Mining Web-based Educational Systems to Predict Student Learning Achievements

Abstract

Educational Data Mining (EDM) is getting great importance as a newinterdisciplinary research field related to some other areas. It isdirectly connected with Web-based Educational Systems (WBES) and DataMining (DM, a fundamental part of Knowledge Discovery in Databases).The former defines the context: WBES store and manage huge amounts ofdata. Such data are increasingly growing and they contain hiddenknowledge that could be very useful to the users (both teachers andstudents). It is desirable to identify such knowledge in the form ofmodels, patterns or any other representation schema that allows a betterexploitation of the system. The latter reveals itself as the tool toachieve such discovering. Data mining must afford very complex anddifferent situations to reach quality solutions. Therefore, data miningis a research field where many advances are being done to accommodateand solve emerging problems. For this purpose, many techniques areusually considered.In this paper we study how data mining can be used to induce studentmodels from the data acquired by a specific Web-based tool for adaptivetesting, called SIETTE. Concretely we have used top down inductiondecision trees algorithms to extract the patterns because these models,decision trees, are easily understandable. In addition, the conductedvalidation processes have assured high quality models.