Off the Beaten Path: The Impact of Adaptive Content Sequencing on Student Navigation in an Open Social Student Modeling Interface

One of the original goals of intelligent educational systems is to guide every student to the most appropriate educational content. Exploring both knowledge-based and social guidance approaches in past work, we learned that each of these approaches has weak sides. In this paper we follow the idea of combining social guidance with more traditional knowledge-based guidance to support more optimal content navigation. We proposed a greedy sequencing approach that maximizes student’s level of knowledge and tested it in a classroom. Results indicated that this approach positively impacts students’ navigation.

Authors and Affiliations

1. Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA

   Roya Hosseini

2. School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA

   I-Han Hsiao

3. School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, USA

   Julio Guerra & Peter Brusilovsky

Corresponding author

Correspondence to Roya Hosseini .

Editors and Affiliations

1. University of British Columbia, Vancouver, British Columbia, Canada

   Cristina Conati

2. Computer Science Department, Worcester Polytechnic Institute, Worcester, Massachusetts, USA

   Neil Heffernan

3. Department of Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand

   Antonija Mitrovic

4. E.T.S.I. Informática, Universidad National de Educacion a Distancia, Madrid, Spain

   M. Felisa Verdejo

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