Multi-Tier Student Performance Evaluation Model (MTSPEM) with Integrated Classification Techniques for Educational Decision Making

Authors

E. S. Vinoth Kumar^1, , S. Appavu alias Balamurugan^{2, *,} , S. Sasikala^3,

¹Associate Professor, Department of Computer Science and Engineering, K.L.N College of Information Technology, Sivagangai, India

²Associate Professor, Department of Computer Science, Central University of Tamilnadu, Thiruvarur, India

³Associate Professor, Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai, India

^*Corresponding author. Email: datasciencebala@gmail.com

DOI

10.2991/ijcis.d.210609.001How to use a DOI?

Keywords

Student performance analysis; Primary classification; Naive Bayes Classification; Ensemble classifiers; Boosting; Stacking and Random Forest (RF); Classification accuracy

Abstract

In present decade, many Educational Institutions use classification techniques and Data mining concepts for evaluating student records. Student Evaluation and classification is very much important for improving the result percentage. Hence, Educational Data Mining based models for analyzing the academic performances have become an interesting research domain in current scenario. With that note, this paper develops a model called Multi-Tier Student Performance Evaluation Model (MTSPEM) using single and ensemble classifiers. The student data from higher educational institutions are obtained and evaluated in this model based on significant factors that impacts greater manner in student's performances and results. Further, data preprocessing is carried out for removing the duplicate and redundant data, thereby, enhancing the results accuracy. The multi-tier model contains two phases of classifications, namely, primary classification and secondary classification. The First-Tier classification phase uses Naive Bayes Classification, whereas the second-tier classification comprises the Ensemble classifiers such as Boosting, Stacking and Random Forest (RF). The performance analysis of the proposed work is established for calculating the classification accuracy and comparative evaluations are also performed for evidencing the efficiency of the proposed model.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - E. S. Vinoth Kumar
AU  - S. Appavu alias Balamurugan
AU  - S. Sasikala
PY  - 2021
DA  - 2021/06/16
TI  - Multi-Tier Student Performance Evaluation Model (MTSPEM) with Integrated Classification Techniques for Educational Decision Making
JO  - International Journal of Computational Intelligence Systems
SP  - 1796
EP  - 1808
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210609.001
DO  - 10.2991/ijcis.d.210609.001
ID  - VinothKumar2021
ER  -