Abstract
This paper reports a hybrid method for data-driven instructional design, a method that combines Principle Components Analysis (PCA), Hidden Markov Models (HMM), and Item Response Theory (IRT). PCA is used to identify instructional objectives as well as potential student states, HMMs are used to identify dynamics between states, and IRT is used to construct measurements of state. We report on the architecture of the system along with preliminary results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baum, L.E., Eagon, J.A.: An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology. Bulletin of the American Mathematical Society 73, 360 (1967)
Birnbaum, A.: Some latent trait models and their use in inferring an examinee’s ability. In: Lord, F.M., Novick, M.R. (eds.) Statistical Theories of Mental Test Scores. Addison–Wesley, Reading (1968)
Cooke, N.M., Schvaneveldt, R.W.: Effects of computer programming experience on network representations of abstract programming concepts. International Journal of Man-Machine Studies 29, 407–427 (1988)
Ohlsson, S.: Deep learning: How the mind overrides experience. Cambridge University Press, Cambridge (2011)
Shanteau, J.: Competence in experts: The role of task characteristics. Organizational Behavior and Human Decision Processes 53, 252–266 (1992)
VanLehn, K., Lynch, C., Schulze, K., Shapiro, J.A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., Wintersgill, M.: The Andes Physics Tutoring System: Lessons Learned. International Journal of Artificial Intelligence and Education 15, 147–204 (2005)
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Carlin, A., Dumond, D., Freeman, J., Dean, C. (2013). Higher Automated Learning through Principal Component Analysis and Markov Models. 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_83
Download citation
DOI: https://doi.org/10.1007/978-3-642-39112-5_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39111-8
Online ISBN: 978-3-642-39112-5
eBook Packages: Computer ScienceComputer Science (R0)