A Preliminary Model of Learning Analytics to Explore Data Visualization on Educator’s Satisfaction and Academic Performance in Higher Education

Abstract

With the rapid proliferation of online learning due to the Covid-19 pandemic, learning management solutions and software has gained an extraordinary importance in tertiary education. This shift has created large amounts of data from online learning systems that need to be translated into meaningful information, hence data visualization has come into prominent focus as a solution that provides a powerful means to drive Learning Analytics to assess and support educators and students alike in decision-making and sense-making activities from the data collected. Although many research works have been published on data visualization focusing on techniques, tools and best practices, there is still a lack of research in the context of online learning to meet this urgent need of quality data visualization for successful decision-making. In this paper, we explore data visualization that is currently used in learning analytics and present an integrated preliminary model based on DeLone and McLean’s IS Success model to examine the role and significance of data visualization by incorporating it as an antecedent to the Information Quality construct of the IS success model, which will support teaching and learning in an online learning environment for improved educators and student performance. This paper adds to the existing literature by incorporating data visualization to support educators decision-making and its performance impact of online learning through the consideration of the IS success model’s elements. This integrated preliminary conceptual model aims to support online teaching and learning by addressing the research gap that has emerged from the expansion of learning analytics in educational technology.