Student models in computer-aided instruction

Self, John A.

doi:10.1016/s0020-7373(74)80005-2

Cited by 122 publications

(39 citation statements)

References 4 publications

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“…Moreover, this category of systems was both the first (Carbonell 1970) and the most successful in modeling user knowledge. As a result, models of user knowledge are frequently called student models, following the name that was originally used for this kind of models in the field of AESs (Self 1974).…”

Section: Overview Of Student Modeling In Adaptive Educational Systemsmentioning

confidence: 99%

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: Overview Of Student Modeling In Adaptive Educational Systemsmentioning

confidence: 99%

Evaluation of topic-based adaptation and student modeling in QuizGuide

Sosnovsky

Brusilovsky

2015

User Model User-Adap Inter

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“…During the 1970s and 80s, AI researchers made great strides in the domain of intelligent technology-based instruction, and created systems under the title of intelligent tutoring systems (ITSs) with an almost fixed architecture that were intelligent with regard to their subject matter (What they teach), learners (Who they teach), and instructional methodologies (How they teach it) (Self, 1974). Many different ITSs were designed and developed in AI labs during that period, showing outstanding degrees of intelligence; however, very few of them could find a place out of AI labs.…”

Section: Intelligent Technology-based Instructionmentioning

confidence: 99%

Intelligent and Adaptive Tutoring Systems: How to Integrate Learners

Bagheri

2015

IJE

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This article is a non-expert overview of intelligent technology-based instructional machines, from the first stages of their emergence up to this date. It is attempted to demonstrate their trend of evolution, and how the advances in other related fields such as education, psychology, and computer science affected them and contributed to their progress. It starts by an account of the concept of intelligent technology-based instruction and continues by defining the notion of learner integration in such systems. Furthermore, Intelligent Tutoring Systems (ITSs) are pictured: how they evolved, their architecture, and how they contributed to the field of Adaptive Learning Systems (ALSs). Finally, a number of adaptive learning platforms which are the state of the art and currently used by large numbers of actual users are introduced and described.

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“…These studies deal with four elements of knowledge communication-the object of communication, the recipient of communication, the skill of communication, and the form of communication. These elements are usually governed by four main ITS components-domain module, student model, tutoring module, and interface, respectively (Self, 1974;Sleeman & Brown, 1982).…”

Section: Realization Of a Social Learning Model Using Particular Techmentioning

confidence: 99%

Redefining the learning companion: the past, present, and future of educational agents

Chou

Chan

Lin

2003

Computers & Education

189

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The development of intelligent tutoring systems has long been the focus of applying artificial intelligence and cognitive science in education. A new breed of intelligent learning environments called learning companion systems was developed over a decade ago. In contrast to an intelligent tutoring system, in which a computer mimics an intelligent tutor, the learning companion system assumes two roles, one as an intelligent tutor and another as a learning companion. Motivated by recent interest in agent research and other technologies, this learning companion field has received increasing attention. This study addresses issues that arise from different perspectives on this research effort. With a view to future networked learning environments, the learning companion is redefined for application to a wide spectrum of educational agent research. Accordingly, several subjects that relate to educational agents, and hence learning companions, are identified. #

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Student models in computer-aided instruction

Cited by 122 publications

References 4 publications

Evaluation of topic-based adaptation and student modeling in QuizGuide

Evaluation of topic-based adaptation and student modeling in QuizGuide

Intelligent and Adaptive Tutoring Systems: How to Integrate Learners

Redefining the learning companion: the past, present, and future of educational agents

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