Home > Archives > IJSRST17342
Prediction of Student performance in Higher Education System using R Programming
Authors(1) :-Neha Kawchale
This document Education plays a vital role in nationâ€™s overall development process. To be effective, analysis related to education sector must be done timely and cope with data scales. Now-a-days the biggest challenges that students face in higher education is predicting the right career paths. Institutions would like to know, which students will be industry ready (get job), who will enroll in which domain and which students will need more assistance in particular subject in terms of job opportunities. Also sometimes, management need more information about student like their result, about the success of new offered courses. Predictive analytics using the R programming language can help and improve the quality of education by analyzing the historical data of the student and allow the decision makers address factors such as unemployment, recommender systems for professional development and curriculum Development to reduce the gap between educational sector and industry requirement. This programming language provides software environment for statistical analysis, graphics representation and finally generates the report. This paper presents an analytical study of student progress report and help to plan accordingly to achieve success using R programming with the help of predictive modelling.
Academic analytics, C5.0 algorithm, Logistics regression model, Predictive analytics, R programming language.
- Jindal Rajni and Dutta Borah Malaya, “Predictive Analytics in a Higher Education Context “IT Professional ,2015IEEE Journals & Magazines Volume: 17, Issue: 4,Pages: 24 - 33, DOI: 10.1109/MITP.2015.68
- Shankar M. Patil “Predictive Analytics in Higher Education”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 12, December 2015.
- A.V. Barneveld et al., “Analytics in Higher Education: Establishing a Common Language,” Educause, Jan.2012; www.educause.edu/library/resources/analytics-higher-education-stablishing-common-language.
- Karthikeyan Natesan Ramamurthy, Moninder Singh, Michael Davis, J. Alex Kevern, Uri Klein, and Michael Peran,” Identifying Employees for Re-Skilling using an Analytics-Based Approach “,2015 IEEE 15th International Conference on Data Mining Workshops, pp. 345-354, DOI 10.1109/ICDMW.2015.206.
- Matthew Malensek; Sangmi Pallickara; Shrideep Pallickara, “Analytic Queries over Geospatial Time-Series Data Using Distributed Hash Tables “,2016 IEEE Transactions on Knowledge and Data Engineering, DOI: 10.1109/TKDE.2016.2520475.
- Joseph Berral; Nicolas Poggi; David Carrera; Aaron Call; Rob Reinauer; Daron Green ,“ALOJA: A Framework for Benchmarking and Predictive Analytics in Hadoop Deployments” ,Computing Year: 2015, Volume: PP, Issue: 99Pages: 1 - 1, DOI: 10.1109/TETC.2015.2496504.
- P. Raj and G.C. Deka, “Big Data Predictive and Prescriptive Analytics,” A Handbook of Research on Cloud Infrastructure for Big Data Analytics, IGI Global, 2014, pp. 370–391; doi:10.4018/978-1-4666-5864-6.ch015.
- Predicting the Future of Predictive Analytics, SAP report, Dec. 2013; http://tinyurl.com/ooygmtq.
- E. Sigel, “Seven Reasons You Need Predictive Analytics, “Prediction Impact, 2010; http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?infotype=SA&subtype=WH&htmlfid=YTW03080USEN.
- R. Jindal and M. Dutta Borah, “A Survey on Educational Data Mining and Research Trends,” Int’lJ. Database Management Systems, vol. 5, no. 3, 2013, pp. 53–73.
Published in : Volume 3 | Issue 4 | May-June 2017
Date of Publication : 2017-06-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 81-87
Manuscript Number : IJSRST17342
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Neha Kawchale, "Prediction of Student performance in Higher Education System using R Programming", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 4, pp.81-87, May-June-2017
URL : http://ijsrst.com/IJSRST17342