Abstract
The need for innovative didactical methods in combination with the efficient deployment of technical systems is an increasing challenge in the research field of e-learning. Research activities concerned with this observation have led to the understanding that the concept of learner models offers a range of possibilities to develop optimized, adaptive e-learning units (e.g. Graf et al. 2009, O’Connor 1998). Process information can enhance these approaches. Data mining is able to build process models from event logs. It means that information about real process execution can be deduced by extracting information from event logs rather than by assuming a behavior model which has been built by conventional modeling methods. This applies to the e-learning context, because a certain behavior of an underlying process model tracked in a Learning Management System (LMS) may differ from the one assumed by instructors or learning object designers of e-learning units. Instructors who need to attribute certain tasks to a huge group of online learners may not be capable of monitoring all factors influencing the appropriateness of all learner-task associations. Learning paths in LMS to which instructors have not paid attention to yet are of considerable interest. We apply a concept of rule based control of e-learning processes based on the framework we have presented in Holzhüter et al. 2010 to demonstrate these goals.
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Holzhüter, M., Frosch-Wilke, D., Klein, U. (2013). Exploiting Learner Models Using Data Mining for E-Learning: A Rule Based Approach. In: Peña-Ayala, A. (eds) Intelligent and Adaptive Educational-Learning Systems. Smart Innovation, Systems and Technologies, vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30171-1_4
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