Learner Analysis Aggregating Context-aware Learning Logs of High Granularity

Abstract

Learning Analytics using logs is an important issue in order to realize the adaptive learning support system. We have been working on constructing a learner model based on browsing history log of high granularity on electronic learning materials and analyzing learners based on editing history log which recorded the transition of source code during programming learning. By analyzing these learning logs individually, we have already shown that it is possible to estimate learners' individual missteps and weak points partially.
In this study, by combining these learning logs we will try to grasp learning conditions of the learners more precisely than ever before. Then we discuss ways to accurately analyze the status of individual learners.