Incorporating Explainable Learning Analytics to Assist Educators with Identifying Students in Need of Attention
Shiva Shabaninejad, Hassan Khosravi, Solmaz Abdi, Marta Indulska, Shazia Sadiq
Abstract
Increased enrolments in higher education, and the shift to online learning that has been escalated by the recent COVID pandemic, have made it challenging for instructors to assist their students with their learning needs. Contributing to the growing literature on instructor-facing systems, this paper reports on the development of a learning analytics (LA) technique called Student Inspection Facilitator (SIF) that provides an explainable interpretation of students learning behaviour to support instructors with the identification of students in need of attention. Unlike many previous predictive systems that automatically label students, our approach provides explainable recommendations to guide data exploration while still reserving judgement about interpreting student learning to instructors. The insights derived from applying SIF in an introductory Information Systems course with 407 enrolled students suggest that SIF can be utilised independent of the context and can provide a meaningful interpretation of students' learning behaviour towards facilitating proactive support of students.