A Systematic Survey for Students Performance Prediction with Holistic and Sustainable Education approach using Educational Data Mining

Abstract

Information about education is conveyed via text, audio, video, images, and other methods. It is essential to enhance students' learning, perception, and comprehension in the context of education because the majority of public inquiries, work, studies, and research are now conducted online. Some of the algorithms, techniques, and methods employed by Educational Data Mining (EDM) to forecast student performance include the Decision Tree (DT), Outlier Detection, Association Rule, Naive Bayes (NB), K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Neural Network, Relationship Mining, Regression Analysis, Random Forest (RF), and Social Network Analysis (SNA). 126 research articles from the core of EDM and other sources were examined to support the study, out of 4507 research publications that were cited at least once on Google Scholar. This paper will also identify the gaps related to value-based education in study and the occurrence of holistic education cum development through adding Universal Human Values (UHV) to STEM (Science, Technology, Engineering, and Mathematics) (UHV-STEM or UHVSTEM), as well as Time Series Analysis find suitable for student performance prediction during pandemic times. So, finally prediction of holistic and sustainable education of all possible forms can be explore through using Educational Data Mining approaches, tools, and techniques. In this sequence, the fourth sustainable development goal of the United Nations can be satisfied by value-based education, or UHV-STEM education. In India, UHV education already adopted by All India Council for Technical Education, University Grant Commission.