Profiling Student Behavior in a Blended Course - Closing the Gap between Blended Teaching and Blended Learning

Nynke Bos, Saskia Brand-Gruwel

2016

Abstract

Blended learning is often associated with student-oriented learning in which students have varying degrees of control over their learning process. However, the current notion of blended learning is often a teacher-oriented approach in which the teacher identifies the used learning technologies and thereby offers students a blended teaching course instead of a blended learning course (George-Walker & Keeffe, 2010). A more student-oriented approach is needed within educational design of blended learning courses since previous research shows that students show a large variation in the way they use the different digital learning resources to support their learning. There is little insight into why students show distinct patterns in their use of these learning resources and what the consequences of these (un)conscious differences are in relation to student performance. The current study explores different usage patterns of learning resources by students in a blended course. It tries to establish causes for these differences by using dispositional data and determines the effect of different usage patterns on student performance.

References

  1. Bliuc, A., Goodyear, P. and Ellis, R. (2007). Research focus and methodological choices in studies into students' experiences of blended learning in higher education. The Internet and Higher Education, 10(4), pp.231-244.
  2. Bos, N., Groeneveld, C., van Bruggen, J. and BrandGruwel, S. (2015). The use of recorded lectures in education and the impact on lecture attendance and exam performance. Br J Educ Technol, p.n/a-n/a.
  3. Chiu, T., Fang, D., Chen, J., Wang, Y. and Jeris, C. (2001). A robust and scalable clustering algorithm for mixed type attributes in large database environment. Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 7801.
  4. Ellis, R., Goodyear, P., Calvo, R. and Prosser, M. (2008). Engineering students' conceptions of and approaches to learning through discussions in face-to-face and online contexts. Learning and Instruction, 18(3), pp.267-282.
  5. George-Walker, L. and Keeffe, M. (2010). Selfdetermined blended learning: a case study of blended learning design. Higher Education Research & Development, 29(1), pp. 1-13.
  6. Henderson, M., Selwyn, N. and Aston, R. (2015). What works and why? Student perceptions of 'useful' digital technology in university teaching and learning. Studies in Higher Education, pp.1-13.
  7. Inglis, M., Palipana, A., Trenholm, S. and Ward, J. (2011). Individual differences in students' use of optional learning resources. Journal of Computer Assisted Learning, 27(6), pp.490-502.
  8. Kovanovic, V., GaĊĦevic, D., Joksimovic, S., Hatala, M. and Adesope, O. (2015). Analytics of communities of inquiry: Effects of learning technology use on cognitive presence in asynchronous online discussions. The Internet and Higher Education, 27, pp.74-89.
  9. Long, P. and Siemens, G. (2011). Penetrating the Fog: Analytics in Learning and Education. Educause Review, (46).
  10. Lust, G., Elen, J. and Clarebout, G. (2013). Students' tooluse within a web enhanced course: Explanatory mechanisms of students' tool-use pattern. Computers in Human Behavior, 29(5), pp.2013-2021.
  11. Lust, G., Vandewaetere, M., Ceulemans, E., Elen, J. and Clarebout, G. (2011). Tool-use in a blended undergraduate course: In Search of user profiles. Computers & Education, 57(3), pp.2135-2144.
  12. Oliver, M. and Trigwell, K. (2005). Can 'Blended Learning' Be Redeemed?. elea, 2(1), p.17.
  13. Orton-Johnson, K. (2009). 'I've stuck to the path I'm afraid': exploring student non-use of blended learning. British Journal of Educational Technology, 40(5), pp.837-847.
  14. Pintrich, P., Smith, D., Garcia, T. and Mckeachie, W. (1993). Reliability and Predictive Validity of the Motivated Strategies for Learning Questionnaire (Mslq). Educational and Psychological Measurement, 53(3), pp.801-813.
  15. Porter, W., Graham, C., Bodily, R. and Sandberg, D. (2016). A qualitative analysis of institutional drivers and barriers to blended learning adoption in higher education. The Internet and Higher Education, 28, pp.17-27.
  16. Porter, W., Graham, C., Bodily, R. and Sandberg, D. (2016). A qualitative analysis of institutional drivers and barriers to blended learning adoption in higher education. The Internet and Higher Education, 28, pp.17-27.
  17. Shum, S. and Crick, R. (2012). Learning dispositions and transferable competencies. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK 7812.
  18. Tempelaar, D., Rienties, B. and Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, pp.157-167.
  19. Verkroost, M. J., Meijerink, L., Lintsen, H., and Veen, W. (2008). Finding a balance in dimensions of blended learning. International Journal on ELearning, 7, 499- 522.
  20. Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R. and Duval, E. (2011). Dataset-driven research for improving recommender systems for learning. Proceedings of the 1st International Conference on Learning Analytics and Knowledge - LAK 7811.
  21. Vermunt, J. D. H. M. (1992) Leerstijlen en sturen van leerprocessen in het hoger onderwijs: naar procesgerichte instructie in zelfstanding denken [Learning styles and regulation of learning in higher education: towards process-oriented instruction in autonomous thinking] (Amsterdam, Lisse: Swets & Zeitlinger).
  22. Wiese, C. and Newton, G. (2013). Use of Lecture Capture in Undergraduate Biological Science Education. cjsotl, pp.1-24.
  23. Winne, P. (2006). How Software Technologies Can Improve Research on Learning and Bolster School Reform. Educational Psychologist, 41(1), pp.5-17.
  24. Winne, P. and Jamieson-Noel, D. (2002). Exploring students' calibration of self reports about study tactics and achievement. Contemporary Educational Psychology, 27(4), pp.551-572.
  25. Zacharis, N. (2015). A multivariate approach to predicting student outcomes in web-enabled blended learning courses. The Internet and Higher Education, 27, pp.44-53.
Download


Paper Citation


in Harvard Style

Bos N. and Brand-Gruwel S. (2016). Profiling Student Behavior in a Blended Course - Closing the Gap between Blended Teaching and Blended Learning . In Proceedings of the 8th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-179-3, pages 65-72. DOI: 10.5220/0005724300650072


in Bibtex Style

@conference{csedu16,
author={Nynke Bos and Saskia Brand-Gruwel},
title={Profiling Student Behavior in a Blended Course - Closing the Gap between Blended Teaching and Blended Learning},
booktitle={Proceedings of the 8th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2016},
pages={65-72},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005724300650072},
isbn={978-989-758-179-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - Profiling Student Behavior in a Blended Course - Closing the Gap between Blended Teaching and Blended Learning
SN - 978-989-758-179-3
AU - Bos N.
AU - Brand-Gruwel S.
PY - 2016
SP - 65
EP - 72
DO - 10.5220/0005724300650072