DR. VIMAL BHATT

Title:Performance Prediction for Post Graduate Students using Artificial Neural NetworkYear: 2019
Authors: Vinod Kumar Pal, Vimal Bhatt Journal: International Journal of Innovative Technology and Exploring Engineering Publication date: 2019 Publisher: BEIESP URL: click here 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.

Title:Performance Prediction for Post Graduate Students using Artificial Neural NetworkYear: 2019

Authors: Vinod Kumar Pal, Vimal Bhatt
Journal: International Journal of Innovative Technology and Exploring Engineering
Publication date: 2019
Publisher: BEIESP
URL: click here

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.

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