Open proctor: An academic integrity tool for the open learning environment

Abstract

Managing integrity of continuous and authentic assessments in an open and distance environments is a complex process. The core challenge is to map student identities with their academic work in effective and efficient manners, while preserving privacy, ensuring minimal disruption, minimizing impacts on accessibility and convenience. To address this issue, we have developed a prototype cloud-based application entitled OpenProctor that employs machine-learning techniques to analyze patterns in the learner-produced textual content in order to provide identity and authorship assurance. In contrast to the traditional academic integrity approaches that seek to control the remote learning environment, our method takes advantage of the readily available learner-generated data to analyze how the students go about doing their academic work. We present the framework, the architecture and main functions of the OpenProctor system and discuss the future research directions and plans for the system’s future development.