Abstract
This paper presents a semi-supervised machine-learning approach to predicting whether students will be successful in solving problem-solving tasks within narrative-centered learning environments. Results suggest the approach often outperforms standard supervised learning methods.
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References
Min, W., Rowe, J.P., Mott, B.W., Lester, J.C.: Personalizing Embedded Assessment Sequences in Narrative-Centered Learning Environments: A Collaborative Filtering Approach. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 369–378. Springer, Heidelberg (2013)
Zhu, X., Goldberg, A.: Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine learning 3(1), 1–130 (2009)
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Min, W., Mott, B.W., Rowe, J.P., Lester, J.C. (2014). Leveraging Semi-Supervised Learning to Predict Student Problem-Solving Performance in Narrative-Centered Learning Environments. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 8474. Springer, Cham. https://doi.org/10.1007/978-3-319-07221-0_99
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DOI: https://doi.org/10.1007/978-3-319-07221-0_99
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07220-3
Online ISBN: 978-3-319-07221-0
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