Ana Tecilazić, Silvija Grgić and Ivana Verveger from Algebra University, Croatia, held a workshop at this year’s European Quality Assurance Forum (EQAF) hosted by University of Twente, the Netherlands. The workshop titled Data-Driven Policymaking: How Can AI Support Student Progression? thematised key reasons for student drop-out, measures for supporting student progression and completion as well as the use of AI in the development of support measures and the human element in the use of AI to support student progression and completion.
Quality assurance in higher education is tightly connected to the topics of progression and completion, as higher education institutions normally strive to prevent students from dropping out of studies by supporting their academic and psychosocial development. AI can aid higher education institutions in this regard by identifying students who, based on certain indicators, are at the risk of achieving poorer academic results and repeating study years and/or dropping out of studies, and by suggesting support measures.
Such data-driven institutional policymaking focused on student progression and early student support is being developed at Algebra University using relevant data on students to identify those at risk. More research on student progression, completion and drop-out as well as AI’s role in student progression and completion is planned at Algebra University, with the aim of advancing the University´s student support system.
