Evaluating a Bayesian Student Model of Decimal Misconceptions

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Standard

Evaluating a Bayesian Student Model of Decimal Misconceptions. / Goguadze, Giorgi; Sosnovsky, Sergey; Isotani, Seiji et al.
Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011). Hrsg. / M. Pechenizkiy; T. Calders; C. Conati; S. Ventura; C. Romero; J. Stamper. Eindhoven University Press, 2011. S. 301-305 (EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining).

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Harvard

Goguadze, G, Sosnovsky, S, Isotani, S & McLaren, B 2011, Evaluating a Bayesian Student Model of Decimal Misconceptions. in M Pechenizkiy, T Calders, C Conati, S Ventura, C Romero & J Stamper (Hrsg.), Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011). EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining, Eindhoven University Press, S. 301-305, International Conference on Educational Data Mining - EDM 2011, Eindhoven, Niederlande, 06.07.11.

APA

Goguadze, G., Sosnovsky, S., Isotani, S., & McLaren, B. (2011). Evaluating a Bayesian Student Model of Decimal Misconceptions. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, & J. Stamper (Hrsg.), Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011) (S. 301-305). (EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining). Eindhoven University Press.

Vancouver

Goguadze G, Sosnovsky S, Isotani S, McLaren B. Evaluating a Bayesian Student Model of Decimal Misconceptions. in Pechenizkiy M, Calders T, Conati C, Ventura S, Romero C, Stamper J, Hrsg., Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011). Eindhoven University Press. 2011. S. 301-305. (EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining).

Bibtex

@inbook{b54e767953594e9586fa9efe0f4763a9,
title = "Evaluating a Bayesian Student Model of Decimal Misconceptions",
abstract = "applications of educational data mining, evaluation of student models is essential for an adaptive educational system. This paper describes the evaluation of a Bayesian model of student misconceptions in the domain of decimals. The Bayesian model supports a remote adaptation service for an Intelligent Tutoring System within a project focused on adaptively presenting erroneous examples to students. We have evaluated the accuracy of the student model by comparing its predictions to the outcomes of students' logged interactions from a study with 255 school children. Students' logs were used for retrospective training of the Bayesian network parameters. The accuracy of the student model was evaluated from three different perspectives: its ability to predict the outcome of an individual student's answer, the correctness of the answer, and the presence of a particular misconception. The results show that the model's predictions reach a high level of precision, especially in predicting the presence of student misconceptions.",
keywords = "Mathematics, Bayesian networks, Bayesian student modeling, Student model evaluation",
author = "Giorgi Goguadze and Sergey Sosnovsky and Seiji Isotani and Bruce McLaren",
year = "2011",
language = "English",
isbn = "978-90-386-2537-9",
series = "EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining",
publisher = "Eindhoven University Press",
pages = "301--305",
editor = "M. Pechenizkiy and T. Calders and C. Conati and S. Ventura and C. Romero and J. Stamper",
booktitle = "Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011)",
address = "Netherlands",
note = "International Conference on Educational Data Mining - EDM 2011, EDM 2011 ; Conference date: 06-07-2011 Through 08-07-2011",
url = "http://educationaldatamining.org/EDM2011/",

}

RIS

TY - CHAP

T1 - Evaluating a Bayesian Student Model of Decimal Misconceptions

AU - Goguadze, Giorgi

AU - Sosnovsky, Sergey

AU - Isotani, Seiji

AU - McLaren, Bruce

N1 - Conference code: 4

PY - 2011

Y1 - 2011

N2 - applications of educational data mining, evaluation of student models is essential for an adaptive educational system. This paper describes the evaluation of a Bayesian model of student misconceptions in the domain of decimals. The Bayesian model supports a remote adaptation service for an Intelligent Tutoring System within a project focused on adaptively presenting erroneous examples to students. We have evaluated the accuracy of the student model by comparing its predictions to the outcomes of students' logged interactions from a study with 255 school children. Students' logs were used for retrospective training of the Bayesian network parameters. The accuracy of the student model was evaluated from three different perspectives: its ability to predict the outcome of an individual student's answer, the correctness of the answer, and the presence of a particular misconception. The results show that the model's predictions reach a high level of precision, especially in predicting the presence of student misconceptions.

AB - applications of educational data mining, evaluation of student models is essential for an adaptive educational system. This paper describes the evaluation of a Bayesian model of student misconceptions in the domain of decimals. The Bayesian model supports a remote adaptation service for an Intelligent Tutoring System within a project focused on adaptively presenting erroneous examples to students. We have evaluated the accuracy of the student model by comparing its predictions to the outcomes of students' logged interactions from a study with 255 school children. Students' logs were used for retrospective training of the Bayesian network parameters. The accuracy of the student model was evaluated from three different perspectives: its ability to predict the outcome of an individual student's answer, the correctness of the answer, and the presence of a particular misconception. The results show that the model's predictions reach a high level of precision, especially in predicting the presence of student misconceptions.

KW - Mathematics

KW - Bayesian networks

KW - Bayesian student modeling

KW - Student model evaluation

UR - http://www.scopus.com/inward/record.url?scp=84857480243&partnerID=8YFLogxK

M3 - Article in conference proceedings

SN - 978-90-386-2537-9

T3 - EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining

SP - 301

EP - 305

BT - Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011)

A2 - Pechenizkiy, M.

A2 - Calders, T.

A2 - Conati, C.

A2 - Ventura, S.

A2 - Romero, C.

A2 - Stamper, J.

PB - Eindhoven University Press

T2 - International Conference on Educational Data Mining - EDM 2011

Y2 - 6 July 2011 through 8 July 2011

ER -

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