Neural Network based Student Grade Prediction Model
subject: Four Layer Network; Hidden Layers; Neural Network; Neural Network Model
description: Student final grade GPA is the collective efforts of their previous and ongoing efforts of each semester examination may predict accurately using the neural network which receives the input weight of each matrix element of variables to next neuron. The GPA prediction based on regular class performance and previous grades with background variables were found much significant. This research tries to explore the model comparison and evaluate student grade prediction using various neural network models. The single-layer half i.e., successful student model predicts 90 total accuracies than the single layer with five hidden layer neurons (88.5 percent). The multi-layer with two hidden layers (7,3) is 84 percent accuracy is less than one percent accuracy than multilayer with three hidden layers. Similarly, the multilayered with four hidden layered 25,12,7,3 model predicts the least accuracy (77 percent accuracy) for student grade. Similarly, the passed student prediction model has less accuracy than both students' 86 percent. 2022 IEEE.
creator: Rimal Y.; Rathore V.S.; Pageni S.; Samanta D.; Karuppiah M.; Singh D.
source: Proceedings - 2022 International Conference on Machine Learning, Computer Systems and Security, MLCSS 2022, pp. 22-27.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022-01-01
rights: Restricted Access
relation: ISBN: 978-166545493-3
format: Online
language: English
type: Conference paper
coverage: Rimal Y., Pokhara University, Gandaki, Nepal; Rathore V.S., Iis (Deemed to Be University), Department of Computer Science, Jaipur, India; Pageni S., Tribhuvan University Pnc, Department of Department of Education, Kaski, Nepal; Samanta D., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India; Karuppiah M., Srm Institute of Science and Technology, Delhi-NCR Campus, Department of Computer Science and Engineering, Ghaziabad, India; Singh D., Soa University, Department of Ca, Bhubaneswar, India