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Guiding and Motivating Students Through Open Social Student Modeling: Lessons Learned

by I-Han Hsiao & Peter Brusilovsky - 2017

Background/Context: A large number of educational resources are now made available on the web to support both regular classroom learning and online learning. The abundance of available content has produced at least two problems: how to help students find the most appropriate resources and how to engage them in using and benefiting from these resources. Personalized and social learning have been suggested as potential ways to address these problems. Our work attempts to integrate these directions of research by combining the ideas of adaptive navigation support and open student modeling with the ideas of social comparison and social visualization. We call our approach Open Social Student Modeling (OSSM).

Objective/Research Questions: First, we review a sequence of our earlier projects focused on Open Social Student Modeling for one kind of learning content and formulate several key design principles that contribute to the success of OSSM. Second, we present our exploration of OSSM in a more challenging context of modeling student progress for two kinds of learning content in parallel. We aim to answer the following research questions: How do we design OSSM interfaces to support many kinds of learning content in parallel? Will current identified design principles (key features) confirm the power of the learning community through OSSM with multiple learning-resource collections? Will the OSSM visualization provide successful personalized guidance within a richer collection of educational resources?

Research Design: We designed four classroom studies to assess the value of different options for OSSM visualization of one and multiple kinds of learning content in the context of programming-language learning. We examined the comparative success of different design options to distill successful design patterns and other important lessons for the future developers of OSSM for personalized and social e-learning.

Findings/Results: The results confirmed the motivational impact of personalized social guidance provided by the OSSM system in the target context. The interface encouraged students to explore more topics and motivated them to work ahead of the course schedule. Both strong and weak students worked with the appropriate levels of questions for their readiness, which yielded consistent performance across different levels of complex problems. Additionally, providing more realistic content collection on the navigation-supported OSSM visualizations resulted in uniform performance for the group.

Conclusions/Recommendation: A sequence of studies of several OSSM interfaces confirmed that a combination of adaptive navigational support, open student modeling, and social visualization in the form of the OSSM interface can reinforce the navigational and motivational values of these approaches. In several contexts, the OSSM interface demonstrated its ability to offer effective guidance in helping students to locate the most relevant content at the right time while increasing student motivation to work with diverse learning content.

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Cite This Article as: Teachers College Record Volume 119 Number 3, 2017, p. 1-42
https://www.tcrecord.org ID Number: 21773, Date Accessed: 9/30/2020 3:24:54 PM

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About the Author
  • I-Han Hsiao
    Arizona State University
    E-mail Author
    I-HAN HSIAO is Assistant Professor of Computer Science at Arizona State University. She is actively involved in the A.I.-supported education for computer science (AIEDCS) and computer-supported peer review in education (CSPRED) communities through organizing and co-chairing workshops. Her expertise and research interests encompass adaptive educational technology, educational technology evaluation, open student modeling, and visual learning analytics. She has also been teaching CS1 and CS2 courses for more than eight years. Dr. Hsiao received her Ph.D. in Information Sciences from the University of Pittsburgh in 2012.
  • Peter Brusilovsky
    School of Information Sciences, University of Pittsburgh
    E-mail Author
    PETER BRUSILOVSKY is Professor of Information Science and Intelligent Systems at the University of Pittsburgh, where he also directs the Personalized Adaptive Web Systems (PAWS) lab. Dr. Brusilovsky has been working in the field of adaptive educational systems, user modeling, and intelligent user interfaces for more than 25 years. He has published numerous papers and edited several books on adaptive hypermedia and the adaptive web. Peter is the editor-in-chief of IEEE Transactions on Learning Technologies and a board member of several journals, including User Modeling and User Adapted Interaction and ACM Transactions on Social Computing. Dr. Brusilovsky received his Ph.D. in Computer Science from Moscow State University in 1987. He also holds a Doctor honoris causa degree from the Slovak University of Technology in Bratislava.
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