Call for Paper - October 2019 Edition
IJCA solicits original research papers for the October 2019 Edition. Last date of manuscript submission is September 20, 2019. Read More

Data Mining Approach towards Students Behavior Assessment Methods for Higher Studies

Print
PDF
 
Download Full Text
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 30
Year of Publication: 2018
Authors:
Sharad Gangele, Kirti Soni, Sunil Patil
10.5120/ijca2018918099

Sharad Gangele, Kirti Soni and Sunil Patil. Data Mining Approach towards Students Behavior Assessment Methods for Higher Studies. International Journal of Computer Applications 181(30):11-14, November 2018. BibTeX

@article{10.5120/ijca2018918099,
	author = {Sharad Gangele and Kirti Soni and Sunil Patil},
	title = {Data Mining Approach towards Students Behavior Assessment Methods for Higher Studies},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2018},
	volume = {181},
	number = {30},
	month = {Nov},
	year = {2018},
	issn = {0975-8887},
	pages = {11-14},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume181/number30/30171-2018918099},
	doi = {10.5120/ijca2018918099},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The quality education to the students is primary point of higher educational institutions. One of the method to achieve high quality education in higher education system is analysis of students behavior with reference to examination performance, mark sheets, abnormal values and other students activities. Data mining methods discovers knowledge from these records for analysis and prediction about students behavior. In this paper, data mining techniques such as association rules and classification are applied to analyze and present a behavior model of students. The students behavior assessment framework is proposed as model for analysis using data mining technique, the model presents the indication to the critical quantities that regulate the students behavior on learning method. The proposed framework can be applied to extract valuable data that shows all characteristic of student behavior by clustering and subdivision of the student behavior large data set.

References

  1. A. Dutt, M. A. Ismail, and T. Herawan, “A systematic review on educational data mining,” IEEE Access, vol. 5, pp. 15 991– 16 005, 2017.
  2. R. S. Baker, “Educational data mining: An advance for intelligent systems in education,” IEEE Intelligent Systems, vol. 29, no. 3, pp. 78–82, May 2014.
  3. A. do Socorro da Silva, S. R. de Brito, N. L. Vijaykumar, C. A. J. da Rocha, J. C. W. A. Costa, and C. R. L. Frances, “Social network analysis to monitor interactions in virtual learning environment,” IEEE Latin America Transactions, vol. 13, no. 10, pp. 3482–3487, Oct 2015.
  4. Y. Wang, “Mechanism of virtual learning environment system,” in e-Learning, e-Education, and Online Training, S. Liu, M. Glowatz, M. Zappatore, H. Gao, B. Jia, and A. Bucciero, Eds. Cham: Springer International Publishing, 2018, pp. 346–352.
  5. W. F. McComas, Virtual Learning Environment. Rotterdam: SensePublishers, 2014, pp. 110–110. [Online]. Available: https://doi.org/10.1007/978-94-6209-497-0 99
  6. “E-learning: the future,” ITNOW, vol. 51, no. 4, p. 28, 2009. [Online]. Available: http://dx.doi.org/10.1093/itnow/bwp071
  7. M. W. Rodrigues, L. E. Zrate, and S. Isotani, “Educational data mining: A review of evaluation process in the e-learning,” Telematics and Informatics, 2018. [Online]. Available: http://www.sciencedirect.com/science/ article/pii/S0736585317306639
  8. O. Solberg, J. U. Miller, S. Heivang, and M. Bjerke, “E-learning for carers,” Orphanet Journal of Rare Diseases, vol. 5, no. 1, p. P13, Oct 2010. [Online]. Available: https://doi.org/10.1186/1750-1172-5-S1-P13
  9. N. Cavus, “Distance learning and learning management systems,” Procedia - Social and Behavioral Sciences, vol. 191, pp. 872 – 877, 2015, the Proceedings of 6thWorld Conference on educational Sciences. [Online]. Available: http://www. sciencedirect.com/science/article/pii/S1877042815028712
  10. R. McDaniel, J. R. Fanfarelli, and R. Lindgren, “Creative content management: Importance, novelty, and affect as design heuristics for learning management systems,” IEEE Transactions on Professional Communication, vol. 60, no. 2, pp. 183– 200, June 2017.
  11. R. Calvo, A. Iglesias, and L. Moreno, “Accessibility barriers for users of screen readers in the moodle learning content management system,” Universal Access in the Information Society, vol. 13, no. 3, pp. 315–327, Aug 2014. [Online]. Available: https://doi.org/10.1007/s10209-013-0314-3
  12. N. Kerimbayev, J. Kultan, S. Abdykarimova, and A. Akramova, “Lms moodle: Distance international education in cooperation of higher education institutions of different countries,” Education and Information Technologies, vol. 22, no. 5, pp. 2125–2139, Sep 2017. [Online]. Available: https://doi.org/10.1007/s10639-016-9534-5
  13. A. Badia, D. Mart´in, and M. G´omez, “Teachers’ perceptions of the use of moodle activities and their learning impact in secondary education,” Technology, Knowledge and Learning, Mar 2018. [Online]. Available: https://doi.org/10.1007/s10758-018-9354-3
  14. A. Ahadi, A. Hellas, and R. Lister, “A contingency table derived method for analyzing course data,” ACM Trans. Comput. Educ., vol. 17, no. 3, pp. 13:1–13:19, Aug. 2017. [Online]. Available: http://doi.acm.org/10.1145/3123814
  15. R. Pellungrini, L. Pappalardo, F. Pratesi, and A. Monreale, “A data mining approach to assess privacy risk in human mobility data,” ACM Trans. Intell. Syst. Technol., vol. 9, no. 3, pp. 31:1–31:27, Dec. 2017. [Online]. Available: http://doi.acm.org/10.1145/3106774
  16. B. K. Baradwaj and S. Pal, “Mining educational data to analyze students’ performance,” CoRR, vol. abs/1201.3417, 2012. [Online]. Available: http://arxiv.org/abs/1201.3417
  17. J. Schaffer, B. Huynh, J. O’Donovan, T. Hllerer, Y. Xia, and S. Lin, “An analysis of student behavior in two massive open online courses,” in 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Aug 2016, pp. 380–385.
  18. G. Sharma and S. K. Vishwakarma, “Analysis and prediction of students academic performance in university courses,” International Journal of Computer Applications, vol. 160, no. 4, pp. 40–44, Feb 2017. [Online]. Available: http://www.ijcaonline.org/archives/volume-160/ number4/27065-2017913045
  19. A. Perti and M. C. Trivedi, “Article: Markov process for behaviour analysis in social media,” IJCA Proceedings on National Conference on Next Generation Technologies for e- Business, e-Education and e-Society, vol. NGTBES 2016, no. 1, pp. 31–33, July 2016, full text available.
  20. L. Don, H. M. J., and E. Douglas, “The natural mathematics of behavior analysis,” Journal of the Experimental Analysis of Behavior, vol. 109, no. 3, pp. 451–474. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/jeab.330
  21. F. G. Miller, A. H. Johnson, H. Yu, S. M. Chafouleas, D. B. McCoach, T. C. Riley-Tillman, G. A. Fabiano, and M. E. Welsh, “Methods matter: A multi-trait multi-method analysis of student behavior,” Journal of School Psychology, vol. 68, pp. 53 – 72, 2018. [Online]. Available: http://www. sciencedirect.com/science/article/pii/S0022440518300207
  22. K. Kularbphettong, “Analysis of students’ behavior based on educational data mining,” in Applied Computational Intelligence and Mathematical Methods, R. Silhavy, P. Silhavy, and Z. Prokopova, Eds. Cham: Springer International Publishing, 2018, pp. 167–172.
  23. D. Shukla and A. Alim, “Big data analytics approach using indexing and ranking for excellence in higher education,” International Journal of Computer Applications, vol. 180, no. 35, pp. 8–22, Apr 2018. [Online]. Available: http://www.ijcaonline.org/ archives/volume180/number35/29288-2018916878
  24. R. Agrawal and R. Srikant, “Fast algorithms for mining association rules in large databases,” in Proceedings of the 20th International Conference on Very Large Data Bases, ser. VLDB ’94. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1994, pp. 487–499. [Online]. Available: http://dl.acm.org/citation.cfm?id=645920.672836.

Keywords

Behavior Analysis, Data Mining Technique, Prediction Analysis, Association, Classification