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Hsiao, I-Han (2012) NAVIGATION SUPPORT AND SOCIAL VISUALIZATION FOR PERSONALIZED E-LEARNING. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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A large number of educational resources is now made available on the Web to support both regular classroom learning and online learning. However, the abundance of available content produced at least two problems: how to help students to find the most appropriate resources and how to engage them into using these resources and benefit from them. Personalized and social learning have been suggested as potential ways to address these problems.

This work attempts to combine the ideas of personalized and social learning by
providing navigation support through an open social student modeling visualization. A series of classroom studies exploited the idea of the approach and revealed promising results, which demonstrated the personalized guidance and social visualization combined helped students to find the most relevant resources of parameterized self-assessment questions for Java programming. Thus, this dissertation extend the approach to a larger collection of learning objects for cross content navigation and verify its capability of supporting social visualization for personalized E-Learning.

The study results confirm that working with the non-mandatory system, students enhanced the learning quality in increasing their motivation and engagement. They successfully achieved better learning results. Meanwhile, incorporating a mixed collection of content in the open social student modeling visualizations effectively led the students to work at the right level of questions. Both strong and weak student worked with the appropriate levels of questions for their readiness accordingly and yielded a consistent performance across all three levels of complexities. Additionally, providing a more realistic content collection on the navigation supported open social student modeling visualizations results in a uniform performance in the group. The classroom study revealed a clear pattern of social guidance, where the stronger students left the traces for weaker ones to follow. The subjective evaluation confirms the design of the interface in terms of the content organization. Students’ positive responses also compliment the objective system usage data.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Hsiao, I-Hanihh4@pitt.eduIHH4
ContributionContributors NameEmailPitt UsernameORCID
Committee ChairBrusilovsky, Peterpeterb@pitt.eduPETERBUNSPECIFIED
Committee MemberAshley, Kevinashley@pitt.eduASHLEYUNSPECIFIED
Committee MemberHirtle, Stephen C.hirtle@pitt.eduHIRTLEUNSPECIFIED
Committee MemberSpring, Michaelspring@pitt.eduSPRINGUNSPECIFIED
Committee MemberZadorozhny, Vladimirvladimir@sis.pitt.eduVIZUNSPECIFIED
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairBrusilovsky, Peterpeterb@pitt.eduPETERB
Committee MemberAshley, Kevinashley@pitt.eduASHLEY
Committee MemberHirtle, Stephen C.hirtle@pitt.eduHIRTLE
Committee MemberSpring, M.B.spring@pitt.eduSPRING
Committee MemberZadorozhny, Vladimirvladimir@sis.pitt.eduVIZ
Date: 21 August 2012
Date Type: Publication
Defense Date: 9 July 2012
Approval Date: 21 August 2012
Submission Date: 7 August 2012
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 172
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: No
Uncontrolled Keywords: navigation support, social visualization, e-learning, open student modeling
Date Deposited: 21 Aug 2012 21:20
Last Modified: 15 Nov 2016 14:01


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