Abstract
The Pedagogical Assessment Workflow System (PAWS) is a new workflow-based pedagogical assessment framework that enables the efficient and robust integration of diverse datasets for the purposes of student assessment. The paper highlights two particular e-learning workflows supported by PAWS. The first workflow correlates student performance, as measured by project grades, with different dialogue roles, information seeker and information provider, that students take on in project-based discussion forums. The second workflow identifies the distribution of question topics within student discussions. Both workflows employ state of the art natural language processing techniques and machine learning algorithms for dialogue classification tasks. Workflow results were reviewed with a course instructor and feedback regarding the analysis and its fidelity are reported.
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References
Gil, Y., Ratnakar, V., Kim, J., Gonzales-Calero, P., Groth, P., Moody, J., Deelman, E., et al.: WINGS: Intelligent Workflow-Based Design of Computational Experiments. IEEE Intelligent Systems (2010)
Ma, J., Shaw, E., Kim, J.: Computational workflows for assessing student learning. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6095, pp. 188–197. Springer, Heidelberg (2010)
Kang, J., Kim, J., Shaw, E.: A network analysis of student groups in threaded discussions. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6095, pp. 359–361. Springer, Heidelberg (2010)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Ramage, D., Hall, D., Nallapati, R., Manning, C.D.: Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (2009)
Feng, D., Kim, J., Shaw, E., Hovy, E.: Towards Modeling Threaded Discussions using Induced Ontology Knowledge. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI 2006), pp. 1289–1294 (2006)
McLaren, B.M., Scheuer, O., De Laat, M., Hever, R., De Groot, R., Rose, C.P.: Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions. In: Proceedings of the 13th Int’l Conf. on Artificial Intelligence in Education (2007)
Reed-Rhoads, C16 – Tools for Assessing Learning in Engineering. Presentation on Inventions and Impact 2: Building Excellence in Undergraduate STEM Education (2008)
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© 2011 Springer-Verlag Berlin Heidelberg
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Ma, J., Kang, JH., Shaw, E., Kim, J. (2011). Workflow-Based Assessment of Student Online Activities with Topic and Dialogue Role Classification. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science(), vol 6738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21869-9_26
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DOI: https://doi.org/10.1007/978-3-642-21869-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21868-2
Online ISBN: 978-3-642-21869-9
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