Abstract
We propose a machine learning approach to automate the estimation of the interpersonal help-seeking level of students in an online course, based on their behavior in an LMS platform. We selected behavioral and performance features from the LMS logs, using forum and wiki variables in the context of a professional development course in audiology rehabilitation (N = 93). Then, we applied different state-of-the-art regression algorithms to predict their responses, using student-level cross-validation in the training set and evaluated the resulting models in a separate test set. As result, we had approximately an error of one point with our model, on average. We discuss some deviant cases and how this information can be used to inform tutors in online courses.
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Acknowledgments
The ‘Auditory Rehabilitation in Children’ course was funded by the Brazilian Ministry of Health - Support Program for Institutional Development of the National Health System (Proadi/SUS - Grant 25000.024953/2015-89). The authors also thanks CNPq (Grant 307887/2017-0), CAPES and FAPESP (Grant15/24507-2) for the funding support.
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Penteado, B.E., Isotani, S., Paiva, P.M., Morettin-Zupelari, M., Ferrari, D.V. (2018). Prediction of Interpersonal Help-Seeking Behavior from Log Files in an In-Service Education Distance Course. 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_49
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DOI: https://doi.org/10.1007/978-3-319-93846-2_49
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