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What Should I Do Next? Adaptive Sequencing in the Context of Open Social Student Modeling

  • Roya Hosseini
  • I-Han Hsiao
  • Julio Guerra
  • Peter Brusilovsky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9307)

Abstract

One of the original goals of intelligent educational systems was to guide each student to the most appropriate educational content. In previous studies, we explored both knowledge-based and social guidance approaches and learned that each has a weak side. In the present work, we have explored the idea of combining social guidance with more traditional knowledge-based guidance systems in hopes of supporting more optimal content navigation. We propose a greedy sequencing approach aimed at maximizing each student’s level of knowledge and implemented it in the context of an open social student modeling interface. We performed a classroom study to examine the impact of this combined guidance approach. The results of our classroom study show that a greedy guidance approach positively affected students’ navigation, increased the speed of learning for strong students, and improved the overall performance of students, both within the system and through end-of-course assessments.

Keywords

Personalized guidance Open social student modeling Adaptive navigation support E-learning Java programming 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Roya Hosseini
    • 1
  • I-Han Hsiao
    • 2
  • Julio Guerra
    • 3
  • Peter Brusilovsky
    • 3
  1. 1.Intelligent Systems ProgramUniversity of PittsburghPittsburghUSA
  2. 2.School of Computing, Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA
  3. 3.School of Information SciencesUniversity of PittsburghPittsburghUSA

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