Advancing log analysis of student interactions with cognitive tools

Abstract

We have developed a software application (gStudy) that supports learning with multimedia documents. Students use gStudy to create and link notes, highlight and label text and images, construct glossaries and concept maps, exchange information objects through a chat interface, and perform other operations on multimedia content. The detailed log files recorded by gStudy constitute a wealth of data about how students process information as they learn. In this paper we describe log parsing and data mining methods we have borrowed from computer science and apply to researching selfregulated learning. The analysis software we developed identifies coherent learner actions from the complex series of low-level events recorded by gStudy and detects sequential patterns of these actions that may be interspersed with unrelated actions. It applies a data mining algorithm to discover action patterns which are the longest subsequences common to a group of participants. The use of these methods is illustrated through an analysis of gStudy log files generated by 103 university students. 2 Log Analysis of Student Interactions with Cognitive Tools