Visual Analytics for Faculty Teaching Practice and Performance using Educational Mining Techniques - Khalid, Atikah (IRC, 2020)
Over the years, in making successful careers, higher education has gained prominence over the graduate students. Faculty teaching practice and performance are thus given the utmost importance in developing students’ quality for performance in academics. The performance of the faculty plays an important role in academic institutions. Evaluating the faculty members' performance helps to gather critical information and discover new ways of improving them. In this paper, the proposed system can be used as a comprehensive system for evaluating, reporting and analyzing data with a promising audience by utilizing the visual analytics platform in using the educational mining techniques. Based on different parameters, the faculty teaching practice and performance are evaluated and projected by building models. The sample data is collected, preprocessed, and model learning is done using Decision Tree, Support Vector Machine (SVM) and Artificial Neural Network (ANN) in this evaluation. Besides, an analysis of the variable importance for each classifier model is done to see which questions appear in determining the success of faculty members' performance. The idea of this paper is to indicate the effectiveness of Visual Analytics for Faculty Teaching Practice and Performance using Educational Mining Techniques on Student's Self-Reflection Tool (SSRT) survey.