Learning Analytics is full of situations where features essential to understanding the learning process cannot be measured. In this work, doctoral candidate Ben Hicks and Kirsty Kitto propose an approach…
Hicks, B., & Kitto, K. (2025). Game Theoretic Models of Intangible Learning Data. Proceedings of the 15th International Learning Analytics and Knowledge Conference, Dublin, IRE. https://doi.org/10.1145/3706468.3706557
Learning Analytics is full of situations where features essential to understanding the learning process cannot be measured. The cognitive processes of students, their decisions to cooperate or cheat on an assessment, their interactions with class environments can all be critical contextual features of an educational system that are impossible to measure. This leaves an empty space where essential data is missing from our analysis. This paper proposes the use of Game Theoretic models as a way to explore that empty space and potentially even to generate synthetic data for our models. Cooperating or free-riding on the provisioning of feedback in a class activity is used as a case study. We show how our initially simple model can gradually be built up to help understand potential educator responses as new situations arise, using the emergence of GenAI in the classroom as a case in point.