I’m excited to be launching a microcredential (a non-credit bearing credential granted by the University) in educational data analytics with my colleague at UTK, Louis Rocconi. A nice description of microcredentials is available here; in brief:
A micro-credential is a short, competency-based recognition that allows an educator to demonstrate mastery in a particular area.
These don’t just apply to educators; a common microcredential in the data science world is the Data Science Specialization offered through Johns Hopkins University/Coursera; many data scientists got their start with the statistical software and programming language R through these (awesome) courses that lead to a non-credit bearing certificate.
We had the idea for this and pitched it for our College’s program that allowed faculty to request funds for projects that support the College’s Strategic Vision and Diversity Action Plan. Here is what we proposed:
The proposed project is the development of a microcredential offered through the College of Education, Health, and Human Sciences (CEHHS) in data science and statistics. The microcredential will be entitled “Getting Started in Educational Data Analytics Using R”. The audience for the microcredential will be educational leaders and analysts, graduate students in CEHHS and across the University of Tennessee, Knoxville (UTK) looking to get started with R, and individuals from the wider community seeking out a gentle introduction to analytics, R, and data analytics.
This microcredential is distinguished from existing offerings by its focus on the widely-used statistical software and programming language R. Commonly used in the life, physical, and social sciences because of how it enables a range of analyses and data visualizations, the use of R is less established in education. We tailor the contents of the microcredential using problems and data sources familiar to educators and educational researchers. We also assume no prior knowledge of programming or statistics. The specific contents of the microcredential are five-fold: 1) understanding the role of R and RStudio, 2) setting up and using RStudio, 3) using base R functions (functions built-in to R) 4) using tidyverse (an add-on to R that makes it more accessible and powerful) functions, and 5) carrying out exploratory data analysis.
The primary work supported through the requested funds will involve creating and designing five online modules that guide participants through the microcredential. Participants will be able to complete the microcrediental at their own pace. Louis and Josh will develop the content for each module based on existing materials from courses they teach and new material—namely, code-along videos and formative assessments built-in to RStudio. For example, the module on base R foundations will include topics such as the role of add-ons to R (packages); loading data into R; data frames; and manipulating data frames.
This will be the first in what we hope will be a series of microcredentials focused on data science and statistics; future microcredentials can build on this initial offering and can support recruitment efforts and the pursuit of external funding through agencies such as the National Science Foundation and the Institute for Education Sciences. Future credentials include data science topics such as Wrangling Data With R, Data Visualization for Publications and Presentations Using ggplot2, and Using RMarkdown Documents for Reporting, and specific statistical and computational methods such as Exploring Bivariate Relationships, Introduction to Regression Models, and Social Network Analysis.
Our goal is for the microcredential to be available September, 2022. Thanks to Louis for being a great collaborator on this and to our College and Department for supporting it.