Individualized Bayesian Knowledge Tracing Models

Yudelson, Michael; Koedinger, Kenneth R.; Gordon, Geoffrey J.

doi:10.1007/978-3-642-39112-5_18

Cited by 321 publications

(155 citation statements)

References 7 publications

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“…The prior knowledge levels for QuizGuide student models were computed using the original formula explained in Section 3.3 (i.e., in exactly the same way as it was done in all classroom studies). The prior knowledge for BKT student models was computed with the help of the toolkit described in (Yudelson et al 2013) with by-skill parameter fitting using the gradient descent method.…”

Section: Evaluation Of Topic-based User Modeling: Predictive Validitymentioning

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: Evaluation Of Topic-based User Modeling: Predictive Validitymentioning

confidence: 99%

Evaluation of topic-based adaptation and student modeling in QuizGuide

Sosnovsky

Brusilovsky

2015

User Model User-Adap Inter

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“…We then construct a two-part Bayesian network using Bayesian Knowledge Tracing (BKT) [22] similar to that of the AN-DES tutor [4]. This architecture is summarized in Figure 1.…”

Section: Bayesian Model With Error Contextsmentioning

confidence: 99%

Using Student Logs to Build Bayesian Models of Student Knowledge and Skills

Nguyen

Liew

2018

EasyChair Preprints

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Recent works on Intelligent Tutoring Systems have focused on more complicated knowledge domains, which pose challenges in automated assessment of student performance. In particular, while the system can log every user action and keep track of the student's solution state, it is unable to determine the hidden intermediate steps leading to such state or what the student is trying to achieve. In this paper, we show that this information can be acquired through data mining, along with the type, frequency and context of errors that students made. Our technique has been implemented as part of the student model in a tutor that teaches red-black trees. The system was evaluated on three semesters of student data. Analysis of the results shows that the proposed framework of error analysis can help the system in predicting student performance with good accuracy and the instructor in determining difficulties that students encounter, both individually and collectively as a class.

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“…Em ambas as versões, ele exige a definição de quatro parâmetros: (i) o conhecimento inicial do estudante (P(L 0 )), (ii) a probabilidade dele transitar do estado "Não Sabe" para o estado "Sabe", a taxa de aprendizado (P(T)), (iii) a probabilidade de um erro por falta de atenção (slip) (P(S)), e (iv) a probabilidade de um acerto acidental (guess) (P(G)). Estudos mais recentes [15] buscam tornar este modelo mais adaptável, através de ajustes do parâ-metro P(T), tornando-o mais individualizado para cada estudante.…”

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