Performance Prediction for Post Graduate Students using Artificial Neural Network
Vinod Kumar Pal1, Vimal Kamlesh Kumar Bhatt2
1Vinod Kumar Pal, Balaji Institute of International Business, Pune, India.
2Vimal Kamlesh Kumar Bhatt, Balaji Institute of Modern Management, Pune, India.
Manuscript received on 15 May 2019 | Revised Manuscript received on 22 May 2019 | Manuscript Published on 02 June 2019 | PP: 446-454 | Volume-8 Issue-7S2 May 2019 | Retrieval Number: G10760587S219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Understanding university students’ performance and identifying factors that affect university students’ performance are very important issues for educational institutions, educators, and students. To extract some meaningful information from these large volumes of data, academic organizations have to mine the data. The current system of evaluating student performance is not feasible and it has been observed that it often leads to dissatisfaction among the students, as in the absence of correct predictors of success in educational institutes, students and institutions put emphasis on incorrect predictors and invest time and resources in. This paper presents a comprehensive study on predicting student performance in R Programming for postgraduate students using deep learning (which is a small part of the artificial neural network). The study’s objective is 1) to study the prediction accuracy rate using R programming 2) to analyze the factors that affect academic achievement that contribute to predicting academic performance of students. The researchers evaluated the proposed methods on a dataset consisting of 395 student’s records with 30 attributes of students has been gathered from UCI Repository after the collected data has been preprocessed, cleaned, and filtered using R Programming.
Keywords: Artificial Neural Network, Educational Data Mining, Prediction of Students’ Performance, University Education.
Scope of the Article: Data Mining and Warehousing