Adaptation “in the Wild”: Ontology-Based Personalization of Open-Corpus Learning Material

Sosnovsky, Sergey; Hsiao, I-Han; Brusilovsky, Peter

doi:10.1007/978-3-642-33263-0_38

Cited by 18 publications

(12 citation statements)

References 6 publications

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“…While the first approach emerged as a popular practice and the second one provoked a stream of interesting research (Lawless et al 2008;Smith,Blandford 2003;Specht et al 2002;Wang,Taylor 2007;Hatala et al 2009;Brusilovsky et al 2006;Sosnovsky et al 2004;Sosnovsky et al 2012;Apted et al 2004), this paper focuses on our experience with less-investigated third approach.…”

Section: Introductionmentioning

confidence: 98%

Evaluation of topic-based adaptation and student modeling in QuizGuide

Sosnovsky

Brusilovsky

2015

User Model User-Adap Inter

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Abstract:This paper presents an in-depth analysis of a nonconventional topic-based personalization approach for adaptive educational systems (AES) that we have explored for a number of years in the context of university programming courses. With this approach both student modeling and adaptation are based on coarse-grained knowledge units that we called topics. Our motivation for the topic-based personalization was to enhance AES transparency for both teachers and students by utilizing typical topic-based course structures as the foundation for designing all aspects of an AES from the domain model to the end-user interface. We illustrate the details of the topic-based personalization technology, with the help of the Webbased educational service QuizGuide -the first system to implement it. QuizGuide applies the topicbased personalization to guide students to the right learning material in the context of an undergraduate C programming course. While having a number of architectural and practical advantages, the suggested coarse-grained personalization approach deviates from the common practices toward knowledge modeling in AES. Therefore, we believed, that several aspects of QuizGuide required a detailed evaluation -from modeling accuracy to the effectiveness of adaptation. How can one evaluate the soundness of a user modeling (UM) approach, rather than a specific personalized system? The paper discusses how this new student modeling approach can be evaluated, and presents our attempts to evaluate it from multiple different prospects. The evaluation of QuizGuide across several consecutive semesters demonstrates that, although topics do not always support precise UM, they can provide a basis for successful personalization in AESs. Keywords:Adaptive Hypermedia, Student Modeling, Adaptive Educational System, Adaptive Navigation Support, Adaptive Link Annotation, Topic-based Adaptation, Topic-based User Modeling, Adaptive System Evaluation, Layered EvaluationThe first page should include the following declaration: "This paper or a similar version is not currently under review by a journal or conference, nor will it be submitted to such within the next three months. This paper is free of plagiarism or self-plagiarism as defined in Springer's Policy on Publishing Integrity. Abstract:This paper presents an in-depth analysis of a nonconventional topic-based personalization approach for adaptive educational systems (AES) that we have explored for a number of years in the context of university programming courses. With this approach both student modeling and adaptation are based on coarse-grained knowledge units that we called topics. Our motivation for the topic-based personalization was to enhance AES transparency for both teachers and students by utilizing typical topic-based course structures as the foundation for designing all aspects of an AES from the domain model to the end-user interface. We illustrate the details of the topic-based personalization technology, with the help of the Web-based educational service Quiz...

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Section: Introductionmentioning

confidence: 98%

Evaluation of topic-based adaptation and student modeling in QuizGuide

Sosnovsky

Brusilovsky

2015

User Model User-Adap Inter

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“…Wang et al (2015) have extracted concept hierarchies from textbooks based on their TOCs and recognised DBpedia terms. Also, Sosnovsky et al (2012) experimented with harvesting topic-based models from HTML textbooks using structures of their headings and mapping them into ontologies to facilitate more fine-grained personalisation of the textbook content. The work presented in this paper falls into this category-an approach implementing all stages of textbook knowledge extraction.…”

Section: Knowledge Modelsmentioning

confidence: 99%

Knowledge models from PDF textbooks

Alpizar-Chacon

Sosnovsky

2021

New Review of Hypermedia and Multimedia

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Textbooks are educational documents created, structured and formatted by domain experts with the primary purpose to explain the knowledge in the domain to a novice. Authors use their understanding of the domain when structuring and formatting the content of a textbook to facilitate this explanation. As a result, the formatting and structural elements of textbooks carry the elements of domain knowledge implicitly encoded by their authors. Our paper presents an extensible approach towards automated extraction of knowledge models from textbooks and enrichment of their content with additional links (both internal and external). The textbooks themselves essentially become hypertext documents where individual pages are annotated with important concepts in the domain. The evaluation experiments examine several aspects and stages of the approach, including the accuracy of model extraction, the pragmatic quality of extracted models using one of their possible applications-semantic linking of textbooks in the same domain, the accuracy of linking models to external knowledge sources and the effect of integration of multiple textbooks from the same domain. The results indicate high accuracy of model extraction on symbolic, syntactic and structural levels across textbooks and domains, and demonstrate the added value of the extracted models on the semantic level.

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“…Previous research on personalization was focused mainly on separating linking between resources and the space to which they relate and attempting to find In recent years, there has been a tendency to apply ontology-based personalization for open corpus resources [36], generate open corpuses for languages [37], or generate open corpuses by performing large-scale information retrieval, as well as language construct retrieval and linking [38].…”

Section: Open Corpus Personalizationmentioning

confidence: 99%

Web personalization issues in big data and Semantic Web: challenges andopportunities

Raufi¹,

Ismaili²,

Ajdari³

et al. 2019

Turk J Elec Eng & Comp Sci

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Web personalization is a process that utilizes a set of methods, techniques, and actions for adapting the linking structure of an information space or its content or both to user interaction preferences. The aim of personalization is to enhance the user experience by retrieving relevant resources and presenting them in a meaningful fashion. The advent of big data introduced new challenges that locate user modeling and personalization community in a new research setting. In this paper, we introduce the research challenges related to Web personalization analyzed in the context of big data and the Semantic Web. This paper also introduces some models and approaches that can bridge the gap between the two. Future challenges and opportunities related to Web personalization, analyzed from the big data and Semantic Web perspective, are also presented. The research challenges outlined in this paper involve the scrutability of user models in personalization, generic personalization, meta-personalization, open corpus personalization, and semantic data modeling.

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Adaptation “in the Wild”: Ontology-Based Personalization of Open-Corpus Learning Material

Cited by 18 publications

References 6 publications

Evaluation of topic-based adaptation and student modeling in QuizGuide

Evaluation of topic-based adaptation and student modeling in QuizGuide

Knowledge models from PDF textbooks

Web personalization issues in big data and Semantic Web: challenges andopportunities

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