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Analysis of Student Performance Using Mining Technique: A Review

Vijay Anand Sullare, R.S. Thakur, Bharat Mishra

Abstract


Today’s higher education is one of the needs of any students for growing his life.One of the biggest challenges with higher education is thatinstitutions would like to know, which students will enroll in which course, and which students will need more assistance in particular subject and what efforts should be taken for weak students.Data mining is one of the powerful tools to extract knowledge from large educational database and it can be used for decision making in educational system.

In this paper data mining techniques such as clustering,Association, Rule mining etc. based work is analyzed which will give direction to the educational institution for improving performances of their students. This review also helps to researchers for choosing appropriate data mining techniques to analyzed student data.


Keywords


Educational Data Mining, Association Rule Mining, Classification, Clustering, Soft Computing.

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References


Dr. Abdullah AL-Malaise et al. “STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUE” International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.4, No.5, pp 1 -20 ,September 2014.

Abeer Badr El Din Ahmed et al. “ Data Mining: A prediction for Student's Performance Using Classification Method” , World Journal of Computer Application and Technology 2(2),pp 43-47, 2014.

Samrat Singh et al . “Performance Analysis of Engineering Students for Recruitment Using Classification Data Mining Techniques ”, IJCSET ,Vol 3, Issue 2, ISSN:2231-0711, pp 31-37 February 2013 .

www.weakipedia.com/data mining.

Alaa M. El-Halees et al. “Mining Educational Data to Improve Students’ Performance:A Case Study “ in International Journal of Information and Communication Technology Research Volume 2 No. 2, ISSN 2223-4985 , pp 140-146,February 2012.

Shibbir Ahmed et al. “ Knowledge Discovery from Academic Data using Association Rule Mining” , 17th International Conference on Computer and Information Technology (ICCIT) , pp 314-319, IEEE2014.

Maria Goga et al. “A recommender for improving the student academic performance” ,The 6th International Conference Edu World 2014 &“Education Facing Contemporary World Issues”, pp 1481-1488 ,7th - 9th November 2014 ,www.sciencedirect.com.

Ajinkya Kunjir et al. “ Recommendation of Data Mining Technique in Higher Education” International Journal of Computational Engineering Research (IJCER) ,ISSN (e): 2250 – 3005 ,Volume, 05 ,Issue, 03 ,pp 29-34,March – 2015 .

D. Magdalene Delighta Angeline et al. “Association Rule Generation for Student Performance Analysis using Apriori Algorithm” The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), Vol. 1, No. 1, pp 12-16,March-April 2013.

Renza Campagni et al “Data mining models for student careers” journal Expert Systems with Applications ,www.elsevier.com/locate/eswa , Science Direct ,pp5508–5521 ,2015.

J.K. Jothi Kalpana et al” Intellectual Performance Analysis of Students by Using Data Mining Techniques” ,International Journal of Innovative Research in Science, Engineering and Technology , Volume 3, Special Issue 3, pp 1922-1929,March 2014 .

Bhise R.B et al “Importance of Data Mining in Higher Education System” IOSR Journal Of Humanities And Social Science (IOSR-JHSS) ISSN: 2279-0837, ISBN: 2279-0845. Volume 6, Issue 6, PP 18-21 , (Jan. - Feb. 2013),www.Iosrjournals.Org.

Md. Hedayetul Islam Shovon et al “Prediction of Student Academic Performance by an Application of K-Means Clustering Algorithm ” ,International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 7, ISSN: 2277 128X, pp 353-355, July 2012 ,www.ijarcsse.com

Mr. Bhushan S. Olokar et al “Application of Data Mining Technique for Prediction of Academic Performanceof Student A Literature survey” International Journal on Recent and Innovation Trends in Computing and Communication , ISSN: 2321-8169, Volume: 2 Issue: 12 pp 3962 – 3965,dec 2014.

Sonali Agarwal et al ” Data Mining in Education: Data Classification and Decision Tree Approach”, International Journal of e-Education, e-Business, e-Management and e-Learning, Vol. 2, No. 2, pp 140-144,April 2012.

Ya-Han Hu et al “Developing early warning systems to predict students’ online learning performance” Science Direct Computers in Human Behavior journal ,pp469-478, 2014 www.elsevier.com/ locate/comphumbeh.

Brijesh Kumar Baradwaj et al ,“Mining Educational Data to Analyze Student’s Performance” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, pp 63-69,2011.

Sushmita Mitra et al “Data Mining in Soft Computing Framework: A Survey” IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 1, pp 3-14, JANUARY 2002.

AZWA ABDUL AZIZ et al “MINING STUDENTS’ ACADEMIC PERFORMANCE” Journal of Theoretical and Applied Information Technology , Vol. 53 No.3.ISSN: 1992-8645 pp485-490, 31st July 2013.


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