Analysis of the Effectiveness of Online Learning Using Eda Data Science and Machine Learning

Abstract

In educational institutions, it is important to evaluate the performance of lecturers. One aspect that can be used as a reference in assessing the performance of lecturers is the assessment in terms of teaching (learning - teaching process). Value data in the learning evaluation process can be further processed to classify or map the results of the learning evaluation. In this study, the authors use Data Science and Exploratory Data Analysis (EDA) techniques and apply the K-Means++ clustering algorithm using the Java programming language to determine the value of the learning process. group learning evaluation data. To determine the number of clusters to be formed, the elbow method is applied. Meanwhile, to measure the quality of each cluster formed, the silhouette coefficient method is applied. The elbow graph can visualize the Sum of Square Error (SSE) value as a way to determine the best k value in the clustering process. From the tests carried out 12 times, using 6 datasets with variations in 4 areas of assessment, 10 tests resulted in a value of k = 3, 10 tests produced a value of k = 4, and 1 test resulted in a value of k = 5 as the best number of clusters. In the cluster quality measurement process, 13 tests have good clustering quality, because on average they produce clusters that have a good structure with a Global Silhouette Coefficient > 0.65.

GS Cited Analysis