What Can Peripheral Data Tell Us About Students' Self-Regulated Learning?

• In Event: What Can Multimodal Data Streams Reveal About Students’ Self-Regulated Learning?

Abstract

Research of self-regulated learning propose that cognitive, motivational and affective states as well as learning performance are relevant variables to adapt instructional support. Data from peripheral input devices (mouse & keyboard) provide a potential unobtrusive and non-reactive source but is rarely addressed in educational research. In an empirical study with 43 undergraduates, we examined whether mining this data can deliver important information about student’s self-regulated learning about website programming. Analysis of peripheral data showed the possibility to predict performance and motivation from indices of student’s typing behavior. No robust relations with affect variables could be shown. Example applications of the findings are analyses of SRL processes and more accurate adaptions of instructional support

Authors

• Markus Hörmann, Technical University of Munich

• Katharina Engelmann, Technical University of Munich

• Maria A. Bannert, Technical University of Munich