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ERIC Number: EJ1126794
Record Type: Journal
Publication Date: 2016
Pages: 19
Abstractor: As Provided
ISSN: EISSN-1929-7750
Microgenetic Learning Analytics Methods: Workshop Report
Aghababyan, Ani; Martin, Taylor; Janisiewicz, Philip; Close, Kevin
Journal of Learning Analytics, v3 n3 p96-114 2016
Learning analytics is an emerging discipline and, as such, benefits from new tools and methodological approaches. This work reviews and summarizes our workshop on microgenetic data analysis techniques using R, held at the second annual Learning Analytics Summer Institute in Cambridge, Massachusetts, on 30 June 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 behavioural 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.
Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Descriptive
Education Level: Elementary Education
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: EEC1329438; DRL1338176