Abstract
Learner behavioral data (e.g., clickstream activity logs) collected by online education platforms contains rich information about learners and content, but is often highly redundant. In this paper, we study the problem of learning low-dimensional, interpretable features from this type of raw, high-dimensional behavioral data. Based on the premise of generative adversarial networks (GANs), our method refines a small set of human-crafted features while also generating a set of additional, complementary features that better summarize the raw data. Through experimental validation on a real-world dataset that we collected from an online course, we demonstrate that our method leads to features that are both predictive of learner quiz scores and closely related to human-crafted features.
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Allen, L., Jacovina, M., Dascalu, M., Roscoe, R., Kent, K., Likens, A., McNamara, D.: ENTERing the time series SPACE: uncovering the writing process through keystroke analyses. In: Proceedings of International Conference on Educational Data Mining, pp. 22–29, June 2016
Beheshti, B., Desmarais, M., Naceur, R.: Methods to find the number of latent skills. In: Proceedings of International Conference on Educational Data Mining, pp. 81–86, June 2012
Bergner, Y., Droschler, S., Kortemeyer, G., Rayyan, S., Seaton, D., Pritchard, D.: Model-based collaborative filtering analysis of student response data: machine-learning item response theory. In: Proceedings of International Conference on Educational Data Mining, pp. 95–102, June 2012
Brinton, C., Chiang, M.: MOOC performance prediction via clickstream data and social learning networks. In: Proceedings of IEEE Conference on Computer Communications, pp. 2299–2307, April 2015
Brinton, C., Buccapatnam, S., Chiang, M., Poor, H.V.: Mining MOOC clickstreams: video-watching behavior vs. in-video quiz performance. IEEE Trans. Signal Process. 64, 3677–3692 (2016)
Chen, W., Brinton, C., Cao, D., Chiang, M.: Behavior in social learning networks: early detection for online short-courses. In: Proceedings of IEEE Conference on Computer Communications, pp. 1–9, May 2017
Gelman, B., Revelle, M., Domeniconi, C., Johri, A., Veeramachaneni, K.: Acting the same differently: a cross-course comparison of user behavior in MOOCs. In: Proceedings of International Conference on Educational Data Mining, pp. 376–381, June 2016
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning. MIT Press, Cambridge (2016)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Halawa, S., Greene, D., Mitchell, J.: Dropout prediction in MOOCs using learner activity features. In: Proceedings European MOOCs Stakeholders Summit, pp. 58–65, February 2014
Klingler, S., Wampfler, R., Kaser, T., Solenthaler, B., Gross, M.: Efficient feature embeddings for student classification with variational auto-encoders. In: Proceedings of International Conference on Educational Data Mining, pp. 72–79, June 2017
Lan, A.S., Brinton, C.G., Yang, T., Chiang, M.: Behavior-based latent variable model for learner engagement. In: Proceedings of International Conference on Educational Data Mining, pp. 64–71, June 2017
Lee, K., Chung, J., Cha, Y., Suh, C.: ML approaches for learning analytics: collaborative filtering or regression with experts?, December 2016. http://ml4ed.cc/attachments/LeeLCCS.pdf
McBroom, J., Jeffries, B., Koprinska, I., Yacef, K.: Mining behaviours of students in autograding submission system logs. In: Proceedings of International Conference on Educational Data Mining, pp. 159–166, June 2016
Slater, S., Baker, R., Ocumpaugh, J., Inventado, P., Scupelli, P., Heffernan, N.: Semantic features of math problems: relationships to student learning and engagement. In: Proceedings of International Conference on Educational Data Mining, pp. 223–230, June 2016
Tomkins, S., Ramesh, A., Getoor, L.: Predicting post-test performance from online student behavior: a high school MOOC case study. In: Proceedings of International Conference on Educational Data Mining, pp. 239–246, June 2016
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Cao, D., Lan, A.S., Chen, W., Brinton, C.G., Chiang, M. (2018). Learner Behavioral Feature Refinement and Augmentation Using GANs. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_8
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DOI: https://doi.org/10.1007/978-3-319-93846-2_8
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