Abstract
Researchers engaged in the scholarship of teaching and learning seek tools for rigorous, quantitative analysis. Here we present a brief introduction to computational techniques for the researcher with interest in analyzing data pertaining to pedagogical study. Sample dataset and fully executable code in the open-source R programming language are provided, along with illustrative vignettes relevant to common forms of inquiry in the educational setting.
Highlights
The scholarship of teaching and learning (SoTL), i.e., research as applied in a pedagogical setting (Witman and Richlin, 2007; Kanuka, 2011), has become increasingly prominent over the past 20 years (Boyer, 1997; Gilpin and Liston, 2009; Hutchings et al, 2011; Bishop-Clark and Dietz-Uhler, 2012)
R is derived from the S programming language, developed at Bell Labs in the 1970s, designed to provide a variety of statistical and graphical techniques, with an intentionally extensionable design, meaning that users can devise and publish code add-ons to enhance base-R’s functionality
R is regularly ranked in the top-20 of the TIOBE index of programming language popularity and as of March, 2018 is the highest-ranked software with focal application to statistical analysis
Summary
Department of Rehabilitation Sciences, University of Hartford, West Hartford, CT, United States, 2 Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States, 3 Cooperative Studies Program, Department of Veterans Affairs, West Haven, CT, United States Researchers engaged in the scholarship of teaching and learning seek tools for rigorous, quantitative analysis. Here we present a brief introduction to computational techniques for the researcher with interest in analyzing data pertaining to pedagogical study. Sample dataset and fully executable code in the open-source R programming language are provided, along with illustrative vignettes relevant to common forms of inquiry in the educational setting. Keywords: education, code, numerical, analysis, statistics, programming, measurement, SoTL Reviewed by: Isabel Menezes, Universidade do Porto, Portugal Vincent Anthony Knight, Cardiff University, United Kingdom Specialty section: This article was submitted to Digital Education, a section of the journal Frontiers in Education Citation: Prokop TR and Wininger M (2018) A Primer on R for Numerical Analysis in Educational Research. Front. Educ. 3:80.
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