Meta-learning: Can It Be Suitable to Automatise the KDD Process for the Educational Domain?
The use of e-learning platforms is practically generalised in all educational levels. Even more, virtual teaching is currently acquiring a great relevance never seen before. The information that these systems record is a wealthy source of information that once it is suitably analised, allows both, instructors and academic authorities to make more informed decisions. But, these individuals are not expert in data mining techniques, therefore they require tools which automatise the KDD process and, the same time, hide its complexity. In this paper, we show how meta-learning can be a suitable alternative for selecting the algorithm to be used in the KDD process, which will later be wrapped and deployed as a web service, making it easily accessible to the educational community. Our case study focuses on the student performance prediction from the activity performed by the students in courses hosted in Moodle platform.
KeywordsMeta-learning classification predicting student performance
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