Investigating the Effect of Feature Selection to Increase the Accuracy of Predicting Learners' Performance in an Online Educational Environment

Today, we are in the era of big data, and high-dimensional data is growing in the education system, and this rapid growth has created challenges in efficient and effective data management. Proper data management allows us to obtain the necessary knowledge from big data more quickly and accurately with methods such as data mining and machine learning. One of the ways to increase prediction accuracy in machine learning algorithms is feature engineering. Feature engineering is one of the most important steps to increase the model's predictive performance and produce a quality dataset. Many studies have shown that performing feature engineering before data classification is necessary and necessary in order to obtain optimal results. Feature selection methods as part of feature engineering increase the efficiency of the learning process. The goal of a feature selection method is to identify relevant features and remove irrelevant features in order to obtain a suitable subset of features. So as to increase the accuracy of performance prediction and this selected set is able to describe the original data set well. In this research, using feature selection methods, the effect of features on the results of predicting the performance of learners has been investigated.

کلیدواژه ها:

Machine learning ، Feature engineering ، Feature selection ، Data mining ، Big Data.