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Microgenetic Learning Analytics Methods: Hands-on Sequence Analysis Workshop Report
Author(s) -
Ani Aghababyan,
Taylor Martin,
Phillip Janisiewicz,
Kevin Close
Publication year - 2016
Publication title -
journal of learning analytics
Language(s) - English
Resource type - Journals
ISSN - 1929-7750
DOI - 10.18608/jla.2016.33.6
Subject(s) - learning analytics , toolbox , computer science , data science , analytics , data analysis , software analytics , scope (computer science) , cluster analysis , software , human–computer interaction , artificial intelligence , data mining , software development , software construction , programming language
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.

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