Microgenetic Learning Analytics Methods: Hands-on Sequence Analysis Workshop Report

Ani Aghababyan
Taylor Martin
Phillip Janisiewicz
Kevin Close

Abstract


Learning analytics is an emerging discipline and, as such, it benefits from new tools and methodological approaches.  This work reviews and summarizes our workshop on microgenetic data analysis techniques using R, held at the 2nd annual Learning Analytics Summer Institute in Cambridge, Massachusetts on June 30th, 2014. Specifically, this paper introduces educational researchers to our experience using data analysis techniques with the RStudio development environment to analyze temporal records of 52 elementary students’ affective and behavioral responses to a digital learning environment. In the RStudio development environment, we used methods such as hierarchical clustering and sequential pattern mining. We also used RStudio to create effective data visualizations of our complex data. The scope of the workshop, and this paper, assumes little prior knowledge of the R programming language, and thus covers everything from data import and cleanup to advanced microgenetic analysis techniques. Additionally, readers will be introduced to software setup, R data types, and visualizations. This paper not only adds to the toolbox for learning analytics researchers (particularly when analyzing time series data), but also shares our experience interpreting a unique and complex dataset.


Keywords


Microgenetic analysis; R; RStudio; learning analytics; data mining; hierarchical clustering; sequential pattern mining

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References


Abbott, A. & Tsay, A. (2000) Sequence Analysis and Optimal Matching Methods in Sociology: Review and Prospect Sociological Methods & Research, Vol. 29, 3-33.

Berland, M., Martin, T., Benton, T., Petrick Smith, C., & Davis, D. (2013). Using Learning Analytics to Understand the Learning Pathways of Novice Programmers. Journal of the Learning Sciences, 22(4), 564–599.

Blöte, A. W., Resing, W., Mazer, P., & Van Noort, D. A. (1999). Young children's organizational strategies on a same–different task: A microgenetic study and a training study. Journal of Experimental Child Psychology, 74(1), 21-43.

Buchta, C., Hahsler, M., Buchta, M. C., & Matrix, I. (2007). The arulesSequences Package.

Fazio, L. K., & Siegler, R. S. (2013). Microgenetic learning analysis: A distinction without a difference. Human Development, 56(1), 52-58.

Flavell, J. H., & Draguns, J. (1957). A microgenetic approach to perception and thought. Psychological Bulletin, 54(3), 197.

Gabadinho, A., Ritschard, G., Müller, N. S., Studer, M. (2011). Analyzing and Visualizing State Sequences in R with TraMineR. Journal of Statistical Software, 40(4), 1-37. URL http://www.jstatsoft.org/v40/i04/.

Granott, N. (1998). A paradigm shift in the study of development: Essay review of Emerging Minds by RS Siegler. Human Development, 41(5-6), 360-365.

Kuhn, D., Goh, W., Iordanou, K., & Shaenfield, D. (2008). Arguing on the Computer: A Microgenetic Study of Developing Argument Skills in a Computer‐Supported Environment. Child Development, 79(5), 1310-1328.

Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K.(2015). cluster: Cluster Analysis Basics and Extensions. R package version 2.0.3.

Neuwirth, E., & Neuwirth, M. E. (2007). The RColorBrewer Package.

Opfer, J. E., & Thompson, C. A. (2008). The trouble with transfer: Insights from microgenetic changes in the representation of numerical magnitude. Child Development, 79(3), 788-804.

Schlagmüller, M., & Schneider, W. (2002). The development of organizational strategies in children: Evidence from a microgenetic longitudinal study. Journal of Experimental Child Psychology, 81(3), 298-319.

Siegler, R.S. and Jenkins, E. (1989) How Children Discover New Strategies. Hillsdale, N.J.: Erlbaum

Siegler, R. S., Thompson, C. A., & Schneider, M. (2011). An integrated theory of whole number and fractions development. Cognitive psychology, 62(4), 273-296.

Tunteler, E., Pronk, C. M., & Resing, W. (2008). Inter-and intra-individual variability in the process of change in the use of analogical strategies to solve geometric tasks in children: A microgenetic analysis. Learning and Individual Differences, 18(1), 44-60.

Ward, J. H., Jr. (1963), "Hierarchical Grouping to Optimize an Objective Function", Journal of the American Statistical Association, 58, 236–244.

Zaki., M. J. (2001). SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine

Learning Journal, 42, 31–60




DOI: http://dx.doi.org/10.18608/jla.2016.33.6

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