Bayesian Modelling of Student Misconceptions in the one-digit Multiplication with Probabilistic Programming

Type: Evidence | Proposition: B: Teaching | Polarity: | Sector: | Country:

One-digit multiplication errors are one of the most extensively analysed mathematical problems. Research work primarily emphasises the use of statistics whereas learning analytics can go one step further and use machine learning techniques to model simple learning misconceptions. Probabilistic programming techniques ease the development of probabilistic graphical models (bayesian networks) and their use for prediction of student behaviour that can ultimately influence decision processes.

Citation: Behnam Taraghi, Anna Saranti, Robert Legenstein and Martin Ebner (2016). "Bayesian Modelling of Student Misconceptions in the one-digit Multiplication with Probabilistic Programming". In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK '16). ACM, New York.