Predicting Student’s Performance Using Machine Learning Algorithm
Patil Swapnil Anil, Uday pramod chaudhari, Kangane Swati Sahebrao, Shelar Rupali , Sweety Mahajan
Classification, Data Mining, Supervised Learning, Education , Traditional Methods, Grades.
Although the educational level of the Portuguese population has improved in the last decades, the statistics keep Portugal at Europe’s tail end due to its high student failure rates. In particular, lack of success in the core classes of Mathematics and the Portuguese language is extremely serious. On the other hand, the fields of Machine Learning, which aim at extracting high-level knowledge from raw data, offer interesting automated tools that can aid the education domain. The present work intends to approach student achievement in secondary education using machine learning techniques. Recent real-world data (e.g. student grades, demographic, social and school related features) was collected by using school reports and questionnaires. The two core classes (i.e. Mathematics and Portuguese) were modelled under binary/five-level classification and regression tasks. As a direct outcome of this research, more efficient student prediction tools can be developed, improving the quality of education and enhancing school resource management.
Article Details
Unique Paper ID: 151599

Publication Volume & Issue: Volume 8, Issue 1

Page(s): 336 - 340
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Last Date 25 September 2021

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