Exploring Student Expectations and Confidence in Learning Analytics

Hayk Asatryan, Basile Tousside, Janis Mohr, Malte Neugebauer, Hildo Bijl, Paul Spiegelberg, Claudia Frohn-Schauf, Jörg Frochte

Abstract: Learning Analytics (LA) is nowadays ubiquitous in many educational systems, providing the ability to collect and analyze student data in order to understand and optimize learning and the environments in which it occurs. On the other hand, the collection of data requires to comply with the growing demand regarding privacy legislation. In this paper, we use the Student Expectation of Learning Analytics Questionnaire (SELAQ) to analyze the expectations and confidence of students from different faculties regarding the processing of their data for Learning Analytics purposes. This allows us to identify four clusters of students through clustering algorithms: Enthusiasts, Realists, Cautious and Indifferents. This structured analysis provides valuable insights into the acceptance and criticism of Learning Analytics among students.