About this Event
Anna will talk about how to realize the potential of digital mathematics learning programs, with a particular focus on contextual factors. This means that despite the existence of considerable knowledge about effective program design (such as the incorporation of adaptivity, feedback, and scaffolding), it often seems that programs do not fully realize their potential for learning when used in mathematics education. It is reasonable to assume that this is not necessarily due to the programs themselves, but rather to contextual factors such as teacher integration or individual student prerequisites. In her presentation, Anna would like to address this issue, present selected findings from her dissertation where she extensively explored this topic, and discuss future ideas and approaches to enrich the field of research in this area.
In her presentation, Ute will talk about her interests in automated feedback in test situations as well as AI-based feedback in writing processes. As essential factors for successful feedback procedures and AI implementation, a particular focus of her talk is her current interest in the perspective of teachers, including their perceptions and attitudes towards AI-based feedback. Her current work addresses the question of whether teachers perceive AI-based feedback more negatively (i.e. experience algorithm aversion) compared to teacher-generated feedback to gain an understanding of potential (implicit) biases towards AI-based educational systems. Ute will present (preliminary) results on this question and would like to share her ideas for future studies to explore the underlying mechanisms of algorithm aversion in teachers.
Event Venue & Nearby Stays
Russell Hall 5th Fl - Mixed-Use Space, 525 West 120th Street, New York, United States