Improving retention: Predicting at-risk students by analysing clicking behaviour in a virtual learning environment

ACM International Conference Proceeding Series

Volumn , Issue , 2013, Pages 145-149

  Wolff, Annika ^a   Zdrahal, Zdenek ^a   Nikolov, Andriy ^b   Pantucek, Michal ^c  

^a OPEN UNIVERSITY   (United Kingdom)

^b Fluid Operations AG   (Germany)

^c CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

Author keywords

distance learning; predictive modelling; retention; student data; virtual learning environment

Indexed keywords

DATA-SOURCES; FACE TO FACE; OPEN UNIVERSITIES; PREDICTIVE MODELLING; PREDICTIVE MODELS; RETENTION; VIRTUAL LEARNING ENVIRONMENTS;

AUTOMATA THEORY; COMPUTER AIDED INSTRUCTION; DISTANCE EDUCATION; FORECASTING; STUDENTS;

E-LEARNING;

EID: 84876493601     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2460296.2460324     Document Type: Conference Paper

References (18)

1. Arnold, K.E. and Pistilli, M.D. (2012) Course Signals at Purdue: Using Learning Analytics to increase student success. LAK'12, 29 April - 2 May, Vancouver, Canada
2. Campbell, J.P. and Oblinger, D.G. (2007) Academic Analytics. White paper. Educause. http://net.educause.edu/ir/library/pdf/pub6101.pdf
3. Dekker, G., Pechenizkiy, M. and Vleeshouwers, J. (2009) Predicting Students Drop Out: a Case Study, In Proceedings of the 2nd International Conference on Educational Data Mining (EDM'09), pp. 41-50
4. Diaz, D. P. (2000). Comparison of student characteristics, and evaluation of student success, in an online health education course. Unpublished doctoral dissertation, Nova Southeastern University, Fort Lauderdale, Florida. Retrieved from http://academic.cuesta.edu/ltseries/sitepgs/pdf/dissertn.pdf
5. Frankola, K. (2002). Why online learners drop out. Crain Communications, Inc. Retrieved from http://www.kfrankola.com/Documents/Why%20online%20le arners%20drop%20out-Workforce.pdf
6. Fusch, D. (2011) Identifying At-Risk Students: What Data Are You Looking At? Retrieved from http://www.academicimpressions.com/news/identifying-risk- students- what-data-are-you-looking
7. Galusha, J. M. (1997). Barriers to learning in distance education. Retrieved from http://www.infrastruction.com/barriers.htm.
8. Hájek, P., Havel, I., and Chytil, M. (1966). The GUHA method of automatic hypotheses determination. Computing, 1, 293-308
9. Hájek P., Holeňa and Rauch J. (2010) The GUHA method and its meaning for data mining. J. Computer and System Sciences, 76, 34-48.
10. Hammang, J.M., Campbell, J.P., Smith, V.C., Sweeley, D., Ushveridze, A. and Woosley, S. (2010) Predictive Analytics: Building a Crystal Ball for Student Success. Webinar. http://www.uregina.ca/orp/PapersPresentations/SCUP-Webi nar-Sept29-2010.pdf
11. Mazza, R. and Dimitrova, V. (2004) Visualising Student Tracking Data to Support Instructors in Web-Based Distance Education, 13th International World Wide Web Conference - Alternate Educational Track, 154-161.
12. Pistilli, M.D. and Arnold, K.E. (2010) Purdue Signals: Mining real-time academic data to enhance student success . About Campus, July-August, 22-24 DOI: 10.1002/abc.20025
13. Quinlan, J. R. (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers.
14. Rivera, J. C. and Rice, M. L. (2002). A comparison of student outcomes & satisfaction between traditional & web based course offerings. Online Journal of Distance Learning Administration. The State University of West Georgia, 5(3).
15. Simpson, O. (2004) The impact on retention of interventions to support distance learning students, Open Learning: The Journal of Open and Distance Learning, 19, 1, 79-95
16. Simpson, O. (2008) Motivating learners in open and distance learning: do we need a new theory of learner support? Open Learning: the journal of open and distance learning, 23, 3, 159-170
17. Smith, V.C. and Sweeley, D. (2010) Predictive Analytics: Increasing Online Student Engagement and Success. Retrieved from http://www.uregina.ca/orp/ PapersPresentations/SCUP-Webi nar-Sept29-2010.pdf
18. Thompson, M.M. (1998). Distance learners in higher education. In C.C. Gibson (Ed.), Distance learners in higher education (pp. 9-24). Madison, WI: Atwood Publishing.