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This is an era of computers and technology. Nowadays Computer Science (C.S.) and other technology-related subjects are a hot cake for the students. Due to a good job market for these subjects, students are taking computer science and other related topics without thinking about their capability and without knowing the curriculum of these subjects. So the dropout rate is getting high day by day in these subjects. Especially developing countries like Bangladesh. In this work, we have used current computer science students' data to predict their and also prospective C.S. students' future performance and the chance of dropout using machine learning algorithms like SVM, naive Bayes, neural network, etc. We have also predicted the crucial factors that are strongly correlated to the performance of a C.S. student.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 4 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |