# A Two-Phase Ensemble-Based Method for Predicting Learners' Grade in MOOCs
###### Abstract  
MOOCs are online learning environments which many students use, but the success rate of online learning is low. Machine learning can be used to predict learning success based on how people learn in MOOCs. Predicting the learning performance can promote learning through various methods, such as identifying low-performance students or by grouping students together. Recent machine learning has enabled the development of predictive models, and the ensemble method can assist in reducing the variance and bias errors associated with single-machine learning. This study uses a two-phase classification model with an ensemble technique to predict the learners' grades. In the first phase, binary classification is used, and the non-majority class is then sent to the second phase, which is multi-class classification. The new features are computed based on the distance from the class's center. The distance between the data and the center of an overlapping cluster is calculated using silhouette score-based feature selection. Lastly, Bayesian optimization boosts the performance by fine tuning the optimal parameter set. Using data from the HMPC- and the CNPC datasets, the experiment results demonstrate that the proposed design, the two-phase ensemble-based method, outperforms the state-of-the-art machine learning algorithms. learning performance; grade prediction; MOOC; imbalance data; ensemble method +
Footnote †: journal: Medical Imaging  
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into groups, but the students' actions can also be used to figure out which course each student should take. The system can use this information to suggest courses to students with similar methods of learning [6]. The data from MOOCs are used to train simple deep learning, recommending the best course for each learner. Some learning systems require the learners to pass assessments or submit assignments to receive grades for evaluating their performance. Nevertheless, how a learner learns will affect how well they perform at every stage of the learning process. Consequently, various studies concentrate on predicting the performance of learners. By predicting their performance, the learners can figure out their learning state and improve or change how they learn [7]. In addition, instructors or course owners might apply the prediction results to enhance their teaching or learning materials. In addition, performance information can be combined with other learning behavior characteristics in a learning analysis. Even though many students try to gain enhanced knowledge and skills through MOOCs, the success rate is low [8]. Thus, when MOOCs discuss statistics regarding the success of their students, imbalanced data are usually one of the most prominent features. Clustering Synthetic Minority Oversampling Technique (SMOTE) oversampling is broadly applied to determine the imbalance problem for learner grade prediction [9]. However, it is important to note that resampling data will cause some biases, while undersampling will lead to information loss. In contrast, oversampling increases the risk of overfitting [10], and it cannot overcome the data distributing problem [11]. This study shows how to deal with unbalanced data using a hybrid strategy that combines a two-phase ensemble-based architecture with a centroid distance feature. Following these contributions, the proposed architecture is used to estimate the students' grades based on two imbalanced MOOC datasets. The experiment in this research will collect data without oversampling or undersampling, which could lead to an unbalanced set of data. The new distance features will be chosen based on their silhouette score before they are added to the model. To discover the optimal set of classification parameters, Bayesian optimization is applied. These are the contributions of this research:  
* The two-phase architecture of the grade prediction model is constructed using the ensemble approach.