IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 8 - Issue 10, October 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Survey on data mining Techniques for Educational Dataset

[Full Text]

 

AUTHOR(S)

Ravindra Rawat, Narender Kumar

 

KEYWORDS

Data Mining, Educational Dataset, Analytics, Supervised, Unsupervised, Machine Learning.

 

ABSTRACT

Educational data mining is definitely an inclination, worried with developing approaches for discovering, and analyzing the large details, which come from the educational circumstance. At the brief minute, there can be an increasing curiosity in details mining and educational program, making educational data mining to be a new growing analysis community. This paper Study a past application and history of data gold mining techniques in the educational field. The achievement of the plentiful work needs a lot more specialized job to ensure that educational data exploration to become mature region .That review pursues a twofold goal, the foremost is to preserve and improve the chronicles of most recent educational data mining (EDM) advances production: the second reason is to arrange, analyze, and discuss the content of the review based on the outcome made by a data mining or prospecting (DM) approach. The review concludes utilizing a snap shot of the surveyed EDM functions, and provides a comprehensive analysis of the EDM strengths a weakness, opportanities, and dangers whose elements represent, in this true way future function to be completed. This paper study the use of data mining to regular educational systems, particular online classes ,well-know .learning articles management systems, and intelligent and adaptable web-based educational systems.

 

REFERENCES

[1] Koedinger K, Cunningham P, Skogsholm A, Leber
Udemaerket. An open up test and repository equipment for fine-grained, longitudinal learner data. In: Initial International Meeting on Educational Data Mining.
[2] Amershi, S., Conati, C. (2006) Automatic Reputation of Novice Groupings in Exploratory Learning conditions, proceeding of IT REALLY IS 2006. 8th International seminar on Intelligent Tutoring programming.
[3] Corbett, A. T., & Anderson, J. R. (1995).Understanding Tracing: Modeling the Acquisition of Procedural Understanding.
[4] Desmarais, M. C., Maluf, Your, Liu, J. (1996) User-knowledge modeling with empirically produced probabilistic implication systems.
[5] Hershkovitz, A, Nachmias, R. (2008) Creating a Log-Based Motivation Measure Tool, Proceedings of the Initial International Conference on Educational Data Mining.
[6] Moore, A. (2005) Statistical Data Mining Tutorials.

[7] Heiner, C., Heffernan, N.T., Barnes, T., (Eds.) (2007) Proceedings of the Workshop on Educational Data Mining, at the 13th International Conference on Artificial Intelligence in Education.
[8] Romero, C., Ventura, S. (2006) Data Mining in e Learning. Southampton, UK: WIT Press.

[9] Romero, C., Ventura, S. (2007) Educational Data Mining: A Survey from 1995 to 2005.Expert Systems with Applications.
[10] Baker R.S.J.d., Barnes, T., Beck, J.E. (Eds.) (2008) Proceedings of the 1st International Conference on Educational Data Mining.
[11] Shih, B., Koedinger, K.R., Scheines, R. (2008) A Response-Time Model for Bottom-Out Hints as Worked Examples. Proceedings of the First International Conference on Educational Data Mining.
[12] Siemens G, Baker R.S.J.d. Learning analytics and educational data mining: towards communication and collaboration.
[13] Baepler P, Murdoch CJ. Academic analytics and data mining in higher education. Int J Schoolarship Teach Learn 2010.
[14] Romero C, Ventura S. Educational data mining a survey from 1995 to 2005.
[15] Baker R.S.J.d, Yacef K.The state of educational data mining in 2009: a review and future visions.