A Review on Visualization of Educational Data in Online Learning - Dewan, M. Ali Akber; Pachon, Walter Moreno; Lin, Fuhua (Learning Technologies and Systems, 2021)
Higher educational institutions capture huge amounts of educational data, especially in online learning. Data mining techniques have shown promises to interpret these data using different patterns . However, understanding the mining patterns and extracting meaningful information from the data require reasonable skills and knowledge for the users. Information visualization, due to its potential to display large amount of data, may fill this gap. In this paper, we present a short review of such visualization systems that focus on extracting meaningful information from the educational data. Visualizations have been used in different applications dealing with educational data, especially for monitoring student performance, understanding learning style, analyzing course and program status, and dropout prediction. In this paper, we reviewed the existing visualization systems, their design considerations, and their strengths and weaknesses to analyze educational data in the context of online learning. Research findings indicate that although some progress has been achieved in educational data mining and visualizations, designing and developing effective and easy to understand visualizations and having the functionalities of interactivity and time-series analysis are still challenging. This review provides insight into how to build a learner and instructor focused effective visualization system for an online learning environment.