Literature Survey on Educational Dropout Prediction
Author(s) -
Mukesh Kumar,
Annapoorna Singh,
Disha Handa
Publication year - 2017
Publication title -
international journal of education and management engineering
Language(s) - English
Resource type - Journals
eISSN - 2305-8463
pISSN - 2305-3623
DOI - 10.5815/ijeme.2017.02.02
Subject(s) - dropout (neural networks) , educational data mining , computer science , context (archaeology) , machine learning , missing data , data mining , artificial intelligence , data science , biology , paleontology
Educational Data Mining (EDM) is one of the crucial application areas of data mining which helps in predicting educational dropout and hence provides timely help to students. In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student. At present EDM has not been introduced at higher education level. Due to this we cannot recognize the genuine problems of students during their education. The objective of this analysis is to find the existing gaps in predicting educational dropout and find the missing attributes if any, which my further contribute for better prediction. After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction. Based on the combination of missing attribute and best attribute of student data thus far, a new algorithm can be tested which may overcome the shortcomings of previous work done.
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