Recommender system for predicting student performance

Thai-Nghe, Nguyen; Drumond, Lucas; Krohn-Grimberghe, Artus; Schmidt-Thieme, Lars

doi:10.1016/j.procs.2010.08.006

Cited by 229 publications

(131 citation statements)

References 15 publications

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“…In that case, we predict the correctness of a student's attempt to solve a single or a sequence of problems/tasks/exercises. These approaches include regression models [3,5,10], HMMs and bagged decision trees [18], collaborative filtering techniques and their combination (k-NN, SVD, RBM) [30], matrix completion [11,27,28], and tensor factorization [29].…”

Section: Related Workmentioning

confidence: 99%

Grade prediction with models specific to students and courses

Polyzou

Karypis

2016

Int J Data Sci Anal

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The accurate estimation of students' grades in future courses is important as it can inform the selection of next term's courses and create personalized degree pathways to facilitate successful and timely graduation. This paper presents future course grade predictions methods based on sparse linear and low-rank matrix factorization models that are specific to each course or student-course tuple. These methods identify the predictive subsets of prior courses on a course-by-course basis and better address problems associated with the not-missing-at-random nature of the student-course historical grade data. The methods were evaluated on a dataset obtained from the University of Minnesota, for two different departments with different characteristics. This evaluation showed that focusing on course-specific data improves the accuracy of grade prediction.

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Section: Related Workmentioning

confidence: 99%

Grade prediction with models specific to students and courses

Polyzou

Karypis

2016

Int J Data Sci Anal

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“…(i.e., the ratio between passing and failing students is usually skewed). Recently, (Thai-Nghe et al, 2010;Toscher and Jahrer, 2010) proposed using collaborative filtering, especially matrix factorization for predicting student performance but they have not take the temporal effect into account.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, (Thai-Nghe et al, 2010) have proposed using recommendation techniques, especially matrix factorization, for predicting student performance. The authors have shown that using recommendation techniques could improve prediction results compared to regression methods but they have not taken the temporal effect into account.…”

Section: Introductionmentioning

confidence: 99%

Matrix and Tensor Factorization for Predicting Student Performance

Nguyen

Drumond

Horváth³

et al. 2011

Proceedings of the 3rd International Conference on Computer Supported Education

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Abstract:Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in technology enhanced learning such as recommending resources (e.g. papers, books,...) to the learners (students). In this study, we propose using state-of-the-art recommender system techniques for predicting student performance. We introduce and formulate the problem of predicting student performance in the context of recommender systems. We present the matrix factorization methods, known as the most effective recommendation approaches, to implicitly take into account the latent factors, e.g. "slip" and "guess", in predicting student performance. Moreover, the knowledge of the learners has been improved over the time, thus, we propose tensor factorization methods to take the temporal effect into account. Experimental results show that the proposed approaches can improve the prediction results.

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“…Recently, Thai-Nghe et al (2010a); Toscher and Jahrer (2010) have proposed the use of recommendation techniques, especially matrix factorization, for predicting student performance. The authors have shown that using recommendation techniques could improve prediction results compared to regression methods (Thai-Nghe et al, 2010b) but they have not taken the temporal effect into account.…”

Section: Introductionmentioning

confidence: 99%

“…• formulate the problem of predicting student performance in the context of recommender systems; • propose matrix factorization models for predicting student performance, which are extended from previous works (Thai-Nghe et al, 2010a;Toscher and Jahrer, 2010) and presented in a formalized and detailed way that can be helpful for the researchers who come from educational domains. We can empirically show that these factorization techniques can implicitly encode the "slip" and "guess" factors as well as "student effect" (e.g.…”

Section: Introductionmentioning

confidence: 99%

Factorization Techniques for Predicting Student Performance

Thai-Nghe

Drumond

Horváth

et al. 2012

Educational Recommender Systems and Technologies

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137

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Recommender systems are widely used in many areas, especially in ecommerce. Recently, they are also applied in e-learning for recommending learning objects (e.g. papers) to students. This chapter introduces state-of-the-art recommender system techniques which can be used not only for recommending objects like tasks/exercises to the students but also for predicting student performance. We formulate the problem of predicting student performance as a recommender system problem and present matrix factorization methods, which are currently known as the most effective recommendation approaches, to implicitly take into account the prevailing latent factors (e.g. "slip" and "guess") for predicting student performance. As a learner's knowledge improves over time, too, we propose tensor factorization methods to take the temporal effect into account. Finally, some experimental results and discussions are provided to validate the proposed approach.

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Recommender system for predicting student performance

Cited by 229 publications

References 15 publications

Grade prediction with models specific to students and courses

Grade prediction with models specific to students and courses

Matrix and Tensor Factorization for Predicting Student Performance

Factorization Techniques for Predicting Student Performance

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