Student Interaction with Moodle Activities: a Study Based on Web Mining
DOI:
https://doi.org/10.37467/gka-revtechno.v5.453Keywords:
Virtual Platform Moodle, Data MiningAbstract
The purpose of this article is to analyze the learning data set obtained from the Moodle platform andtrack student activity as an essential requirement for this new teaching-learning interactive in implementing the European Higher Educa-tion Area (EHEA) has been a substantial changes in the assessment process. The various web mining subjects used as a methodology to extract information using variables that provide information about how students interact with different activities configured in the virtual platform Moodle and monitoring that make the subject taking into temporary variables account. This is evidenced by the results that systems for managing learning, Learning Management System (LMS) in the form of virtual learning platforms store large amounts of information that can be drawn from the various subjects under interactuaciones with the virtual platform Moodle. We conclude that there is a relationship between interactions with Moodle and academic performance, and the use of students and teachers from the platform.
References
Agudo-Peregrina, A. F., Iglesias-Pradas, S., Conde-González, M. A., Hernández-García, A. (2013). Can we predict success from log data in VLEs? Classification of interactions for leaning analyticis and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behaviour, accepted to be published.
Baker, R., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future revisions. Journal of Educational Data Mining, 1(1), pp. 3-17.
Balgojevic, M., & Micic, Z. (2013). A web-based intelligent report e-learning system using data mining techniques. Computers and Electrical Engineering, 39, pp. 465-474.
Donnelly, R. (2010). Interaction analysis in a ‘Learning by Doing’problem-based professional development context. Computers & Education, 55(3), pp. 1357-1366.
Ferguson, R. (2012).The state of learning analytics in 2012: A review and future challenges. Technical report KMI-12-01. UK: Knowledge Media Institute, The Open University. Google Analytics, http://www.google.com/analytics/Moodle, https://www.moodle.org/
Mostow, J., & Beck, J. (2006). Some useful tactics to modify, map and mine data from intelligent tutors. Natural Language Engineering, 12(02), pp. 195-208.
Philips, R., Maor, D., Preston, G., & Cumming-Potvin, W. (2012). Exploring learning analytics as indicators of study behavior. In: World Conference on Educational Multimedia, Hypermedia and Telecommunications (EDMEDIA), pp. 2861-2867.
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), pp. 135-146.
Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computer & Education, 51(6), pp. 368-384.
Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40(6), pp. 601-618.
Sims, R. (1999). Interactivity on stage: Strategies for learner-designer communication. Australian Journal of Educational Technology, 15(3), pp. 257-272.
Downloads
Published
Issue
Section
License
All articles are published under an Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0) license. Authors retain copyright over their work.