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Early Prediction of Students Performance using Machine Learning Techniques
| Content Provider | Semantic Scholar |
|---|---|
| Author | Acharya, Anal Sinha, Devadatta |
| Copyright Year | 2014 |
| Abstract | Anal Acharya, Department of Computer Science, St Xavier’s College, Kolkata, India. Devadatta Sinha, Department of Computer Science and Engineering, University of Calcutta, Kolkata, India. ABSTRACT In recent years Educational Data Mining (EDM) has emerged as a new field of research due to the development of several statistical approaches to explore data in educational context. One such application of EDM is early prediction of student results. This is necessary in higher education for identifying the “weak” students so that some form of remediation may be organized for them. In this paper a set of attributes are first defined for a group of students majoring in Computer Science in some undergraduate colleges in Kolkata. Since the numbers of attributes are reasonably high, feature selection algorithms are applied on the data set to reduce the number of features. Five classes of Machine Learning Algorithm (MLA) are then applied on this data set and it was found that the best results were obtained with the decision tree class of algorithms. It was also found that the prediction results obtained with this model are comparable with other previously developed models. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://research.ijcaonline.org/volume107/number1/pxc3899939.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Algorithmic learning theory Class Computer science Comstock–Needham system Decision tree Educational data mining Feature selection Machine learning college |
| Content Type | Text |
| Resource Type | Article |
