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What should I do next? Adaptive sequencing in the context of open social student modeling

Hosseini, R and Hsiao, IH and Guerra, J and Brusilovsky, P (2015) What should I do next? Adaptive sequencing in the context of open social student modeling. In: UNSPECIFIED.

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Abstract

© Springer International Publishing Switzerland 2015. 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.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Hosseini, Rroh38@pitt.eduROH38
Hsiao, IH
Guerra, Jjdg60@pitt.eduJDG60
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Date: 1 January 2015
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 9307
Page Range: 155 - 168
Event Type: Conference
DOI or Unique Handle: 10.1007/978-3-319-24258-3_12
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9783319242576
ISSN: 0302-9743
Date Deposited: 27 Oct 2015 16:09
Last Modified: 01 Nov 2017 12:57
URI: http://d-scholarship.pitt.edu/id/eprint/26266

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