• D. Manjula
• N.Saravanan

Although the educational level of population has improved in the last decades, the statistics keep Portugal at Europe’s tail end due to its high student failure rates. On the other hand, the fields of Business Intelligence (BI)/Data Mining (DM), 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 BI/DM techniques.. The two core classes (i.e. Mathematics and Portuguese) were modeled under binary/five-level classification and regression tasks. Also, four DM models (i.e. Decision Trees, Random Forest, Neural Networks and Support Vector Machines) and three input selections (e.g. with and without previous grades) were tested. The results show that a good predictive accuracy can be achieved, provided that the first and/or second school period grades are available. 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. This paper proposes the use of data available at some university to access the variables that can best predict student progression. We combine virtual learning environment(VLE) and management information systems student records datasets and apply the random forest(RF) algorithm to ascertain which variables. RF demand useful in this case because of the large amount of data available for analysis.

"A Higher Education Predictive Model Using Random Forest Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 8, page no.616-621, August-2018, Available :http://www.jetir.org/papers/JETIRC006424.pdf

"A Higher Education Predictive Model Using Random Forest Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 8, page no. pp616-621, August-2018, Available at : http://www.jetir.org/papers/JETIRC006424.pdf