Understanding the role of learning analytics and generative artificial intelligence on decision-making and learning design practice in higher education

Abstract

High-quality learning design is an important contributor to student success. Higher education institutions can leverage the combined potential of learning analytics (LA) and Generative Artificial Intelligence (GenAI) to enhance instructors’ learning design practices. However, the way LA and GenAI are being used in combination for learning design is not well understood. In this context, one theory that can provide valuable insights into instructors’ technology integration into learning design (LD) practice is Self-Determination Theory (SDT). Using SDT as an interpretative lens, this study illuminates the factors in instructors’ learning design practices when utilising LA and GenAI. For this purpose, we employed quantitative ethnography to explore how instructors utilise LA and GenAI for LD. Eleven focus groups were conducted with instructors from a large Australian university. Using thematic analysis, we created a codebook consisting of ten codes that were applied to our dataset and then analysed using Epistemic Network Analysis (ENA). Our results show that instructors’ LA-informed LD discussions primarily revolved around course, contextual, and creative problem-solving considerations, as well as student support. The predominant GenAI factors were assessment design, contextual consideration, creative problem solving and design for student self-determination. Based on our results, we discuss the integration of LA and GenAI in LD practice and decision-making, highlighting the practical implications of our findings.