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The student dropout rate in universities is fascinating, especially among the students of Electrical Engineering. Even the most developed European countries face 40% to 50% dropout rate of engineering students during their first year, and the rate can be as high as 80% for some engineering disciplines. This problem calls attention of educators and university administration to take measures which can help in the reduction of the dropout rate and assist students in successfully completing their degree. Among many other solutions to control the student dropout rate, one is the adoption of a prediction mechanism whereby students can be warned about their potentially poor performance so that they can improve their performance resulting in better grades. Most of the existing prediction mechanisms apply various machine learning techniques on student cognitive features. In addition, non-cognitive features also have significant impact on students’ performance; however, they have been sparsely applied for prediction. This research aims at improving the existing prediction mechanism by exploiting both cognitive and non-cognitive features of students for predicting their results. It has been found in the result analysis that addition of cognitive features increases prediction accuracies of decision tree; however, the addition does not play a significant role in other techniques. The study also identified the individual cognitive features that should be considered by students and universities to cater for drop outs.
Sultana et al. (Wed,) studied this question.