Advertisement

Investigating Students’ Use of Lecture Videos in Online Courses: A Case Study for Understanding Learning Behaviors via Data Mining

  • Ying-Ying KuoEmail author
  • Juan Luo
  • Jennifer Brielmaier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9412)

Abstract

This study investigated students’ learning behaviors in a fully online psychology course which offered 76 streaming lecture videos and supplementary resources, as well as individual and group activities. This paper focuses on students’ use of lecture videos. Data collection included students’ real usage of data on Blackboard Learn 9.1, a course survey, and students’ final grades. The analysis applied data mining techniques, including sequential patterns, decision trees, and clustering analysis, as well as inferential statistics using ANOVA and correlations. Based on students’ use of lecture videos, their learning behaviors were defined into three groups—adaptive viewer, self-regulating viewer, and infrequent viewer. Statistically significant differences within groups were found in their learning satisfaction, final grades, etc. This case study has shown that students’ learning behaviors were varied in the online environment and that their use of course videos affected their learning outcomes.

Keywords

Educational data mining Instructional design LMS Streaming lecture videos Pattern recognition K-means clustering Self-regulation 

References

  1. 1.
    Giannakos, M.N.: Exploring the video-based learning research: a review of the literature. Br. J. Educ. Technol. 44(6), E191–E195 (2013)CrossRefGoogle Scholar
  2. 2.
    Choi, H.J., Johnson, S.D.: The effect of context-based video instruction on learning and motivation in online courses. Am. J. Distance Educ. 19(4), 215–227 (2005)CrossRefGoogle Scholar
  3. 3.
    Draus, P.J., Curran, M.J., Trempus, M.S.: The Influence of instructor-generated video content on student satisfaction with and engagement in asynchronous online classes. J. Online Learn. Teach. 10(2), 240–254 (2014)Google Scholar
  4. 4.
    Tantrarungroj, P., Lai, F.-Q.: Effect of embedded streaming video strategy in an online learning environment on the learning of neuroscience. Int. J. Learn. 17(11), 17–27 (2011)Google Scholar
  5. 5.
    Evans, H.K.: An experimental investigation of videotaped lectures in online courses. TechTrends 58(3), 63–70 (2014)CrossRefGoogle Scholar
  6. 6.
    Gonyea, R.M.: Self-reported data in institutional research: review and recommendations. New Dir. Inst. Res. 2005(127), 73–89 (2005)Google Scholar
  7. 7.
    Black, E.W., Dawson, K., Priem, J.: Data for free: using LMS activity logs to measure community in online courses. Internet High. Educ. 11(2), 65–70 (2008)CrossRefGoogle Scholar
  8. 8.
    Abdous, M., He, W.: Using text mining to uncover students’ technology-related problems in live video streaming. Br. J. Educ. Technol. 42(1), 40–49 (2011)CrossRefGoogle Scholar
  9. 9.
    Ullrich, C., Shen, R., Xie, W.: Analyzing student viewing patterns in lecture videos. In: 2013 IEEE 13th International Conference on Advanced Learning Technologies (ICALT), pp. 115–117. IEEE (2013)Google Scholar
  10. 10.
    Luo, J.: Regression Learning in Decision Guidance Systems: Models, Languages, and Algorithms. Ph.D. Dissertation. George Mason University, Fairfax, VA, USA (2012)Google Scholar
  11. 11.
    Shen, J., Su, P.-C., Cheung, S.C.S.: Virtual mirror rendering with stationary RGB-D cameras and stored 3D background. IEEE Trans. Image Process. 22(9), 1–16 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Information Technology ServicesGeorge Mason UniversityFairfaxUSA
  2. 2.Department of PsychologyGeorge Mason UniversityFairfaxUSA

Personalised recommendations