Please use this identifier to cite or link to this item:
http://dspace.aiub.edu:8080/jspui/handle/123456789/1332
Title: | Grading System Prediction of Educational Performance Analysis Using Data Mining Approach |
Authors: | Rahman, Mahfujur Hasan, Mehedi Billah, Md Masum Sajuti, Rukaiya Jahan |
Keywords: | Text Classification Data Mining Machine Learning Predictive Model Educational Development |
Issue Date: | 16-Nov-2022 |
Publisher: | Penteract Technology |
Abstract: | In the neoteric century, education holds the key to bringing tremendous upgradation to the world. In most Asian countries, it is very challenging to apply education data mining techniques due to the variety of institutional data categories. In this research, an efficient data collection technique has been designed to gather institutional data, analyse and pre-process the data and apply specific data mining methods to estimate students’ progress. A real-time dataset has been designed from student transcript data, which helps to analyse the prediction of student quality. In our research, six traditional classification algorithms and a deep neural network (DNN) model is applied to perform prediction efficiency. Different classification models perform an accuracy of 90% ~ 94%. Our research predicts student education efficiency, analyses student patterns and introduces a generalized framework for an advanced level of study. |
URI: | http://dspace.aiub.edu:8080/jspui/handle/123456789/1332 |
ISSN: | 2785-8901 (Online) |
Appears in Collections: | Publications: Journals |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Grading System Prediction.pdf | 221.21 kB | Adobe PDF | View/Open | |
Grading System Prediction_(Front Page).pdf | 151.01 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.