Detecting Students' Behavior on the E-Learning System Using SVM Kernels - Based Ensemble Learning Algorithm

The corona pandemic has changed learning methods from face-to-face to online. However, the application of online learning creates difficulties for teachers in monitoring student behavior because of the reduced direct interaction. This problem causes the learning process to be less optimal. Moreover, students may fail to achieve learning objectives. This research addresses this problem by building a model to detect student behavior in this online learning. It focuses on finding an optimal model by exploring the ensemble learning-stacking method based on a combination of SVM kernels (Linear, Polynomial, RBF, Sigmoid). After the model was built, it was evaluated using two performance measurement techniques, namely: cross-validation and percentage split, and several performance measures, namely: AUC, Accuracy, F1, Precision, and Recall. The evaluation results show the superiority of the models applying ensemble learning over those without it. In terms of accuracy, the highest result in the cross-validation technique is 98.4%, achieved by three models employing stacking. Those three are with base learners combination of linear-polynomial-sigmoid kernel (LinPolSig_Stack), a combination of linear-RBF-sigmoid kernel (LinRBFSig_Stack), and a combination of all kernels-linear, polynomial, RBF, sigmoid (AllKernels_Stack). In the percentage split technique, the highest performance is 97.4%, achieved by two models implementing ensemble learning-stacking with base-learners combination of RBF-sigmoid kernel (RBFSig_Stack) and combination of linearpolynomial-sigmoid kernel (LinPolSig_Stack). Finally, the highest performance of these models is equivalent to the minimum error in detecting student behavior. Detection errors were only three students in the three models in the cross-validation technique and only six in the two models in the percentage split technique. © 2023, International Journal of Intelligent Engineering and Systems. All Rights Reserved.

Classifying the Students' Behavior on e-Learning System using Fine-Tuning K-NN Method

monitoring the student's behavior is challenging for teachers in online learning, which is crucial to solving. It is because, in this pandemic period, online learning is required to minimize the spreading of coronavirus. However, research in this domain is not much. This study provides an alternative to this problem by classifying students' behavior in the e-Learning system, where the k-NN is applied to mine the students' behavior. In addition, this paper also tests the proper parameters to improve the performance of k-NN: k and distance. The experimental result shows that the best performance on the cross-validation technique is reached by Euclidean distance and, on the percentage-split, is achieved by distance-Manhattan. These are indicated by the highest accuracy level obtained by neighbors of five and 20 fold, about 96.9% on the cross-validation technique. On the percentage split technique, the highest accuracy level, about 95.3%, is reached by neighbors of four and split 50%. In this best performance, four students are misclassified on the cross-validation and six on the percentage split. © 2022 IEEE.

Clustering the Students' Behavior on the e-Learning using the Density-Based Algorithm

The corona pandemic has changed the learning method from conventional to a more flexible one, such as through the internet. Consequently, students may have less direct interaction with teachers. This condition has made it difficult for teachers to monitor the students' behavior. This research works on this problem by focussing on the clustering of students' behavior using the DBSCAN, which is a density-based algorithm. Noises generated in this process can be considered students who do the uncommon behavior when taking the e-Learning system. Further, we evaluate the resulted clusters using the silhouette index to find their quality. The experimental result shows that the DBSCAN can differentiate clusters containing noises. By taking the silhouette index, the Manhattan distance parameter is superior to that of Euclidean.. © 2021 IEEE.

Covid Symptom Severity Using Decision Tree

Corona is a very contagious virus. In a pandemic like this, people often worry whether they are infected or not. When they cough, they often worry whether it is a sign of covid-19 or an ordinary cough. From the clinical symptoms can actually be known whether someone has Covid or not. In this study, a clinical symptom dataset will be used to classify the symptoms using a Decision Tree algorithm. The decision trees used in this research arc J48 and Hoeffding Tree. Decision Tree is one of the most popular classification methods because it is easy to interpret by Humans. the prediction model uses a hierarchical structure. The concept is to convert data into decision trees or decision rules. the result of J48 were slightly better than the Hoeffding tree in terms of accuracy, precision, and recall. Meanwhile, from the tree view results, the Hoeffding Tree is simpler and the number of nodes is less than J48.