A Dynamic Topic Model of Learning Analytics Research
Michael Derntl, Nikou Günnemann, Ralf Klamma · 2013
Research on learning analytics and educational data mining has been published since the first conference on Educational Data Mining (EDM) in 2008 and gained momentum through the establishment of the Learning Analytics and Knowledge (LAK) conference in 2011. This paper addresses the LAK Data Challenge from the perspective of visual analytics of topic dynamics in the LAK Dataset between 2008 and 2012. The data set was processed using probabilistic, dynamic topic mining algorithms. To enable exploration and visual analysis of the resulting topic model by LAK researchers and stakeholders we developed and deployed D-VITA, a web-based browsing tool for dynamic topic models. In this paper we explore answers to the questions about past, present, and future of LAK posed in the Data Challenge based on a topic model of all papers in the LAK Dataset. We also briefly describe how users can explore the LAK topic model on their own using D-VITA. 1. OBJECTIVES The LAK Data Challenge called for contributions to make sense of the field of learning analytics including its “roots, current state, and future trends, based on how its members report and debate their research ” 1. This paper tackles the challenge by presenting facts obtained from statistical analyses of the paper full texts included in the provided LAK Dataset [7]. The main contributions are as follows: 1. A dynamic topic model was computed using the approach presented in [3]. Using this dynamic topic model we explore in Section 4 three questions about the evolution of topics in the LAK Dataset to distill knowledge about past, present and future of LAK research. 2. In Section 5 we describe the visual analytics application D-VITA 2, which puts the toolkit to answer the