Predicting student's learning outcome from Learning management system logs

Abstract

Teaching is complex activity which requires professors to employ the most effective and efficient teaching strategies to enable students to make progress. Main problem in teaching professors should consider different teaching approaches and learning techniques to suit every student. Today, in computer age, electronic learning (e-learning) is widely used in practice. Development of World Wide Web, especially Web2.0 has led to revolution in education. Student interaction with Learning management systems - LMS result in creating large data sets which are interesting for research. LMS systems also provide tools for following every individual student and statistical view for deeper analyzing result of student - system interaction. However, these tools do not include artificial intelligence algorithms as a support mechanism for decision. In this article we provide framework for student modeling trained on large sets of data using Hadoop and Mahout. This kind of system would provide insight into each individual student's activity. Based on that information, professors could adjust course materials according to student interest and knowledge.