Learning analytics offers the possibilities for higher education institutions (HEIs) to unlock the value of educational big data to help make better learning decisions. While it has recently attracted increasing interest, HEIs have made limited use of the available data and more research is yet to be conducted to build the theoretical and empirical base. This study draws on IT affordance theory to develop an exploratory conceptual framework to understand how the action possibilities afforded by learning analytics to HEI users for decision-making can be actualized. The framework is tested using a qualitative research method based on interviews and case studies. This study mainly contributes to the literature by building an exploratory theory and early theory testing about learning analytics affordances in the HEI context. It has the potential to help HEIs better realize the action possibilities afforded by learning analytics to HEI users thereby to improve learning related decision-making.
Cao, Guangming and Duan, Yanqing, "Understanding Learning Analytics from an IT Affordance Perspective" (2017). PACIS 2017 Proceedings. 83.