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.
%0 Book Section
%1 citeulike:13776561
%A Hosseini, Roya
%A Hsiao, I-Han
%A Guerra, Julio
%A Brusilovsky, Peter
%B Design for Teaching and Learning in a Networked World
%D 2015
%E Conole, Gránne
%E Klobucar, Tomaz
%E Rensing, Christoph
%E Konert, Johannes
%E Lavoué, Élise
%I Springer International Publishing
%K adaptive-navigation-support open-student-model sequencing
%P 155--168
%R 10.1007/978-3-319-24258-3_12
%T What Should I Do Next? Adaptive Sequencing in the Context of Open Social Student Modeling
%U http://dx.doi.org/10.1007/978-3-319-24258-3_12
%V 9307
%X 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.
@incollection{citeulike:13776561,
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.}},
added-at = {2018-03-19T12:24:51.000+0100},
author = {Hosseini, Roya and Hsiao, I-Han and Guerra, Julio and Brusilovsky, Peter},
biburl = {https://www.bibsonomy.org/bibtex/24c47b15a2702dd20507272702d6696d8/aho},
booktitle = {Design for Teaching and Learning in a Networked World},
citeulike-article-id = {13776561},
citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-319-24258-3_12},
citeulike-linkout-1 = {http://link.springer.com/chapter/10.1007/978-3-319-24258-3_12},
doi = {10.1007/978-3-319-24258-3_12},
editor = {Conole, Gr\'{a}nne and Klobu\v{c}ar, Toma\v{z} and Rensing, Christoph and Konert, Johannes and Lavou\'{e}, \'{E}lise},
interhash = {229b90723c32d71d634ed0d69e03609a},
intrahash = {4c47b15a2702dd20507272702d6696d8},
keywords = {adaptive-navigation-support open-student-model sequencing},
pages = {155--168},
posted-at = {2015-09-28 04:12:16},
priority = {2},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{What Should I Do Next? Adaptive Sequencing in the Context of Open Social Student Modeling}},
url = {http://dx.doi.org/10.1007/978-3-319-24258-3_12},
volume = 9307,
year = 2015
}