Jaime Navon, Miguel Nussbaum, Mar Pérez-Sanagustín & Daniela Caballero

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  1. Pablo Schwarzenberg
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  2. Jaime Navon
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  3. Miguel Nussbaum
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  4. Mar Pérez-Sanagustín
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  5. Daniela Caballero
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Correspondence to Pablo Schwarzenberg.

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Appendix

Appendix

See Tables 10, 11, 12 and 13.

Table 10 Refinement of the measurement instrument (F: item was the same in initial and definitive version, A: item added on definitive version, D: item deleted on definitive version)
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Table 11 Factor Loadings and Reliability of the Instrument (Initial Version)
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Table 12 MSA Index for each question on the instrument
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Table 13 Reliability of scales of the final version of the instrument
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Schwarzenberg, P., Navon, J., Nussbaum, M. et al. Learning experience assessment of flipped courses. J Comput High Educ 30, 237–258 (2018). https://doi.org/10.1007/s12528-017-9159-8

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