Automatic Labeling of Problem-Solving Dialogues for Computational Microgenetic Learning Analytics

Abstract

This paper presents a recurrent neural network model to automate the analysis of students' computational thinking in problem-solving dialogue. We have collected and annotated dialogue transcripts from middle school students solving a robotics challenge, and each dialogue turn is assigned a code. We use sentence embeddings and speaker identities as features, and experiment with linear chain CRFs and RNNs with a CRF layer (LSTM-CRF). Both the linear chain CRF model and the LSTM-CRF model outperform the naive baselines by a large margin, and LSTM-CRF has an edge between the two. To our knowledge, this is the first study on dialogue segment annotation using neural network models. This study is also a stepping-stone to automating the microgenetic analysis of cognitive interactions between students.

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