What Should I Do Next? Adaptive Sequencing in the Context of Open Social Student Modeling
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.
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Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA [email protected] 2 School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA [email protected] School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, USA {jdg60,peterb}@pitt.edu
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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Introduction
One of the original goals of intelligent educational systems was to guide each student to the most appropriate educational content. Starting with the first reported ITS system SCHOLAR [7], a range of knowledge-based guidance technologies have been reported. Different technologies in this group include instructional planning [1], course sequencing [3], course generation [14], and adaptive navigation support [2]. All these knowledge-based approaches were based on the same principles: by using a combination of domain models, course goals, and overlay student models, the sequencing engine decides which content is the most appropriate for an individual student at any given moment and delivers it to the student through the interface, which either directly brings the student to the c Springer International Publishing Switzerland 2015 G. Conole et al. (Eds.): EC-TEL 2015, LNCS 9307, pp. 155–168, 2015. DOI: 10.1007/978-3-319-24258-3 12
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right content (as in sequencing), or delivers the content through suggested links (as in course generation and navigation support). Despite the known power of this technology, there are few practical applications, due to the large amount of effort required to build the domain models and analyze the content. In our recent research, we discovered and evaluated a new approach to guide students to the “right” content, based on the ideas behind open social student modeling (OSSM) [10]. OSSM is a recent expansion of open student modeling (OSM), a popular approach that makes traditionally hidden student models available for students to e
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