crsra: A package for cleaning and analyzing coursera research export data

Aboozar Hadavand, Jeffrey Leek

Research output: Contribution to journalArticlepeer-review

Abstract

Due to the fundamental differences between traditional education and Massive Open Online Courses (MOOCs) and the ever-increasing popularity of MOOCs more research is needed to understand current and future trends in education. Although research in the field has rapidly grown in recent years, one of the main challenges facing researchers remains to be the complexity and messiness of the data. Therefore, it is imperative to provide tools that pave the way for more research on the new subject of MOOCs. This paper introduces a package called crsra based on the statistical software R to help clean and analyze massive loads of data provided by Coursera. The advantages of the package are as follows: a) faster loading and organizing data for analysis, b) an efficient method for combining data from multiple courses and even across institutions, and c) provision of a set of functions for analyzing student behaviors.

Original languageEnglish (US)
JournalUnknown Journal
DOIs
StatePublished - Mar 5 2018

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Immunology and Microbiology(all)
  • Neuroscience(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

Fingerprint Dive into the research topics of 'crsra: A package for cleaning and analyzing coursera research export data'. Together they form a unique fingerprint.

Cite this