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An Analysis of Teachers’ Performance using Decision Tree Based C5.0 Mapreduce Algorithm using Bigdata Mining

V. Shanmugarajeshwari, R. Lawrance

Abstract


Data mining is one of the potential research fields regarding interdisciplinary aspects. Educational data mining is developing discipline in the present scenario. Classification techniques in the data mining plays an important role in the area of educational data mining. The main goal regarding this work is to predict the teachers’ performance by using the relevant features. The proposed methodology consists of the phases like preprocessing, attribute selection, classification based on decision tree and performance evaluation. In the data preprocessing phase, the missing values have been filluped. The attributes are converted into a categorized format using the categorization MapReduce process. The gain ratio with MapReduce is the best method for feature selection, since it selects technique extracted the relevant attributes in an accurate manner, which takes less time compares to the other feature selection methods. This paper presents a MapReduce algorithm on the classification structure by using C5.0 algorithm, aiming to accumulate time and obtain high accuracy on huge students’ and teachers’ datasets. The classification process based on C5.0 MapReduce algorithm is resulted with good classification accuracy.


Keywords


Educational Data Mining, Students’ and Teachers’ dataset, MapReduce, Classification, C5.0 Algorithm, Big Data Mining.

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References


Bakar, R.S., and Yacef, K, “The state of educational data mining in 2009: A review and future visions.” JEDM-Journal of Educational Data Mining 1.1 (2009):3-17

Barracosa, J.I.M.S.2011. Mining Behaviors from Educational Data

http://library.queensu.ca/webedu/grad/Purpose_of_the_Literature_ Review.pdf

Lawrance, R., Shanmugarajeshwari, V., “Analysis of Students’ Performance Evaluation using Classification Techniques.” IEEE International Conference on Computing Technologies and Intelligent Data Engineering(ICCTIDE’16), 10.1109/ICCTIDE.2016.7725375, 16426971

Agaoglu, M., IEEE Translation and Content Mining, “Predicting Instructor Performance Using Data Mining Techniques in HigherEducation.” Department of Computer Engineering, Marmara University, Istanbul 334722, Turkey. Volume.4, 2016, pp:2379-2387

http://en.wikipedia.org/wiki/Feature_selection

http://www.saedsayad.com/classification.htm

Quinlan, J. R. "Induction of decision trees." Machine learning 1.1 1986, pp. 81-100.


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