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Predicting Student Performance in Solving Parameterized Exercises

Sahebi, Shaghayegh and Huang, Yun and Brusilovsky, Peter (2014) Predicting Student Performance in Solving Parameterized Exercises. In: Intelligent Tutoring Systems 12th International Conference, ITS 2014, 05 June 2014 - 09 June 2014, Honolulu, HI, USA.

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In this paper, we compare pioneer methods of educational data mining field with recommender systems techniques for predicting student performance. Additionally, we study the importance of including students’ attempt time sequences of parameterized exercises. The approaches we use are Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), Bayesian Probabilistic Tensor Factorization (BPTF), and Bayesian Probabilistic Matrix Factorization (BPMF). The last two approaches are from the recommender system’s field.We approach the problem using question-level Knowledge Components (KCs) and test the methods using cross-validation. In this work, we focus on predicting students’ performance in parameterized exercises. Our experiments shows that advanced recommender system techniques are as accurate as the pioneer methods in predicting student performance. Also, our studies show the importance of considering time sequence of students’ attempts to achieve the desirable accuracy.


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Item Type: Conference or Workshop Item (Paper)
Status: Published
CreatorsEmailPitt UsernameORCID
Sahebi, Shaghayeghshs106@pitt.eduSHS106
Huang, Yunyuh43@pitt.eduYUH43
Brusilovsky, Peterpeterb@pitt.eduPETERB0000-0002-1902-1464
Date: June 2014
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Intelligent Tutoring Systems
Volume: 8474
Publisher: Springer International Publishing
Place of Publication: Honolulu, HI, USA
Page Range: 496 - 503
Event Title: Intelligent Tutoring Systems 12th International Conference, ITS 2014
Event Dates: 05 June 2014 - 09 June 2014
Event Type: Conference
DOI or Unique Handle: 10.1007/978-3-319-07221-0
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
School of Information Sciences > Information Science
Refereed: Yes
ISBN: 978-3-319-07220-3
ISSN: 0302-9743
Official URL:
Related URLs:
Date Deposited: 19 Jun 2014 14:44
Last Modified: 01 Nov 2017 12:57


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