i-manager Publications

Prediction of Slow Learners in Higher Educational Institutions using Random Forest Classification Algorithm

B. Rakesh*, K. Malli Priya**, J. Harshini***

* Assistant Professor, Sree Vidyanikethan Engineering College (Autonomous), Tirupati, India.

**-*** UG Scholar, Sree Vidyanikethan Engineering College (Autonomous), Tirupati, India.

Periodicity:May - July'2015
DOI : https://doi.org/10.26634/jcc.2.3.4794

Abstract

Educational data mining is one of the fields where there is lot of scope for research, which helps educational institutions to analyse the learning capability of the students. And also gives scope to the educational institutions to make modifications in the curriculum and also to change the teaching methodologies based upon the learning capability of a student. Here, this paper concentrates on the learning capability of the students in higher educational institutions. For that, a dataset of 300 records was collected with various socio-economical and graduate attribute factors. Various classification algorithms was performed on the dataset using Weka, an open source tool. Random forest classification algorithm was found as the best performing algorithm on the dataset. This algorithm was used to design an user interface which is used to predict the future state of a student.

Keywords

Educational Data Mining, Higher Educational Institutions, Weka, Random Forest Classification Algorithm.

How to Cite this Article?

Rakesh,B., Priya, K. M., and Harshini, J. (2015). Prediction of Slow Learners in Higher Educational Institutions using Random Forest Classification Algorithm. i-manager’s Journal on Cloud Computing, 2(3), 30-36. https://doi.org/10.26634/jcc.2.3.4794

References

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	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25

Prediction of Slow Learners in Higher Educational Institutions using Random Forest Classification Algorithm

Abstract

Keywords

How to Cite this Article?

References

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