Recent Advances in Multimodal Educational Data Mining in K-12 Education

In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build e↵ective study habits. This task is rather challenging due to the widely-varying quality and pedagogical styles of dialogic instructions. To address these challenges, we utilize pre-trained language models, and propose a multi-task paradigm which enhances the ability to distinguish instances of di↵erent classes by enlarging the margin between categories via contrastive loss. Furthermore, we design a strategy to fully exploit the misclassified examples during the training stage. Extensive experiments on a real-world online educational data set demonstrate that our approach achieves superior performance than other baselines. To encourage reproducible results, we make our code online available at https://github.com/AIED2021/multitask- dialogic-instruction.