Author:
Kinjal ,Parida Sagar Mousam,Suthar Jayesh,Pande Sagar Dhanraj
Abstract
The study of students' academic performance is a significant endeavor for higher education schools and universities since it is essential to the design and management of instructional strategies. The efficacy of the current educational system must be monitored by evaluating student achievement. For this research, we used multiple Machine Learning algorithms and Neural Networks to analyze the learning quality. This study investigates the real results of university examinations for B.Tech (Bachelor in Technology) students, a four-year undergraduate programme in Computer Science and Technology. The K-means clustering approach is used to recommend courses, highlighting those that would challenge students and those that will improve their GPA. The Linear Regression method is used to make a prediction of a student’s rank among their batchmates. Academic planners might base operational choices and future planning on the findings of this study.
Publisher
European Alliance for Innovation n.o.
Reference16 articles.
1. Ciolacu M, Tehrani AF, Beer R, Popp H. Education 4.0—Fostering student's performance with machine learning methods. In2017 IEEE 23rd international symposium for design and technology in electronic packaging (SIITME) 2017 Oct 26 (pp. 438-443). IEEE.
2. Xu J, Moon KH, Van Der Schaar M. A machine learning approach for tracking and predicting student performance in degree programs. IEEE Journal of Selected Topics in Signal Processing. 2017 Apr 7;11(5):742-53.
3. Zeineddine H, Braendle U, Farah A. Enhancing prediction of student success: Automated machine learning approach. Computers & Electrical Engineering. 2021 Jan 1;89:106903.
4. Wang X, Zhao Y, Li C, Ren P. ProbSAP: A comprehensive and high-performance system for student academic performance prediction. Pattern Recognition. 2023 May 1;137:109309.
5. Delavari N, Beikzadeh MR, Phon-Amnuaisuk S. Application of enhanced analysis model for data mining processes in higher educational system. In2005 6th international conference on information technology based higher education and training 2005 Jul 9 (pp. F4B-1). IEEE.