Learning analytics to support teachers during synchronous CSCL: balancing between overview and overload

Abstract

Learning analytics (LA) are summaries, visualizations, and analyses of student data that could improve learning in multiple ways, for example by supporting teachers. However, not much empirical evidence is available yet concerning the effects of LA on how teachers diagnose student progress and intervene during students’ learning activities. The goal of this paper is to summarize the empirical work that was undertaken recently concerning the effect of various types of LA tools on teacher regulation of collaboration groups of students, and to describe the theoretical mechanisms by which LA tools may support teachers in synchronous, moment-to-moment regulation of computer-supported collaborative learning. The hypothesized mechanisms are that LA tools can 1) aggregate information to a manageable level and thereby lower information load, 2) steer the focus of the teacher’s attention, and 3) increase the teacher’s confidence concerning the diagnosis of the situation. In the final section, the role of teacher goals and beliefs in the use of LA tools is discussed, which is argued should be kept in mind when implementing LA in classroom situations.