Explainable AI in Learning Analytics: Improving Predictive Models and Advancing Transparency Trust
Qinyi Liu, Mohammad Khalil · 2024
As online education becomes more widely available, the amount of data available and accessible has exploded. Such a wealth of educational data provides ample opportunities for the fields of learning analytics and educational data mining to expand and yield numerous benefits, such as identifying at-risk students, providing personalized feedback, and providing actionable insights. Machine learning and deep learning techniques are commonly used for these and other purposes. However, simply applying these technologies is not enough without allowing humans to understand them. Because only those who understand the logic behind the technology can cultivate trust and make the technology work to its maximum effectiveness. To address the gap, this paper focuses specifically on a case study of explainable machine learning techniques for predicting student performance in online courses, and its contribution is twofold. First, we show how the use of explainable AI techniques can inform model diagnosis and direction forward in situations where performance is less than ideal. Secondly, we show how different types of explainable AI techniques can improve transparency and trust in educational scenarios, allowing stakeholders to benefit from explainable AI.