Abstract
As mentioned elsewhere in this book, e-learning offers “a new context for education where large amounts of information describing the continuum of the teaching–learning interactions are endlessly generated and ubiquitously available”. But raw information by itself may be of no help to any of the e-learning actors. The use of Data Mining methods to extract knowledge from this information can, therefore, be an adequate approach to follow in order to use the obtained knowledge to fit the educational proposal to the students’ needs and requirements. This chapter provides a case study in which several advanced Data Mining techniques are employed to extract different types of knowledge from virtual campus data concerning students system usage behaviour. The diverse palette of Data Mining problems addressed here include data clustering and visualization, outlier detection, classification, feature selection, and rule extraction. They concern diverse e-learning problems, such as the characterization of atypical students’ behaviour and the prediction of students’ performance.
Keywords
- Feature Selection
- Root Mean Square
- Outlier Detection
- Experience Report
- Rule Extraction
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Vellido, A., Castro, F., Etchells, T.A., Nebot, À., Mugica, F. (2007). Data Mining of Virtual Campus Data. In: Jain, L.C., Tedman, R.A., Tedman, D.K. (eds) Evolution of Teaching and Learning Paradigms in Intelligent Environment. Studies in Computational Intelligence, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71974-8_9
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DOI: https://doi.org/10.1007/978-3-540-71974-8_9
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