Towards a Bayesian Student Model for Detecting Decimal Misconceptions
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Towards a Bayesian Student Model for Detecting Decimal Misconceptions. / Goguadze, Giorgi; Sosnovsky, Sergey; McLaren, Bruce et al.
Proceedings of the 19th International Conference on Computers in Education, ICCE 2011: ICCE 2011. ed. / Fu-Yun Yu; Tsukasa Hirashima; Thepchai Supnithi; Gautum Biswas. Chiang Mai : Asia-Pacific Society for Computers in Education, 2011. p. 34-41.Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Towards a Bayesian Student Model for Detecting Decimal Misconceptions
AU - Goguadze, Giorgi
AU - Sosnovsky, Sergey
AU - McLaren, Bruce
AU - Isotani, Seiji
N1 - Conference code: 19
PY - 2011
Y1 - 2011
N2 - This paper describes the development and evaluation of a Bayesian network 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 the interactions of 255 students with the software. 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 is capable of producing predictions of high accuracy (up to 87%).
AB - This paper describes the development and evaluation of a Bayesian network 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 the interactions of 255 students with the software. 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 is capable of producing predictions of high accuracy (up to 87%).
KW - Mathematics
KW - Bayesian networks
KW - Bayesian student modeling
KW - Student model evaluation
UR - http://www.scopus.com/inward/record.url?scp=84860476148&partnerID=8YFLogxK
M3 - Article in conference proceedings
SN - 978-616-12-0188-3
SP - 34
EP - 41
BT - Proceedings of the 19th International Conference on Computers in Education, ICCE 2011
A2 - Yu, Fu-Yun
A2 - Hirashima, Tsukasa
A2 - Supnithi, Thepchai
A2 - Biswas, Gautum
PB - Asia-Pacific Society for Computers in Education
CY - Chiang Mai
T2 - 19th International Conference on Computers in Education - ICCE 2011
Y2 - 28 November 2011 through 2 December 2011
ER -