%0 Book Section %1 citeulike:13207658 %A Sahebi, Shaghayegh %A Huang, Yun %A Brusilovsky, Peter %B Intelligent Tutoring Systems %D 2014 %E Trausan-Matu, Stefan %E Boyer, KristyElizabeth %E Crosby, Martha %E Panourgia, Kitty %I Springer International Publishing %K edm knowledge-tracing %P 496--503 %R 10.1007/978-3-319-07221-0_62 %T Predicting Student Performance in Solving Parameterized Exercises %U http://dx.doi.org/10.1007/978-3-319-07221-0_62 %V 8474 %X 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.

@inbook{citeulike:13207658, abstract = {{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.}}, added-at = {2018-03-19T12:24:51.000+0100}, author = {Sahebi, Shaghayegh and Huang, Yun and Brusilovsky, Peter}, biburl = {https://www.bibsonomy.org/bibtex/235d980fe37649ce606da186c3b7d02ab/aho}, booktitle = {Intelligent Tutoring Systems}, citeulike-article-id = {13207658}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-319-07221-0_62}, citeulike-linkout-1 = {http://link.springer.com/chapter/10.1007/978-3-319-07221-0_62}, doi = {10.1007/978-3-319-07221-0_62}, editor = {Trausan-Matu, Stefan and Boyer, KristyElizabeth and Crosby, Martha and Panourgia, Kitty}, interhash = {4107abae100b9b9bc7526335f8428ef2}, intrahash = {35d980fe37649ce606da186c3b7d02ab}, keywords = {edm knowledge-tracing}, pages = {496--503}, posted-at = {2014-06-02 19:08:31}, priority = {0}, publisher = {Springer International Publishing}, series = {Lecture Notes in Computer Science}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Predicting Student Performance in Solving Parameterized Exercises}}, url = {http://dx.doi.org/10.1007/978-3-319-07221-0_62}, volume = 8474, year = 2014 }