Abstract
This paper describes our experiments and analysis of utilizing class-level features to predict student performance for retention tests. There are two aspects that make this paper interesting. First, instead of focusing on short-team performance, we investigated student performance after a delay of at least 7 days. Second, we explored several class-level features that can be captured in intelligent tutoring systems (ITS), and we showed that some of them have encouraging predictive power. With the help of class-level features, the prediction result indicated an improvement from an R² of 0.183 with a normal feature set to an R² value of 0.224.
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References
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Xiong, X., Beck, J.E., Li, S. (2013). Class Distinctions: Leveraging Class-Level Features to Predict Student Retention Performance. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_121
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DOI: https://doi.org/10.1007/978-3-642-39112-5_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39111-8
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