Abstract
New statistical methods allow discovery of causal models purely from observational data in some circumstances. Educational research that does not easily lend itself to experimental investigation can benefit from such discovery, particularly when the process of inquiry potentially affects measurement. Whether controlled or authentic, educational inquiry is sprinkled with hidden variables that only change over the long term, making them challenging and expensive to investigate experimentally. Big data learning analytics offers methods and techniques to observe such changes over longer terms at various levels of granularity. Learning analytics also allows construction of candidate models that expound hidden variables as well as their relationships with other variables of interest in the research. This article discusses the core ideas of causality and modeling of causality in the context of educational research with big data analytics as the underlying data supply mechanism. It provides results from studies that illustrate the need for causal modeling and how learning analytics could enhance the accuracy of causal models.
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
References
Bandura, A., & Locke, E. (2003). Negative self-efficacy and goal effects revisited. Journal of Applied Psychology, 88, 87–99.
Boekaerts, M., & Corno, L. (2005). Self-regulation in the classroom: A perspective on assessment and intervention. Applied Psychology An International Review, 54(2), 199–231.
Brokenshire, D., & Kumar, V. (2009). Learning models of self-regulated learning, Proceedings of the International Conference on Artificial Intelligence in Education (AIED 09) (pp 257–264).
Chickering, D. M. (2003). Optimal structure identification with greedy search. Journal of Machine Learning Research, 507–554.
Green, J., & Azevedo, R. (2007). A theoretical review of winne and hadwin’s model of self-regulated learning: New perspectives and directions. Review of Educational Research, 77, 334–372.
MacCallum, R., & Austin, J. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201–226.
Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
Pearl, J. (2003). Statistics and causal inference: A review. Sociedad de Estadistica e Investigacion Opevativa Test, 12, 281–345.
Pintrich, P. (2000). Handbook of self-regulation, Chapter The Role of Goal Orientation in Self-Regulated Learning, pp. 452–502.
Puustinen, M., & Pulkkinen, L. (2001). Models of self-regulated learning: a review. Scandanavian Journal of Educational Research, 45, 269–286.
Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., & Carlstrom, A. (2004). Do psychosocial and study skill factors predict college outcomes? A metaanalysis. Psychological Bulletin, 130, 261–288.
Römer, U., & Wulff, S. (2010). Applying corpus methods to writing research: Explorations of MICUSP. Journal of Writing Research, 2(2), 99–127.
Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search (2nd ed.). Cambridge: The MIT Press.
Winne, P. H., & Hadwin, A. F. (1998). Metacognition in educational theory and practice, chapter Studying as self-regulated learning (pp. 277–304). Lawrence Erlbaum.
Zhang J, Spirtes P (2005) A transformational characterization of markov equivalence for directed acyclic graphs with latent variables, Proceedings of the twenty-first conference on Uncertainty in Artificial Intelligence (UAI2005) (pp. 667–674).
Zhang, J., & Spirtes, P. (2008). Detection of unfaithfulness and robust causal inference. Minds and Machines, 18, 239–271.
Zimmerman, B. J. (Eds.). (2001). Self-Regulated learning and academic achievement: Theoretical Perspectives. Mahwah, NJ: Lawrence Erlbaum Associates.
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41, 64–71.
Zimmerman, B. J., & Bandura, A. (1994). Impact of self-regulatory influences on writing course attainment. American Educational Research Journal, 31, 845–869.
Acknowledgments
This research was funded in part by NSERC/iCORE/Xerox/Markin Research Chair for Adaptivity and Personalization in Informatics, NSERC Discovery Grants, and SSHRC INE (LearningKit). The section on Metacognitive Analytics expands on findings from a Masters Thesis by David Brokenshire, supervised by Drs Vive Kumar and Marek Hatala, at Simon Fraser University, Canada.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kumar, V.S., Kinshuk, Clemens, C., Harris, S. (2015). Causal Models and Big Data Learning Analytics. In: Kinshuk, ., Huang, R. (eds) Ubiquitous Learning Environments and Technologies. Lecture Notes in Educational Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44659-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-662-44659-1_3
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44658-4
Online ISBN: 978-3-662-44659-1
eBook Packages: Humanities, Social Sciences and LawEducation (R0)