PREDICTING COURSE GRADES OF STUDENTS’ ACADEMICPERFORMANCE USING THE LIGHTGBM REGRESSOR
DOI:
https://doi.org/10.47344/sdubnts.v62i1.952Keywords:
Machine learning, grades prediction, outliers’ identification, LGBM Regressor, Linear RegressionAbstract
In the modern world, using all available opportunities and
technologies, special attention should be paid to the development of the
education system of students, since education serves as the basis for the
development of the future generation. Nowadays, thanks to the use of available
Artificial Intelligence methods, it is possible to predict various events, anomalies
or other important things. With the help of machine learning, it is possible to
predict at an early stage of a student's education whether he will finish the course
successfully or not. In this study, it is proposed to predict the final score which
student will receive at the end of the course using a number of predictors as an
assessment for the first quiz and 3 types of tasks using the LightGBM regressor,
which is a high-performance algorithm with gradient boosting. The results of
using the LGBM regressor using GridSearchCV allowed to determine the best
settings of hyperparameters from three selected tree-like boosting methods:
'dart', 'gbdt', 'goss'. The GOSS method was determined to be the best of the three
methods listed with an estimate of R2 score in 0.81, which is 0.24 more than the
R2 score of the Linear Regression forecast of – (0.57).
