Design and implementation of an expert system for predicting students’ academic performance: A case study of computer science department federal polytechnic Bida
1 Federal Polytechnic Bida, Department of Information Communication Technology, Niger State, Nigeria.
2 Department of Mathematics, Statistics and Computer Science, University of Calabar, Nigeria.
3 Department of Computer Engineering, University of Ilorin, Kwara State, Nigeria.
4 Department of Computer Science, Federal University of Technology Akure, Ondo State, Nigeria.
5 Department of Computer Science, Texas Southern University, Houston, Texas, USA.
6 Department of Mathematics, Tai Solarin University of Education, Ijebu Ode, Ogun State, Nigeria.
7 Department of Mechanical Engineering, Zhejiang University Hangzhou, Hangzhou, Zhejiang, China.
Review Article
World Journal of Advanced Research and Reviews, 2024, 24(02), 915–926
Publication history:
Received on 24 September 2024; revised on 09 November 2024; accepted on 11 November 2024
Abstract:
The application of machine learning techniques in predicting students’ performance, based on their background and their in-term performance has proved to be a helpful tool for foreseeing poor and good performances in various levels of education. The major problem institutions face is the systematic monitoring of students’ academic progress in their course of study, which is influence by many factors. This research work assesses the potential of predicting student academic performance with an expert system in the Federal Polytechnic Bida. The expert system is develop using python programming language and it accepts data about a student in various category such as demographic background, attitudes study habits, etc. and through the partial activation of multiple rules, predicts an outcome for each student. The students’ data was collected via a user-friendly interface and analysed using fuzzy logic algorithm for prediction. The prediction analysis for students’ can only be viewed by the administrator (instructor) as it requires logins for forecasting students’ possible outcomes.
Keywords:
Machine Learning; Student Performance Prediction; Academic Monitoring; Predictive Modeling; Fuzzy Logic Algorithm
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