Questions play an important role in the educational domain, representing the main form of interaction between instructors and students. In this paper, we introduce the first taxonomy and annotated educational corpus of questions that aims to help with the analysis of student responses. The dataset can be employed in approaches that classify questions based on the expected answer types. This can be an important component in applications that require prior knowledge about the desired answer to a given question, such as educational and question answering systems. To demonstrate the applicability and the effectiveness of the data within approaches to classify questions based on expected answer types, we performed extensive experiments on our dataset using a neural network with word embeddings as features. The approach achieved a weighted F1-score of 0.511, overcoming the baseline by 12%. This demonstrates that our corpus can be effectively integrated in simple approaches that classify questions based on the response type.
@InProceedings{GODEA18.1001, author = {Andreea Godea and Rodney Nielsen}, title = "{Annotating Educational Questions for Student Response Analysis}", 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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