Multi-Layer Perception model with Elastic Grey Wolf Optimization to predict student achievement

This study proposes a Grey Wolf Optimization (GWO) variant named Elastic Grey Wolf Optimization algorithm (EGWO) with shrinking, resilient surrounding, and weighted candidate mechanisms. Then, the proposed EGWO is used to optimize the weights and biases of Multi-Layer Perception (MLP), and the EGWO-...

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Bibliographic Details
Published in:PloS one Vol. 17; no. 12; p. e0276943
Main Authors: Song, Yinqiu, Meng, Xianqiu, Jiang, Jianhua
Format: Journal Article
Language:English
Published: United States Public Library of Science 30.12.2022
Public Library of Science (PLoS)
Subjects:
Academic achievement
Academic Success
Achievement
Algorithms
Analysis
Behavior
Big Data
Biology and Life Sciences
Classrooms
Colleges & universities
Data mining
Datasets
Ecology and Environmental Sciences
Education
Educational technology
Efficiency
Evaluation
Humans
Leadership
Machine learning
Mathematical analysis
Mathematical models
Mathematical optimization
Methods
Model accuracy
Modelling
Multilayers
Neural networks
Optimization
Optimization algorithms
People and Places
Perception
Physical Sciences
Prediction models
Research and Analysis Methods
Semantics
Social Sciences
Students
Training
Portugal
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:This study proposes a Grey Wolf Optimization (GWO) variant named Elastic Grey Wolf Optimization algorithm (EGWO) with shrinking, resilient surrounding, and weighted candidate mechanisms. Then, the proposed EGWO is used to optimize the weights and biases of Multi-Layer Perception (MLP), and the EGWO-MLP model for predicting student achievement is thus obtained. The training and verification of the EGWO-MLP prediction model are conducted based on the thirty attributes from the University of California (UCI) Machine Learning Repository dataset’s student performance dataset, including family features and personal characteristics. For the Mathematics (Mat.) subject achievement prediction, the EGWO-MLP model outperforms one model’s prediction accuracy, and the standard deviation possesses the stable ability to predict student achievement. And for the Portuguese (Por.) subject, the EGWO-MLP outperforms three models’ Mathematics (Mat.) subject achievement prediction through the training process and takes first place through the testing process. The results show that the EGWO-MLP model has made fewer test errors, indicating that EGWO can effectively feedback weights and biases due to the strong exploration and local stagnation avoidance. And the EGWO-MLP model is feasible for predicting student achievement. The study can provide reference for improving school teaching programs and enhancing teachers’ teaching quality and students’ learning effect.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0276943