Human-Computer Interaction modelling can benefit from machine learning. This paper presents a case study of the use of machine learning for the development of two interrelated Bayesian Networks for the purposes of modelling student interactions within Intelligent Learning Environments. The models predict (a) whether a given student's interaction is effective in terms of learning and (b) whether a student can answer correctly questions in an intelligent learning environment without requesting help. After discussing the requirements for these models, the paper presents the particular techniques used to pre-process and learn from the data. The case study discusses the models learned based on data collected from student interactions on their own time and location. The paper concludes by discussing the application of the models and directions for future work.
Adaptive Intelligent Learning Environments