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Ontology-Based Open-Corpus Personalization for E-Learning

Sosnovsky, Sergey (2011) Ontology-Based Open-Corpus Personalization for E-Learning. Doctoral Dissertation, University of Pittsburgh.

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    Abstract

    Conventional closed-corpus adaptive information systems control limited sets of documents in predefined domains and cannot provide access to the external content. Such restrictions contradict the requirements of today, when most of the information systems are implemented in the open document space of the World Wide Web and are expected to operate on the open-corpus content. In order to provide personalized access to open-corpus documents, an adaptive system should be able to maintain modeling of new documents in terms of domain knowledge automatically and dynamically. This dissertation explores the problem of open-corpus personalization and semantic modeling of open-corpus content in the context of e-Learning. Information on the World Wide Web is not without structure. Many collections of online instructional material (tutorials, electronic books, digital libraries, etc.) have been provided with implicit knowledge models encoded in form of tables of content, indexes, headers of chapters, links between pages, and different styles of text fragments. The main dissertation approach tries to leverage this layer of hidden semantics by extracting and representing it as coarse-grained models of content collections. A central domain ontology is used to maintain overlay modeling of students’ knowledge and serves as a reference point for multiple collections of external instructional material. In order to establish the link between the ontology and the open-corpus content models a special ontology mapping algorithm has been developed. The proposed approach has been applied in the Ontology-based Open-corpus Personalization Service that recommends and adaptively annotates online reading material. The domain of Java programming has been chosen for the proof-of-concept implementation. A controlled experiment has been organized to evaluate the developed adaptive system and the proposed approach overall. The results of the evaluation have demonstrated several significant learning effects of the implemented open-corpus personalization. The analysis of log-based data has also shown that the open-corpus version of the system is capable of providing personalization of similar quality to the close-corpus one. Such results indicate that the proposed approach successfully supports open-corpus personalization for e-Learning. Further research is required to verify if the approach remains effective in other subject domains and with other types of instructional content.


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    Item Type: University of Pittsburgh ETD
    Creators/Authors:
    CreatorsEmailORCID
    Sosnovsky, Sergeysosnovsky@gmail.com
    ETD Committee:
    ETD Committee TypeCommittee MemberEmailORCID
    Committee ChairBrusilovsky, Peterpeterb@pitt.edu
    Committee MemberDicheva, Darinadichevad@wssu.edu
    Committee MemberHe, Daqingdah44@pitt.edu
    Committee MemberLane, H. Chadlane@ict.usc.edu
    Committee MemberSpring, Michaelspring@pitt.edu
    Title: Ontology-Based Open-Corpus Personalization for E-Learning
    Status: Published
    Abstract: Conventional closed-corpus adaptive information systems control limited sets of documents in predefined domains and cannot provide access to the external content. Such restrictions contradict the requirements of today, when most of the information systems are implemented in the open document space of the World Wide Web and are expected to operate on the open-corpus content. In order to provide personalized access to open-corpus documents, an adaptive system should be able to maintain modeling of new documents in terms of domain knowledge automatically and dynamically. This dissertation explores the problem of open-corpus personalization and semantic modeling of open-corpus content in the context of e-Learning. Information on the World Wide Web is not without structure. Many collections of online instructional material (tutorials, electronic books, digital libraries, etc.) have been provided with implicit knowledge models encoded in form of tables of content, indexes, headers of chapters, links between pages, and different styles of text fragments. The main dissertation approach tries to leverage this layer of hidden semantics by extracting and representing it as coarse-grained models of content collections. A central domain ontology is used to maintain overlay modeling of students’ knowledge and serves as a reference point for multiple collections of external instructional material. In order to establish the link between the ontology and the open-corpus content models a special ontology mapping algorithm has been developed. The proposed approach has been applied in the Ontology-based Open-corpus Personalization Service that recommends and adaptively annotates online reading material. The domain of Java programming has been chosen for the proof-of-concept implementation. A controlled experiment has been organized to evaluate the developed adaptive system and the proposed approach overall. The results of the evaluation have demonstrated several significant learning effects of the implemented open-corpus personalization. The analysis of log-based data has also shown that the open-corpus version of the system is capable of providing personalization of similar quality to the close-corpus one. Such results indicate that the proposed approach successfully supports open-corpus personalization for e-Learning. Further research is required to verify if the approach remains effective in other subject domains and with other types of instructional content.
    Date: 22 December 2011
    Date Type: Publication
    Defense Date: 30 November 2011
    Approval Date: 22 December 2011
    Submission Date: 21 December 2011
    Release Date: 22 December 2011
    Access Restriction: No restriction; The work is available for access worldwide immediately.
    Patent pending: No
    Number of Pages: 304
    Institution: University of Pittsburgh
    Thesis Type: Doctoral Dissertation
    Refereed: Yes
    Related URLs:
    Degree: PhD - Doctor of Philosophy
    Uncontrolled Keywords: Open-corpus personalization, Information Extraction, e-Learning, Ontology Mapping, Ontologies, Student Modeling, Adaptive Systems, Adaptation, Adaptive Hypermedia
    Schools and Programs: School of Information Sciences > Information Science
    Date Deposited: 22 Dec 2011 08:35
    Last Modified: 16 Jul 2014 17:04

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