Task-based Classification of Reflective Thinking Using Mixture of Classifiers

This paper studies the problem of Reflective Thinking in children during mathematics related problem solving activities. We present our approach in solving task 2 of the AffectMove challenge, which is Reflective Thinking Detection (RTD) while solving a mathematical activity. We utilize temporal data consisting of 3D joint positions, to construct a series of classifiers that can predict whether the subject appeared to possess reflective thinking ability during the given instance. We tackle the challenge of highly imbalanced data by incorporating and analyzing several meaningful data augmentation techniques and handcrafted features. We then feed different features through a number of machine learning classifiers and select the best performing model. We evaluate our predictions on multiple metrics including accuracy, F1 score, and MCC to work towards a generalized solution for the real-world dataset.