Impact of Depression and Stress on the Programming Performance of the Students

Data mining involves the use of sophisticated data analysis tools which discovers previously unknown, valid patterns and relationships in large data sets. Educational Data Mining is concerned with developing methods for exploring the unique types of data that comes from educational scenario and using those methods to better understand students and the settings in which they learn. These days stress and depression are found to be common among students. Programming is considered to be the most essential skill for IT students in order to flourish in life. The objective of this paper is to find out the way in which stress and depression experienced by the students have impact on their programming performance. The level of stress and depression are measured by the use of questionnaires. The mark obtained by the students in the programming language is considered as a measure of programming performance. Frequent-pattern growth algorithm has been used to discover the various patterns available. Association Rule Mining has then been applied to find out the association.