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Enhanced Higher Order Orthogonal Iteration Algorithm for Student Performance Prediction

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)

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.

Keywords

Recommendation systems HOSVD Higher order orthogonal iteration algorithm Kernel smoothing Tensors 

Notes

Acknowledgment

This work derives direction and inspiration from the Chancellor of Amrita University, Sri Mata Amritanandamayi Devi. We would like to thank Dr. M. Ramachandra Kaimal, Head, Department of Computer Science and Dr. Bhadrachalam Chitturi, Associate Professor, Department of Computer Science, Amrita University for their valuable feedback.

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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Amrita CREATEAmrita Vishwa VidyapeethamKollamIndia
  2. 2.Department of Computer ScienceAmrita Vishwa VidyapeethamKollamIndia

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