Research Article
Student Progress Predictor
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 140 - Issue 12 |
| Published: April 2016 |
| Authors: R. Ksohy, A.V. Sakpal, R. More |
10.5120/ijca2016909521
|
R. Ksohy, A.V. Sakpal, R. More . Student Progress Predictor. International Journal of Computer Applications. 140, 12 (April 2016), 28-32. DOI=10.5120/ijca2016909521
@article{ 10.5120/ijca2016909521,
author = { R. Ksohy,A.V. Sakpal,R. More },
title = { Student Progress Predictor },
journal = { International Journal of Computer Applications },
year = { 2016 },
volume = { 140 },
number = { 12 },
pages = { 28-32 },
doi = { 10.5120/ijca2016909521 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2016
%A R. Ksohy
%A A.V. Sakpal
%A R. More
%T Student Progress Predictor%T
%J International Journal of Computer Applications
%V 140
%N 12
%P 28-32
%R 10.5120/ijca2016909521
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Using data mining algorithms can help discover-ing pedagogically relevant knowledge contained in databases obtained from Web-based educational systems. These findings can be used both to help teachers with managing their class, understand their students learning and reflect on their teaching and to support learner reflection and provide proactive feedback to learners.
References
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- Agrawal, R. R. Srikant. ”Fast Algorithms for Mining Association Rules” in Proceedings of VLDB, Santiago, Chile (2010).
- SPSS, Clementine, www.spss.com/clementine/ (accessed 2011).
- SODAS,http://www.ceremade.dauphine.fr/touati/sodaspagegarde. html (accessed 2013)
- Witten, E. Frank, Data Mining, Practical Machine LearningTools and Techniques with Java Implementation, Morgan Kaufmann Publishers, 2010.
- R. Kirkby, WEKA Explorer User Guide for version 3-3-4, University of Weikato, 2012.
- M. H. Dunham, Data Mining, Introductory and Advanced Topics, Prentice Hall, 2012.
Index Terms
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