Abstract
Nowadays, a great number of learning management systems (LMSs) both commercial and open source are widely used for educational and training purposes. Thousands of lessons are delivered through the web by utilizing some LMS and this phenomenon is expected to be escalated the following years by means of the continuous increase in demand for life long learning. In such a learning model learners, educators and educational organizations need smarter tools to face the problem of continuous feedback of the educational process in order to offer a learning environment capable to increase the learning effectiveness of the new mode of learning as well as the efficient organization of the institutional resources. Recently, the academic community uses techniques from data mining giving rise a new hot area in the e-learning field called “Educational Data Mining”. Although LMSs can hold large volumes of rich data, they provide a limited set of reporting features and they don’t support any data mining techniques. Moreover, the current data mining tools both commercial and open source are complex systems, they have not been designed for educational purposes and therefore they are difficult to be used by non-experts in this specific area. In this paper, we firstly examine the requirements for data mining facilities in LMSs. Then we describe a new approach supported by a tool for analyzing learners’ behaviour in LMSs. Finally, we describe initial results arising from the use of the tool in two undergraduate courses.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
ADL (Advanced Distributed Learning) (2006) Sharable Content Object Reference Model. SCORM®, 2004 3rd edn. Available at: [http://www.adlnet.gov/scorm]
Goodyear P (2000) Environments for lifelong learning: ergonomics, architecture and educational design. In: Spector JM, Anderson T (eds) Integrated and holistic perspectives on learning, instruction and technology: understanding complexity. Kluwer, Dordrecht, pp 1–18
IEEE LTSC (Learning Technology Standards Committee) (2003) Draft Standard for Learning Technology Systems Architecture (LTSA). IEEE standard no.: 1484.1–2003
IMS Question and Test Interoperability (IMS QTI) (2006) Draft specification for IMS question and test interoperability. IMS global learning consortium
Klosgen W, Zytkow J (2002) Handbook of data mining and knowledge discovery. Oxford University Press, New York
Koutri M, Avouris N (2004) Daskalaki S (2004) A survey on web usage mining techniques for web-based adaptive hypermedia systems. In: Chen SY, Magoulas GD (eds) Adaptable and adaptive hypermedia systems. Idea Publishing Inc., Hershey
Mazza R, Milani C (2005) Exploring usage analysis in learning systems: gaining insights from visualisations. In: Workshop on usage analysis in learning systems at 12th international conference on artificial intelligence in education, Amsterdam
Psaromiligkos Y, Kytagias C, Athanasopoulou L, Kytagias D (2006) Implementing e-learning models for education and training: a methodology and a case study at the technological education institute of Piraeus. In: Nagopoulos et al (ed) Qualifying labor, social issues and new technologies
Psaromiligkos Y, Retalis S (2003) Re-evaluating the effectiveness of a web-based learning system: a comparative case study. Int J Educ Multimedia Hypermedia 12(1):5–20
Romero C, Ventura S (2007) Educational data mining: a survey from 1995 to 2005. Expert Syst Appl 33(1):135–146
Srivastava J, Cooley R, Deshpande M, Tan P (2000) Web usage mining: discover and applications of usage patterns from web data. SIGKDD Explot 1(2):12–23
Talavera L, Gaudioso E (2004) Mining student data to characterize similar behavior groups in unstructured collaboration spaces. Workshop on artificial intelligence in CSCL. In: 6th European conference on artificial intelligence, pp 17–23
ZaIane O R, Luo J (2001) Towards evaluating learners’ behaviour in a Web-based distance learning environment. In: 2nd IEEE international conference on advanced learning technologies (ICALT’01)
Zinn C, Scheuer O (2006) Getting to know your student in distance-learning contexts. In: Proceedings of EC-TEL, vol 4227 of LNCS, Springer, Heidelberg, pp 437–451
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Psaromiligkos, Y., Orfanidou, M., Kytagias, C. et al. Mining log data for the analysis of learners’ behaviour in web-based learning management systems. Oper Res Int J 11, 187–200 (2011). https://doi.org/10.1007/s12351-008-0032-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12351-008-0032-4