Faculty workload analytics in academia

Abstract

In the recent decades, many researchers that research on higher education data were exploring into quality teaching using analytic approach. Many state institutes and private institutes are taking this data to another level by analyzing and discover useful information about faculties and student to use it as a competitive edge over others.

Many studies had been made in specific to data analytics in higher education industries using techniques of data mining task. Conducting experiments and creating data mining models from simple data such student grading to complex data such as student feedbacks, faculty activities and complexity of faculty research etc.

In this project, the focus was on analyzing and visualization of faculty workload using post processing phase of data analytics. It consists a series of progressive phases starting from preprocessing of data, understanding the domain knowledge of data, discovering hidden trends, finding visual model to represent these trends, implementing and integrating interactive visual model into an application using Java platform.