Capturing Learning over Time for Supporting Pedagogical Decision Making: A Process Modeling Approach

Capturing Learning over Time for Supporting Pedagogical Decision Making: A Process Modeling Approach

Peter Reimann (University of Sydney, Australia), Wilfrid Utz (BOC, Austria), Roland Unterberger (Joanneum Research, Austria) and Wolfgang Halb (Joanneum Research, Austria)
DOI: 10.4018/978-1-4666-4651-3.ch010
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This chapter aims for a methodological contribution to online learning research and to the practical use of temporal information for pedagogical decision making. The authors address two interconnected concerns: how to describe the temporal features of teaching/learning activities and how to capture learning activities across learning applications and time. The main argument is that the analysis of temporal processes based on student data that can be automatically captured (in log files and through other means) will benefit from an explicit modeling of the teaching process, because in this way, some of the problems associated with a purely inductive approach to process and sequence mining can be overcome. In terms of advancing the state of the art, the authors suggest an approach that is grounded in meta-model architectures for process modeling and demonstrate its advantages with respect to tracking and monitoring students’ learning activities across learning applications. After providing some background on long-term learning, the chapter describes the conceptual as well as several of the implementation solutions developed in the EC-funded NEXT-TELL project.
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The Pedagogical Challenge: Long Term (And Distributed) Learning

Meaningful and deep learning takes time; anybody who learned to speak a second language or to play a musical instrument knows this. It is also true for learning in domains such as science and mathematics. For the sake of brevity, we confine ourselves here to a short analysis of long term learning in science, not only due to its relevance in K-12 education, but also because the long-term nature of science learning is usually not acknowledged as often as second language learning, for instance.

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