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

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:	Creators Email Pitt Username ORCID Hosseini, R roh38@pitt.edu ROH38 Hsiao, IH Guerra, J jdg60@pitt.edu JDG60 Brusilovsky, P peterb@pitt.edu PETERB 0000-0002-1902-1464
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:	30 Mar 2021 15:55
URI:	http://d-scholarship.pitt.edu/id/eprint/26266

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