Abstract
On a daily basis, a large amount of data is gathered through the participation of students in e-learning environments. This wealth of data is an invaluable asset to researchers as they can utilize it in order to generate conclusions and identify hidden patterns and trends by using big data analytics techniques. The purpose of this study is a threefold analysis of the data that are related to the participation of students in the online forums of their University. In one hand the content of the messages posted in these fora can be efficiently analyzed by text mining techniques. On the other hand, the network of students interacting through a forum can be adequately processed through social network analysis techniques. Still, the combined knowledge attained from both of the aforementioned techniques, can provide educators with practical and valuable information for the evaluation of the learning process, especially in a distance learning environment. The study was conducted by using real data originating from the online forums of the Hellenic Open University (HOU). The analysis of the data has been accomplished by using the R and the Weka tools, in order to analyze the structure and the content of the exchanged messages in these fora as well as to model the interaction of the students in the discussion threads.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abel, F., Bittencourt, I., Costa, E., Henze, N., Krause, D., Vassilev, J.: Recommendations in Online Discussion Forums for E-Learning Systems. IEEE Transactions on Learning Technologies 3(2), 165–176 (2010)
Kalles, D., Pierrakeas, C.: Analyzing Student Performance in Distance Learning with Genetic Algorithms and Decision Trees. Applied Artificial Intelligence 20(8), 655–674 (2006)
Brindley, J.E., Walti, C., Blaschke, L.M.: Creating Effective Collaborative Learning Groups in an Online Environment. IRRODL 10(3) (2009), http://www.irrodl.org/index.php/irrod/article/view/675/1271 (retrieved January 10, 2014)
Kalles, D., Pierrakeas, C., Xenos, M.: Intelligently Raising Academic Performance Alerts. In: 1st International Workshop on Combinations of Intelligent Methods and Applications (CIMA), 18th European Conference on Artificial Intelligence, Patras, Greece, pp. 37–42 (July 2008)
Yusof, E.N., Rahman, A.A.: Students’ interactions in online asynchronous discussion forum: A social network analysis. In: International Conference on Education Technology and Computer, pp. 25–29 (2009)
Siemens, G., Baker, R.S.J.: Learning Analytics and Educational Data Mining: Towards Communication and Collaboration. In: LAK 2012 (2012)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth, Monterey (1984)
Lopez, M.I., et al.: Classification via clustering for predicting final marks based on student participation in forums. In: Educational Data Mining Proceedings (2012)
de Laat, M., Lally, V., Lipponen, L., Simons, R.-J.: Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for social network analysis. International Journal of Computer-Supported Collaborative Learning 2(1), 87–103 (2007)
Reihaneh Rabbany, K., Takaffoli, M., Zaïane, O.R.: Social network analysis and mining to support the assessment of on-line student participation. ACM SIGKDD Explorations Newsletter 13(2), 20–29 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Lotsari, E., Verykios, V.S., Panagiotakopoulos, C., Kalles, D. (2014). A Learning Analytics Methodology for Student Profiling. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-07064-3_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07063-6
Online ISBN: 978-3-319-07064-3
eBook Packages: Computer ScienceComputer Science (R0)