Abstract
This paper proposes a prediction framework for student performance based on comment data mining. Given the comments containing multiple topics, we seek to discover the topics that help to predict final student grades as their performance. To this end, the paper proposes methods that analyze students’ comments by two topic models: Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA). The methods employ Support Vector Machine (SVM) to generate prediction models of final student grades. In addition, Considering the student grades predicted in a range of lessons can deal with prediction error occurred in each lesson, and achieve further improvement of the student grade prediction.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Goda, K., Mine, T.: Analysis of Students’ Learning Activities through Quantifying Time-Series Comments. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part II. LNCS, vol. 6882, pp. 154–164. Springer, Heidelberg (2011)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42, 177–196 (2001)
Qualters, D.M.: Using classroom assessment data to improve student learning. Center for Effective University Teaching & GE Master Teacher’s Team, Northeatern University (2001)
Sorour, S.E., Mine, T., Goda, K., Hirokawa, S.: Comment data mining for student grade prediction considering differences in data for two classes. International Journal of Computer & Information Science 15(2), 12–25 (2014)
Steyvers, T.L.G.M.: Finding scientific topics. Proceedings of the National Academy of Sciences 101, 5228–5235 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Sorour, S.E., Goda, K., Mine, T. (2015). Student Performance Estimation Based on Topic Models Considering a Range of Lessons. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_117
Download citation
DOI: https://doi.org/10.1007/978-3-319-19773-9_117
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19772-2
Online ISBN: 978-3-319-19773-9
eBook Packages: Computer ScienceComputer Science (R0)