Utilizing online learning data to design face-to-face activities in a flipped classroom: a case study of heterogeneous group formation

Abstract

This study investigates the possibility of utilizing online learning data to design face-to-face activities in a flipped classroom. We focus on heterogeneous group formation for effective collaborative learning. Fifty-three undergraduate students (18 males, 35 females) participated in this study, and 8 students (3 males, 5 females) among them joined post-study interviews. For this study, a total of 6 student characteristics were used: three demographic characteristics obtained from a simple survey and three academic characteristics captured from online learning data. We define three demographic group heterogeneity variables and three academic group heterogeneity variables, where each variable is calculated using the corresponding student characteristic. In this way, each heterogeneity variables represents a degree of diversity within the group. Then, a two-stage hierarchical regression analysis was conducted to identify the significant group heterogeneity variables that influence face-to-face group achievement. The results show that the academic group heterogeneity variables, which were derived from the online learning data, accounted for a significant proportion of the variance in the group achievement when the demographic group heterogeneity variables were controlled. The interviews also reveal that the academic group heterogeneity indeed affected group interaction and learning outcome. These findings highlight that online learning data can be utilized to obtain relevant information for effective face-to-face activity design in a flipped classroom. Based on the results, we discuss the advantages of this data utilization approach and other implications for face-to-face activity design.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

References

  1. Bayne, S. (2015). What’s the matter with ‘technology-enhanced learning’? Learning, Media and Technology, 40(1), 5–20.

    Google Scholar 

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

    Google Scholar 

  3. Bishop, J. L. (2013). A controlled study of the flipped classroom with numerical methods for engineers. Unpublished doctoral dissertation, Utah State University, Logan, Utah.

  4. Bryant, S. M., & Albring, S. M. (2006). Effective team building: Guidance for accounting educators. Issues in Accounting Education, 21(3), 241–265.

    Google Scholar 

  5. Chan, T., Chen, C. M., Wu, Y. L., Jong, B. S., Hsia, Y. T., & Lin, T. W. (2010). Applying the genetic encoded conceptual graph to grouping learning. Expert Systems with Applications, 37(6), 4103–4118.

    Google Scholar 

  6. Cho, K. L., & Jonassen, D. H. (2002). The effects of argumentation scaffolds on argumentation and problem solving. Educational Technology Research and Development, 50(3), 5–22.

    Google Scholar 

  7. Cohen, E. G. (1994). Restructuring the classroom: Conditions for productive small groups. Review of Educational Research, 64(1), 1–35.

    Google Scholar 

  8. Gannod, G. C., Burge, J. E., & Helmick, M. T. (2008). Using the inverted classroom to teach software engineering. In Proceedings of the 30th international conference on software engineering (pp. 777–786). Leipzig, Germany.

  9. Graf, S., & Bekele, R. (2006). Forming heterogeneous groups for intelligent collaborative learning systems with ant colony optimization. In Proceedings of the 8th international conference on intelligent tutoring systems (pp. 217–226). Jhongil, Taiwan.

  10. Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology and Society, 15(3), 42–57.

    Google Scholar 

  11. Harrison, D. A., Price, K. H., & Bell, M. P. (1998). Beyond relational demography: Time and the effects of surface- and deep-level diversity on work group cohesion. Academy of Management Journal, 41(1), 96–107.

    Google Scholar 

  12. Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers and Education, 90, 36–53.

    Google Scholar 

  13. Hwang, G. J., Lai, C. L., & Wang, S. Y. (2015). Seamless flipped learning: A mobile technology-enhanced flipped classroom with effective learning strategies. Journal of Computers in Education, 2(4), 449–473.

    Google Scholar 

  14. Jo, I. H., Kim, D., & Yoon, M. (2015). Constructing proxy variable to measure adult learners’ time management strategies in LMS. Journal of Education Technology & Society, 18(3), 214–225.

    Google Scholar 

  15. Johnson, D. W., & Johnson, R. T. (1999). Making cooperative learning work. Theory into Practice, 38(2), 67–73.

    Google Scholar 

  16. Kinchin, I., & Hay, D. (2005). Using concept maps to optimise the composition of student groups: A pilot study. Issues and Innovations in Nursing Education, 51(2), 1–6.

    Google Scholar 

  17. Kinshuk, (2016). Designing adaptive and personalized learning environments. New York: Routledge.

    Google Scholar 

  18. Kong, S. C. (2011). An evaluation study of the use of a cognitive tool in a one-to-one classroom for promoting classroom-based dialogic interaction. Computers and Education, 57(3), 1851–1864.

    Google Scholar 

  19. Laakso, M. J., Myller, N., & Korhonen, A. (2009). Comparing learning performance of students using algorithm visualizations collaboratively on different engagement levels. Educational Technology and Society, 12(2), 267–282.

    Google Scholar 

  20. Lehman, S., Kauffman, D. F., White, M. J., Horn, C. A., & Bruning, R. H. (2001). Teacher interaction: Motivating at-risk students in web-based high school courses. Journal of Research on Technology in Education, 33(5), 1–20.

    Google Scholar 

  21. Lin, Y. T., Huang, Y. M., & Cheng, S.-C. (2010). An automatic group composition system for composing collaborative learning groups using enhanced particle swarm optimization. Computers & Education, 55(4), 1483–1493.

    Google Scholar 

  22. Mccann, T. M. (1989). National council of teachers of English student argumentative writing knowledge and ability at three grade levels. Research in the Teaching of English, 23(1), 62–76.

    Google Scholar 

  23. Moreno, J., Ovalle, D. A., & Vicari, R. M. (2012). A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics. Computers and Education, 58(1), 560–569.

    Google Scholar 

  24. Murphree, D. S. (2014). “Writing wasn’t really stressed, accurate historical analysis was stressed”: Student perceptions of in-class writing in the inverted, general education, university history survey course. The History Teacher, 47(2), 209–219.

    Google Scholar 

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

    Google Scholar 

  26. Pelled, L. H., Eisenhardt, K. M., & Xin, K. R. (1999). Exploring the black box: An analysis of work group diversity, conflict, and performance. Administrative Science Quarterly, 44(1), 1–28.

    Google Scholar 

  27. Rowe, N. C. (2004). Cheating in online student assessment: Beyond plagiarism. Online Journal of Distance Learning Administration, 7, 1–10.

    Google Scholar 

  28. Spanjers, I. A. E., Könings, K. D., Leppink, J., Verstegen, D. M. L., de Jong, N., Czabanowska, K., et al. (2015). The promised land of blended learning: Quizzes as a moderator. Educational Research Review, 15, 59–74.

    Google Scholar 

  29. Stone, B. B. (2012). Flip your classroom to increase active learning and student engagement. In Proceedings from the 28th annual conference on distance teaching & learning (pp. 1–5). Madison, Wisconsin, USA.

  30. Walker, C. O., Greene, B. A., & Mansell, R. A. (2006). Identification with academics, intrinsic/extrinsic motivation, and self-efficacy as predictors of cognitive engagement. Learning and Individual Differences, 16(1), 1–12.

    Google Scholar 

  31. Wang, D. Y., Lin, S. S. J., & Sun, C. T. (2007). DIANA: A computer-supported heterogeneous grouping system for teachers to conduct successful small learning groups. Computers in Human Behavior, 23(4), 1997–2010.

    Google Scholar 

  32. Wiedmann, M., Leach, R. C., Rummel, N., & Wiley, J. (2012). Does group composition affect learning by invention? Instructional Science, 40(4), 711–730.

    Google Scholar 

  33. Yeh, S. S. (1998). Validation of a scheme for assessing argumentative writing of middle school students. Assessing Writing, 5(1), 123–150.

    Google Scholar 

  34. You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. Internet and Higher Education, 29, 23–30.

    Google Scholar 

  35. Zhan, Z., Fong, P. S. W., Mei, H., & Liang, T. (2015). Effects of gender grouping on students’ group performance, individual achievements and attitudes in computer-supported collaborative learning. Computers in Human Behavior, 48, 587–596.

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Grant Nos. NRF-2016R1A2B1014734 and NRF-2017R1E1A1A03070560).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Wonjong Rhee.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Han, J., Huh, S.Y., Cho, Y.H. et al. Utilizing online learning data to design face-to-face activities in a flipped classroom: a case study of heterogeneous group formation. Education Tech Research Dev 68, 2055–2071 (2020). https://doi.org/10.1007/s11423-020-09743-y

Download citation

Keywords

  • Online learning data
  • Activity design
  • Group heterogeneity
  • Learning analytics
  • Flipped classroom