Informing Authoring Best Practices Through an Analysis of Pedagogical Content and Student Behavior

Conference paper

DOI: 10.1007/978-3-319-39583-8_5

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9684)
Cite this paper as:
Roy M., Kumar R. (2016) Informing Authoring Best Practices Through an Analysis of Pedagogical Content and Student Behavior. In: Micarelli A., Stamper J., Panourgia K. (eds) Intelligent Tutoring Systems. ITS 2016. Lecture Notes in Computer Science, vol 9684. Springer, Cham

Abstract

Among other factors, student behavior during learning activities is affected by the pedagogical content they are interacting with. In this paper, we analyze this effect in the context of a problem-solving based online Physics course. We use a representation of the content in terms of its position, composition and visual layout to identify eight content types that correspond to problem solving sub-tasks. Canonical examples as well as a sequence model of these tasks are presented. Student behaviors, measured in terms of activity, help-requests, mistakes and time on task, are compared across each content type. Students request more help while working through complex computational tasks and make more mistakes on tasks that apply conceptual knowledge. We discuss how these findings can inform the design of pedagogical content and authoring tools.

Keywords

Student behavior Content development Authoring Online learning Problem solving 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Raytheon BBN TechnologiesCambridgeUSA

Personalised recommendations