Analyzing student performance using evolutionary artificial neural network algorithm

Abstract

Educational Data Mining (EDM) and Learning Systematic (LS) research have appeared as motivating areas of research, which are clarifying beneficial understanding from educational databases for many purposes such as predicting student's success factor. The ability to predict a student's performance can be beneficial in modern educational systems. This research work aims at developing an evolutionary approach based on genetic algorithm and the artificial neural network. The traditional artificial neural network lacks predicting student performance due to the poor modeling structure and the capability of assigning proper weights to each node under the hidden layer. This problem is overwhelmed with the aid of genetic algorithm optimization approach which produces appropriate fitness function evaluation in each iteration of the learning process. The performances gradually increase the accuracy of the prediction and classification more precisely.

Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning

“…Adaptive learning algorithms can diagnose gaps in understanding and deliver targeted interventions, facilitating mastery over key subject areas. Furthermore, AI can liberate educators from the burden of administrative tasks, enabling them to dedicate more time to instructional activities [ 47 ].…”

Reviewer Perspective on the Essentials of Scientific Experiments Based Research Articles

“…This article does not follow the IMRaD format which is discussed in this article and some other methods are used. Arunachalam and Velmurugan 21 carried out a research work titled "Analyzing student performance using evolutionary artificial neural network algorithm" in the International Journal http://doi.org/10.21124/tsp.2023.9.17 | Page 13…”

“…http://doi.org/10.21124/tsp.2023.9.17 | Page 14 9 Yes No Yes Perumal and Velmurugan 10 Yes No Yes Liu and Wang 11 Yes No Yes de Freitas Barbosa et al 12 Yes No Yes Thambusamy and Umasankar 13 Yes No Yes Naveen and Velmurugan 14 No Yes Yes Sivaram and Ramar 15 No Yes Yes Latha and Velmurugan 16 No Yes No Manimaran and Velmurugan 17 Yes No No Shirodkar and Pereira 18 No Yes No Navitha and Velmurugan 19 No Yes No Mahalakshmi and Velmurugan 20 No Yes Yes Arunachalam and Velmurugan 21 No Yes Yes Romero and Ventura 22 No Yes Yes DeepaLakshmi and Velmurugan 23 No Yes Yes Govindasamy and Velmurugan 24 No Yes No Sukassini and Velmurugan 25 Yes No No Mishra et al 26 Yes No Yes Velmurugan and Hemalatha 27 No Yes Yes SriPradha et al 28 Yes No Yes Hogie et al 29 No Yes No Zhang et al 30 No Yes Yes Fang and Zhan 31 No Yes Yes…”

Design of a machine learning based model for academic performance prediction

“…Many others researchers addressed the problem of predicting student's performance using machine learning tools [7,8,9,10,11,12,13,14].…”