Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Educational Data Mining and Learning Analytics in Higher Education


Affiliations
1 P.E.S's Modern College of Engineering, Pune, India
     

   Subscribe/Renew Journal


The emerging fields of academic analytics also the educational data mining are rapidly producing new hypothesis for gathering, analyzing, and presenting student data. Different Area of Computer applications and also business administrations have addition significant standing value in higher education. Thus the kind of education, students turn in these areas depends on the geo-economical and the social demography. Thus the decision making of an institution in these area of higher education dependent on several factors like economic condition of students, geographical area of the institution, quality of educational organizations etc. To have a strategic move for the development of importing knowledge in this area requires understanding the behavior aspect of these parameters. Thus the scientific apprehension of these can be had from obtaining patterns or recognizing the attribute behavior from early academic years. Farther, applying data mining tool to the preceding data on the attributes known will throw better light on the behavioral view of identified patterns.


Keywords

Data Mining, Decision Tree Classification, Educational Data Mining.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Ryan S.J.d. Baker, "Data Mining for Education". International Encyclopedia of Education (3rd edition). Oxford, UK: Elsevier 2010.
  • Bhise R.B., Thorat S.S., and Supekar A.K, “Importance of Data Mining in Higher Education System”, IOSR Journal Of Humanities And Social Science (IOSR-JHSS), Jan-Feb, 2013.
  • Ogunde A. O and Ajibade D. A. ," A Data Mining System for Predicting University Students’ Graduation Grades Using ID3 Decision Tree Algorithm ". Journal of Computer Science and data Technology.
  • Sonali Agarwal, G. N. Pandey, and M. D. Tiwari, “Data Mining in Education: Data Classification and Decision Tree Approach”, International Journal of e-Education, eBusiness, e-Management and e-Learning, Vol. 2, No. 2, April 2012.
  • Brijesh Kumar Baradwaj and Saurabh Pal, “Mining Educational Data to Analyze Students Performance”, International Journal of Advanced Computer Science and Applications, 2011.
  • Jing Luan, “Data Mining Applications in Higher Eduacation”, SPSS publications.
  • Sunita B Aher and Lobo, “Data Mining in Educational System using WEKA”, International Conference on Emerging Technology Trends (ICETT) 2011.
  • Monika Goyal and Rajan Vohra, “Applications of Data Mining in Higher Education”, International Journal of Computer Science Issues, Vol. 9, Issue 2, No 1, March 2012.
  • M. Durairaj, C. Vijitha, “Educational Data mining for Prediction of Student Performance Using Clustering Algorithms”, International Journal of Computer Science and data Technologies, Vol. 5 (4) , 2014, 5987- 5991.
  • Naeimeh DELAVARI, Somnuk PHON-AMNUAISUK, Mohammad Reza BEIKZADEH, “Data Mining Application in Higher Learning Institutions”, Informatics in Education, 2008, Vol. 7, No. 1, 31–54

Abstract Views: 485

PDF Views: 9




  • Educational Data Mining and Learning Analytics in Higher Education

Abstract Views: 485  |  PDF Views: 9

Authors

Padma Mishra
P.E.S's Modern College of Engineering, Pune, India
Vaishali B.
P.E.S's Modern College of Engineering, Pune, India

Abstract


The emerging fields of academic analytics also the educational data mining are rapidly producing new hypothesis for gathering, analyzing, and presenting student data. Different Area of Computer applications and also business administrations have addition significant standing value in higher education. Thus the kind of education, students turn in these areas depends on the geo-economical and the social demography. Thus the decision making of an institution in these area of higher education dependent on several factors like economic condition of students, geographical area of the institution, quality of educational organizations etc. To have a strategic move for the development of importing knowledge in this area requires understanding the behavior aspect of these parameters. Thus the scientific apprehension of these can be had from obtaining patterns or recognizing the attribute behavior from early academic years. Farther, applying data mining tool to the preceding data on the attributes known will throw better light on the behavioral view of identified patterns.


Keywords


Data Mining, Decision Tree Classification, Educational Data Mining.

References





DOI: https://doi.org/10.25089/MERI%2F2018%2Fv12%2Fi1%2F180120