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, log in via an institution to check access.
Similar content being viewed by others
References
Bayne, S. (2015). What’s the matter with ‘technology-enhanced learning’? Learning, Media and Technology, 40(1), 5–20.
Bergmann, J., & Sams, A. (2012). Flip your classroom: Reach every student in every class every day. Washington, DC: Internal Society for Technology in Education.
Bishop, J. L. (2013). A controlled study of the flipped classroom with numerical methods for engineers. Unpublished doctoral dissertation, Utah State University, Logan, Utah.
Bryant, S. M., & Albring, S. M. (2006). Effective team building: Guidance for accounting educators. Issues in Accounting Education, 21(3), 241–265.
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 grou** learning. Expert Systems with Applications, 37(6), 4103–4118.
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.
Cohen, E. G. (1994). Restructuring the classroom: Conditions for productive small groups. Review of Educational Research, 64(1), 1–35.
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.
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.
Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology and Society, 15(3), 42–57.
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.
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.
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.
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.
Johnson, D. W., & Johnson, R. T. (1999). Making cooperative learning work. Theory into Practice, 38(2), 67–73.
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.
Kinshuk, (2016). Designing adaptive and personalized learning environments. New York: Routledge.
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.
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.
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.
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.
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.
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.
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.
O’Flaherty, J., & Phillips, C. (2015). The use of flipped classrooms in higher education: A sco** review. Internet and Higher Education, 25, 85–95.
Pelled, L. H., Eisenhardt, K. M., & **n, K. R. (1999). Exploring the black box: An analysis of work group diversity, conflict, and performance. Administrative Science Quarterly, 44(1), 1–28.
Rowe, N. C. (2004). Cheating in online student assessment: Beyond plagiarism. Online Journal of Distance Learning Administration, 7, 1–10.
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.
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.
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.
Wang, D. Y., Lin, S. S. J., & Sun, C. T. (2007). DIANA: A computer-supported heterogeneous grou** system for teachers to conduct successful small learning groups. Computers in Human Behavior, 23(4), 1997–2010.
Wiedmann, M., Leach, R. C., Rummel, N., & Wiley, J. (2012). Does group composition affect learning by invention? Instructional Science, 40(4), 711–730.
Yeh, S. S. (1998). Validation of a scheme for assessing argumentative writing of middle school students. Assessing Writing, 5(1), 123–150.
You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. Internet and Higher Education, 29, 23–30.
Zhan, Z., Fong, P. S. W., Mei, H., & Liang, T. (2015). Effects of gender grou** on students’ group performance, individual achievements and attitudes in computer-supported collaborative learning. Computers in Human Behavior, 48, 587–596.
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
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11423-020-09743-y