Earliest Possible Global and Local Interpretation of Students’ Performance in Virtual Learning Environment by Leveraging Explainable AI

Abstract

In this research study, we propose an Explainable Artificial Intelligence (XAI) model that provides the earliest possible global and local interpretation of students’ performance at various stages of course length. Global and local interpretation is provided in such a way that the prediction accuracy of a single local observation is close to the model’s overall prediction accuracy. For the earliest possible understanding of student performance, local and global interpretation is provided at 20%, 40%, 60%, 80%, and 100% of course length. Machine Learning (ML) and Deep Learning (DL) which are subfields of Artificial Intelligence (AI) have recently emerged to assist all educational institution’s in predicting the performance, engagement, and dropout rate of online students. Unfortunately, traditional ML and DL techniques lack in providing data analysis results in an understandable human way. Explainable AI (XAI), a new branch of AI, can be used in educational settings, specifically in VLEs, to provide the instructor with the study performance results of thousands or even millions of online students in a human-understandable way. Thus, unlike black box approaches such as traditional ML and DL techniques, XAI can help instructors to interpret the strengths and weaknesses of an individual student, providing them with timely personalized feedback and guidance. Various traditional and various ensemble ML algorithms were trained on demographic, clickstream, and assessment features to determine which algorithm gives the best performance result. The best-performing ML algorithm was ultimately selected and provided to the XAI model as an input for local and global interpretation of students’ study behavior at various percentages of course length. We have used various XAI tools to give students’ performance reports to instructors, in an explicable human way, at different stages of course length. The intermediate data analysis and performance reports will help instructors and all key stakeholders in decision-making and optimally facilitate online students.

Keywords

• Global explainability
• local explainability
• explainable AI
• course length
• decision making
• artificial intelligence