Assessment of academic success in online learning environments using learning analytics data and machine learning

During the Covid-19 pandemic, the use of online learning environments is rapidly increasing. Estimation and classification studies of student success with machine learning methods on learning analytics data generated in these environments have gained importance in recent years. In order to understand the relationship between learning analytics data obtained from the online learning environment and student success; in this thesis, we deal with the estimation and classification of student success using the learning analytic data. With these aims, we propose a method based on One-Hot-Encoding (OHE) representation of data, feature selection, and Convolutional Neural Network (CNN) architecture for the estimation and classification of student success. We determine the features related to student success by using correlation, feature importance, fisher score, selectKbest, and knowledge gain feature selection methods on the data set. We also perform the normalization of the selected features and transform the representation of the data with OHE method. To demonstrate the efficacy of the proposed CNN-based architecture we also employ traditional machine learning algorithms such as Random Forest (RF), Multilayer Perceptron (MLP), and k-Nearest Neighbor, (k-NN) in the analyses. For the learning analytics data, we use the Moodle data, which is the online learning environment of Başkent University of the 2020-2021 academic year, and the Open University online learning dataset of years 2013-2014 in England. The results on the Başkent University dataset show that the proposed CNN model with- and without-OHE in three-class classification (fail, pass, distinction) score is higher than the traditional machine learning methods. We also compare the results of binary (fail, pass), three-class (withdrawn, fail, pass) and four-class (withdrawn, fail, pass, distinction) classification performance of our proposed CNN-based architecture on the Open University dataset. We achieved better results than the literature with the highest accuracy rates of 95.43% in two-class classification, 88% in three-class classification and 73.32% in four-class classification. For the estimation of student's grade, Root mean square error (RMSE) and mean absolute error (MAE) values remained below 1% in the proposed CNN-based model, giving a low error rate compared to other models. As a result, the proposed method achieves promising and better results in the evaluations. KEYWORDS: Student performance classification, Convolutional Neural Network, One-hot encoding (OHE), Learning analytics, Feature selection, Covid-19