Survey of Technologies for Evaluation of Student Dropout Using Educational Data
Keywords:
Cloud Computing, Fault Tolerance, Virtual Machines Migration, Resource ManagementAbstract
Interpretation of the dropout students and the reason behind is the most important for the universities. Due to many different reasons such as pressure, low performance, high expectations from family, faculties and individuals it is being tough to sustain for the students. Most important source of knowing the expressions of the students in these instances is their social media interactions with other students. They express their major problems on it. But this is a challenge to process such huge data and evaluating expressions from it. Data mining techniques have given a boost in such processing and application of machine learning has become boon for it. It is found that there are many such techniques available but newest techniques which are best fit in processing of expressional data is machine learning. Surveys of such techniques have become a great source of expression evaluation.
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