Computationally Modelling the Processes of Learning and Teaching

Computational models of the processes of teaching and learning can support the process of design and evaluation of novel learning strategies and technologies. For instance, a simulation of students, based on probabilistic models built from assumptions about students learning processes and trained on student data, has been used to understand the behavior of students in MOOCs, to analyze the inductive reasoning strategies of children, and to predict the rate of progress of students for activities in classrooms.

Similarly, we can consider the challenge where a teacher has to deliver the same sequence of examples to a diverse group of students with different prior knowledge and learning rates. One way to solve such a challenge is by using machine teaching, an inverse problem of machine learning, where the target is known. The goal of this project is to build robust computational models of the teaching and learning processes which can guide the design and orchestration of complex learning scenarios in technology-enhanced classrooms.