CFP last date
20 May 2024
Reseach Article

A Study of Application of Data Mining and Analytics in Education Domain

by Sahil P. Karkhanis, Shweta S. Dumbre
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 22
Year of Publication: 2015
Authors: Sahil P. Karkhanis, Shweta S. Dumbre
10.5120/21393-4436

Sahil P. Karkhanis, Shweta S. Dumbre . A Study of Application of Data Mining and Analytics in Education Domain. International Journal of Computer Applications. 120, 22 ( June 2015), 23-29. DOI=10.5120/21393-4436

@article{ 10.5120/21393-4436,
author = { Sahil P. Karkhanis, Shweta S. Dumbre },
title = { A Study of Application of Data Mining and Analytics in Education Domain },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 120 },
number = { 22 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 23-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number22/21393-4436/ },
doi = { 10.5120/21393-4436 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:06:54.498534+05:30
%A Sahil P. Karkhanis
%A Shweta S. Dumbre
%T A Study of Application of Data Mining and Analytics in Education Domain
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 22
%P 23-29
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data Mining techniques and algorithms have been used on a large scale in almost all the sectors which range from computer science, manufacturing industry, and healthcare industry. A recently introduced concept of academic analytics uses the data mining algorithms on the educational data of students and gives certain insights about the expected performances of the students, expected retention rate of students and percentage of resources properly utilized. These results also help administrators in decision making and answering certain questions like whether the faculty v/s student's ratio is giving satisfactory results or there is a change needed in the teaching methodology. Educational Data Mining is also another upcoming field and is an allied field of Academic analytics, but it focuses on the data mining algorithm outputs being given back to the faculties in order to properly assess the student's performance. Educational data mining basically helps the tutors modify their teaching strategies if the results with the current teaching model are not satisfactory. This paper basically is a study of certain research experiments which aim to apply data mining algorithms to educational data and contribute to the field of Academic analytics and Educational Data Mining.

References
  1. Barahate Sachin R. and Shelake Vijay M. , A Survey and Future Vision of Data mining in Educational Field in Proceedings of the IEEE Symp. Second International Conference on Advanced Computing & Communication Technologies.
  2. Crist´obal Romero and Sebasti´an Ventura, Educational Data Mining: A Review of the State of the Art in Proceedings of IEEE transacations on systems, man and cybernetics.
  3. Kannan Govindarajan, Thamarai Selvi Somasundaram, Vivekanandan S Kumar, Kinshuk, Continuous Clustering in Big Data Learning Analytics in Proceedings IEEE Fifth International Conference on Technology for Education.
  4. Charoula Angeli , Nicos Valanides Using educational data mining methods to assess field-dependent and field-independent learners' complex problem solving in Springer Association for Educational Communications and Technology
  5. Carlos Márquez-Vera, Cristóbal Romero Morales, and Sebastián Ventura Soto, Predicting School Failure and Dropout by Using Data Mining Techniques in Proceedings of IEEE Journal of Latin-American learning technologies.
  6. S. Anupama Kumar and Vijayalakshmi M. N, Mining of Student Academic Evaluation Records in Higher Education in Proceedings of International conference on recent advances in computing and Software Systems
  7. Eitel J. M. Lauría, Joshua D. Baron, Mallika Devireddy, Venniraiselvi Sundararaju and Sandeep M. Jayaprakash, Mining academic data to improve college student retention: An open source perspective in Proceedings of ACM Second International Conference on Learning Analytics and Knowledge.
  8. Eitel J. M. Lauría, Joshua D. Baron, Mallika Devireddy, Venniraiselvi Sundararaju and Sandeep M. Jayaprakash, Open Academic Analytics Initiative: Initial Research Findings in Proceedings of ACM Third International Conference on Learning Analytics and Knowledge.
  9. Eitel J. M. Lauría, Joshua D. Baron, Mallika Devireddy, Ven-niraiselvi Sundararaju and Sandeep M. Jayaprakash, Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative in International Journal of Learning Analytics
  10. Reshma Desai, Academic Analytics in Customer Relationship Management Perspective using Data Mining in Proceedings of International Conference in Recent Trends in Information Technology and Computer Science
  11. Mohiuddin Ali Khan, Wajeb Gharibi, Sateesh Kumar Pradhan, Data Mining Techniques for Business Intelligence in Educational System: A Case Mining in Proceedings of IEEE Computer Applications and Information Systems
  12. Mahdi Nasiri, Fereydoon Vafaei, Behrouz Minaei, Predicting GPA and Academic Dismissal in LMS Using Educational Data Mining: A Case Mining in Proceedings of IEEE 6th National and 3rd International conference of e-Learning and e-Teaching
  13. Rainer Knauf, Kinshuk, Kouhei Takada, and Yoshitaka Sakurai, Takashi Kawabe, Setsuo Tsuruta, Personalized and Adaptive Curriculum Optimization Based on a Performance Correlation Analysis in Proceedings of IEEE 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems
  14. Rainer Knauf, Yoshitaka Sakurai, Kouhei Takada, and Setsuo Tsuruta, A Case Study on Using Personalized Data Mining for University Curricula in Proceedings of 2012 IEEE International Conference on Systems, Man, and Cybernetics
  15. Fangming Guo and hua Song, Research and Application of Data-Mining Technique in Timetable Scheduling in Proceedings of Computer Engineering and Technology (ICCET),2nd International Conference
  16. Dr. B. L. Shivakumar and Mr. V. Murugananthan, A Novel Application Framework for Educational Data Mining towards Automated Learning System in Proceedings of IEEE 2014 International Conference on Intelligent Computing Applications.
  17. Bin Mat, U. Shah Alam, Buniyamin, N. Arsad, P. M. , Kassim R, An overview of using academic analytics to predict and improve students' achievement: A proposed proactive intelligent intervention in Proceedings of Engineering Education (ICEED), 2013 IEEE 5th Conference.
  18. Sérgio André Ferreira and António Andrade, Academic Analytics: Mapping the Genome of the University in Proceedings of IEEE Revista Iberoamericana de tecnologias Del aprendizaje.
  19. M. A. Chatti, A. L. Dyckhoff, U. Schroeder, and H. Thüs, A Reference Model for Learning Analytics in International Journal of Technology Enhanced Learning (IJTEL) – Special Issue on State-of-the-Art in TEL.
  20. Che-Cheng Lin and Chiung-Hui Chiu, Correlation between Course Tracking Variables and Academic Performance in Blended Online Courses in Proceedings of 2013 IEEE 13th International Conference on Advanced Learning Technologies.
  21. Eleni Koulocheri Alexandros Soumplis Michalis Xenos, Applying Learning Analytics in an Open Personal Learning Environment in Proceedings of IEEE 2012 16th Panhellenic Conference on Informatics.
Index Terms

Computer Science
Information Sciences

Keywords

Academic Analytics Educational Data Mining Learning Analytics Classification.