Publication Type : Book Chapter
Thematic Areas : Learning-Technologies
Publisher : Proceedings of the Second International Conference on Computer and Communication Technologies: IC3T 2015,
Source : Proceedings of the Second International Conference on Computer and Communication Technologies: IC3T 2015, Volume 1, Springer India, New Delhi, p.639–649 (2016).
Url : http://dx.doi.org/10.1007/978-81-322-2517-1_61
ISBN : 9788132225171
Keywords : Higher order orthogonal iteration algorithm, HOSVD, Kernel smoothing, Recommendation systems, Tensors
Campus : Amritapuri
School : School of Engineering
Center : Amrita Center For Research in Analytics, AmritaCREATE
Department : Computer Science
Year : 2016
Abstract : Predicting Student Performance is the process that predicts the successful completion of a task by a student. Such systems may be modeled using a three-mode tensor where the three entities are user, skill, and task. Recommendation systems have been implemented using Dimensionality reduction techniques like Higher Order Singular Value Decomposition (HOSVD) combined with Kernel smoothing techniques to bring out good results. Higher Order Orthogonal Iteration (HOOI) algorithms have also been used in recommendation systems to bring out the relationship between the three entities, but the prediction results would be largely affected by the sparseness in the tensor model. In this paper, we propose a generic enhancement to HOOI algorithm by combining it with Kernel smoothing techniques. We perform an experimental comparison of the three techniques using an ITS dataset and show that our proposed method improves the prediction for larger datasets.
Cite this Research Publication : Prof. Prema Nedungadi and Smruthy, T. K., “Enhanced Higher Order Orthogonal Iteration Algorithm for Student Performance Prediction”, in Proceedings of the Second International Conference on Computer and Communication Technologies: IC3T 2015, Volume 1, S. Chandra Satapathy, K. Raju, S., Mandal, J. Kumar, and Bhateja, V., Eds. New Delhi: Springer India, 2016, pp. 639–649
