Advertisement

Models to provide guidance in flipped classes using online activity

  • Pablo SchwarzenbergEmail author
  • Jaime Navon
  • Mar Pérez-Sanagustín
Article
  • 47 Downloads

Abstract

The flipped classroom gives students the flexibility to organize their learning, while teachers can monitor their progress analyzing their online activity. In massive courses where there are a variety of activities, automated analysis techniques are required in order to process the large volume of information that is generated, to help teachers take timely and appropriate actions. In these scenarios, it is convenient to classify students into a small number of groups that can receive dedicated support. Using only online activity to group students has proven to be insufficient to characterize relevant groups, because of that this study proposes to understand differences in online activity using differences in course status and learning experience, using data from a programming course (n = 409). The model built shows that learning experience can be categorized in three groups, each with different academic performance and distinct online activity. The relationship between groups and online activity allowed us to build classifiers to detect students who are at risk of failing the course (AUC = 0.84) or need special support (AUC = 0.73), providing teachers with a useful mechanism for predicting and improving student outcomes.

Keywords

Flipped classroom Learning experience Learning analytics Engagement 

Notes

Acknowledgements

This work was partially funded by Grant CONICYT-PCHA/doctorado Nacional/2013-21130045. This work was partially supported by the FONDECYT (11150231), and by the European Commission through the project LALA (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP).

Compliance with ethical standards

Conflict of interest

The authors have no conflict of interest with the execution or outcomes of this study.

References

  1. Agresti, A. (2007). Introduction to categorical data analysis. Hoboken: Wiley.Google Scholar
  2. Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542–550.Google Scholar
  3. Andergassen, M., Mödritscher, F., & Neumann, G. (2014). Practice and repetition during exam preparation in blended learning courses: Correlations with learning results. Journal of Learning Analytics, 1(1), 48–74.Google Scholar
  4. Anderson, A., Huttenlocher, D., Kleinberg, J., & Leskovec, J. (2014). Engaging with massive online courses. In Proceedings of the 23rd international conference on World Wide Web (pp. 687–698). ACM.Google Scholar
  5. Barba, P. G., Kennedy, G. E., & Ainley, M. D. (2016). The role of students’ motivation and participation in predicting performance in a MOOC. Journal of Computer Assisted Learning, 3(32), 218–231.Google Scholar
  6. Bergmann, J., & Sams, A. (2012). Flip your classroom: Reach every student in every class every day. Washington: International Society for Technology in Education.Google Scholar
  7. 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).Google Scholar
  8. Block, J. H., & Burns, R. B. (1976). 1: Mastery learning. Review of Research in Education, 4(1), 3–49.Google Scholar
  9. Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6), 4–16.Google Scholar
  10. Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2017). Predicting student performance from LMS data: A comparison of 17 blended courses using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17–29.Google Scholar
  11. Dillon, J., Bosch, N., Chetlur, M., Wanigasekara, N., Ambrose, G. A., Sengupta, B., & D’Mello, S. K. (2016). Student emotion, co-occurrence, and dropout in a MOOC context. In Proceedings of the 9th international conference on educational data mining (pp. 353–357).Google Scholar
  12. Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5–6), 304–317.Google Scholar
  13. Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. Internet and Higher Education, 28, 68–84.Google Scholar
  14. Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71.Google Scholar
  15. Gašević, D., Jovanović, J., Pardo, A., & Dawson, S. (2017). Detecting learning strategies with analytics: Links with self-reported measures and academic performance. Journal of Learning Analytics, 4(1), 113.Google Scholar
  16. Guskey, T. R. (2007). Closing achievement gaps: revisiting Benjamin S. Bloom’s, “Learning for Mastery”. Journal of Advanced Academics, 19(1), 8–31.Google Scholar
  17. Hattie, J. (2013). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. New York: Routledge.Google Scholar
  18. Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.Google Scholar
  19. Haykin, S. (2009). Neural networks and learning machines. Upper Saddle River: Pearson.Google Scholar
  20. Ho, A. D., Reich, J., Nesterko, S., Seaton, D. T., Mullaney, T., Waldo, J., & Chuang, I. (2014). HarvardX and MITx: The first year of open online courses, Fall 2012–Summer 2013 (January 21, 2014) (HarvardX and MITx Working Paper No. 1). http://dx.doi.org/10.2139/ssrn.2381263.
  21. Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers and Education, 98, 157–168.Google Scholar
  22. 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), 5.Google Scholar
  23. Khalil, M., & Ebner, M. (2017). Clustering patterns of engagement in massive open online courses (MOOCs): The use of learning analytics to reveal student categories. Journal of Computing in Higher Education, 29(1), 114–132.Google Scholar
  24. Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. In Proceedings of the third international conference on learning analytics and knowledge (pp. 170–179). ACM.Google Scholar
  25. Lanza, S. T., Flaherty, B. P., & Collins, L. M. (2003). Latent class and latent transition analysis. In I. Weiner (Ed.), Handbook of psychology (pp. 663–685). Hoboken: Wiley.Google Scholar
  26. Nakamura, J., & Csikszentmihalyi, M. (2014). The concept of flow. Flow and the foundations of positive psychology (pp. 239–263). Dordrecht: Springer.Google Scholar
  27. 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.Google Scholar
  28. Pekrun, R., Goetz, T., Daniels, L. M., Stupnisky, R. H., & Perry, R. P. (2010). Boredom in achievement settings: Exploring control-value antecedents and performance outcomes of a neglected emotion. Journal of Educational Psychology, 102(3), 531.Google Scholar
  29. Schwarzenberg, P., Navon, J., Nussbaum, M., Pérez-Sangustín, M., & Caballero, D. (2017). Learning experience assessment of flipped courses. Journal of Computing Higher Education, 1, 11.  https://doi.org/10.1007/s12528-017-9159-8.Google Scholar
  30. Sharma, K., Jermann, P., & Dillenbourg, P. (2015). Identifying styles and paths toward success in MOOCs. In Proceedings of the 8th international conference on educational data mining (pp. 408–411).Google Scholar
  31. Siemens, G., & Gasevic, D. (2012). Guest editorial—Learning and knowledge analytics. Educational Technology and Society, 15(3), 1–2.Google Scholar
  32. Zimmerman, B. J. (2013). From cognitive modeling to self-regulation: A social cognitive career path. Educational Psychologist, 48(3), 135–147.Google Scholar
  33. Zimmerman, B. J., & Campillo, M. (2003). Motivating self-regulated problem solvers. In J. Davidson & R. Sternberg (Eds.), The psychology of problem solving (pp. 233–262). New York: Cambridge University Press.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of EngineeringUniversidad Andrés BelloSantiagoChile
  2. 2.School of EngineeringPontificia Universidad Católica de ChileSantiagoChile

Personalised recommendations