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.
@InProceedings{MENG18.997, author = {Yuanliang Meng and Anna Rumshisky and Florence Sullivan}, title = "{Automatic Labeling of Problem-Solving Dialogues for Computational Microgenetic Learning Analytics}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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