Enhancing student performance prediction using bidirectional long short- term memory networks with secretary bird optimization

M. Kannan; Dr. K. R. Ananthapadmanaban

Author(s)

M. Kannan Department of Computer Science, Faculty of Science and Humanities, SRM Institute of Science and Technology,Vadapalani Campus, Chennai, Tamilnadu,

Dr. K. R. Ananthapadmanaban Department of Computer Science, Faculty of Science and Humanities, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, Tamilnadu

Abstract

The complexities of student related issues were captured by internal and external setup of educational institutions which designed to anticipate student performance accurately to render timely and effective engagement, and significant challenges. Due to suboptimal data handling and feature selection methods many traditional algorithms is in underperformed level. This study utilized Secretary Bird Optimization (SBO) algorithm for Bidirectional Long Short-Term Memory (BiLSTM) to predict students performance. This model evaluatoon starts with preprocessing, SBO feature selection, feature learning carried out by BiLSTM networks. The real dataset was utilized for evaluation of SBO with BiLSTM networks. Integration of these advanced techniques produced high accuracy over existing methods. This models attained accuracy of 97.51% and precision of 95.21% , 95.87 for recall and 97.10 for f1-Score . This research work results were recorded that SBO-BiLSTM networks validate and predict students performance and their outcome in timely with higher accuracy.

Publication Details

Page(s) 1005-1015

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: S1, Year: 2024

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Keywords

Prediction, Secretary Bird Optimization, Bidirectional Long Short-Term Memory, Educational Institutions Students performance and outcomes

References

Batool S.,Rashid J.,Nisar M. W.,Kim J.,Kwon H. Y.,Hussain A. .2023 .Educational data mining to predict students' academic performance: A survey study. Education and Information Technologies, 28(1) : 905-971.

Baig M. A.,Shaikh S. A.,Khatri K. K.,Shaikh M. A.,Khan M. Z.,Rauf M. A.,Emerg M. A. .2023 .Prediction of Students Performance Level Using Integrated Approach of ML Algorithms. , 18(1) : 216-234.

Kuadey N. A.,Ankora C.,Tahiru F.,Bensah L.,Agbesi C. C. M.,Bolatimi S. O. .2024 .Using machine learning algorithms to examine the impact of technostress creators on student learning burnout and perceived academic performance. International Journal of Information Technology, 16(4) : 2467-2482.

Guleria P.,Sood M. .2023 .Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling. Education and Information Technologies, 28(1) : 1081-1116.

Chen S.,Ding Y. .2023 .A machine learning approach to predicting academic performance in Pennsylvania's schools. Social Sciences, 12(3) : 118.

Anggrawan A.,Hairani H.,Satria C. .2023 .Improving SVM classification performance on unbalanced student graduation time data using SMOTE. International Journal of Information and Education Technology, 13(2) : 289-295.

Sghir N.,Adadi A.,Lahmer M. .2012 .Recent advances in Predictive Learning Analytics: A decade systematic review (. Education and information technologies, 28(7) : 8299-8333.

Cardona T.,Cudney E. A.,Hoerl R.,Snyder J. .2023 .Data mining and machine learning retention models in higher education. Journal of College Student Retention: Research, Theory & Practice, 25(1) : 51-75.

Alsabhan W. .2023 .Student cheating detection in higher education by implementing machine learning. LSTM techniques. Sensors, 23(8) : 4149.

Alnasyan B.,Basheri M.,Alassafi M. .2024 .The Power of Deep Learning Techniques for Predicting Student Performance in Virtual Learning Environments: A Systematic Literature Review. Computers and Education: Artificial Intelligence, 100231 : .

Li S.,Liu T. .2021 .Performance prediction for higher education students using deep learning. Complexity, : 1-10.

Hussain S.,Muhsin Z.,Salal Y.,Theodorou P.,Kurtoğlu F.,Hazarika G. .2019 .Prediction model on student performance based on internal assessment using deep learning. International Journal of Emerging Technologies in Learning, 14(8) : .

Al-Azazi F. A.,Ghurab M. .2023 .A deep learning model for early student performance prediction in MOOC. , 9(4) : .

Asselman A.,Khaldi M.,Aammou S. .2023 .Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interactive Learning Environments, 31(6) : 3360-3379.

Anand G.,Kumari S.,Pulle R. .2023 .Fractional-Iterative BiLSTM Classifier: A Novel Approach to Predicting Student Attrition in Digital Academia. International Journal of Computer Science and Engineering, 10(5) : 1-9.

Dataset V.,Thamilarasi V.,Kannan M. .2024 .Enhancing Student Performance Prediction Using Deep Belief Networks with Ant Lion Optimization. International Journal of Intelligent Systems and Applications in Engineering, 12(21s) : 4505.