Role of Secondary Attributes to Boost the Prediction Accuracy of Students’ Employability Via Data Mining
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2015, Vol 6, Issue 11
Abstract
Data Mining is best-known for its analytical and prediction capabilities. It is used in several areas such as fraud detection, predicting client behavior, money market behavior, bankruptcy prediction. It can also help in establishing an educational ecosystem, which discovers useful knowledge, and assist educators to take proactive decisions to boost student performance and employability. This paper presents an empirical study that compares varied classification algorithms on two datasets of MCA (Masters in Computer Applications) students collected from various affiliated colleges of a reputed state university in India. One dataset includes only primary attributes, whereas other dataset is feeded with secondary psychometric attributes in it. The results showcase that solely primary academic attributes don’t lead to smart prediction accuracy of students’ employability, once they square measure within the initial year of their education. The study analyzes and stresses the role of secondary psychometric attributes for better prediction accuracy and analysis of students’ performance. Timely prediction and analysis of students’ performance can help Management, Teachers and Students to work on their gray areas for better results and employment opportunities.
Authors and Affiliations
Pooja Thakar, Prof. Dr. Anil Mehta, Dr. Manisha
Keywords
Related Articles
- EP ID EP127981
- DOI 10.14569/IJACSA.2015.061112
- Views 88
- Downloads 0
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