Automating grading workflow with the gradetools package in R

Mine Dogucu^1,

¹ Department of Statistics, University of California Irvine

The Package

Developed by Federica Zoe Ricci, Catalina Medina, and Mine Dogucu (2022).
Not on CRAN but can be installed using devtools::install_github("federicazoe/gradetools")

Pedagogical Importance

Using assessments to improve and evaluate student learning is one of the six recommendations of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) college report (Carver et al. 2016).

As data science and statistics classes are getting larger, there is a bigger challenge to assess students’ learning and provide them meaningful feedback on open-ended assignments such as lab reports (Carver et al. 2016), semester long projects (Cetinkaya-Rundel, Dogucu, and Rummerfield 2022), and writing assignments (Woodard, Lee, and Woodard 2020).

The gradetools package aims to automate the grading workflow while not automating the grading. We do this by reducing grading time spent on repetitive administrative tasks while allowing more time for pedagogical tasks. We provide a few examples of administrative and pedagogical tasks below.

Administrative tasks

Opening and closing files
Finding the corresponding student on the grade sheet
Entering and storing grades to the grades sheet

Pedagogical Tasks

Evaluating students’ work
Providing feedback
Assigning a score

How to Use gradetools

We provide vignettes that provide a step-by-step guide to using gradetools. Each of the vignettes serves a different purpose.

Functions

assist_grading()
assist_regrading()
assist_team_grading()

Features

Figure 1: An overview of the grading process using gradetools

Example Use

Figure 2: An example rubric setup

Figure 3: A screenshot from a hypothetical grading process

Answers to FAQ

Any file type which opens in RStudio (e.g. Quarto, Markdown, R script, Python script) can be opened using gradetools.
There is support for assignments completed in teams.
The gradetools package needs to be supported by other packages such as rcanvas (2017), moodleR (Dietrichson 2022), ghclass (Rundel and Cetinkaya-Rundel 2022) for a fully-automated grading workflow.
We have developed and used gradetools as part of our courses such as introdata.science. We utilize both gradetools and ghclass for a fully-automated grading workflow.

Acknowledgments

Medina and Dogucu were supported by NSF award IIS #2123366. Ricci was supported by HPI Research Center in Machine Learning and Data Science at UC Irvine.

Poster Access

This poster can be accessed via mdogucu.github.io/gradetools-jsm2023/

References

Carver, Robert, Michelle Everson, John Gabrosek, Nicholas Horton, Robin Lock, Megan Mocko, Allan Rossman, et al. 2016. “Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report 2016.”

Cetinkaya-Rundel, Mine, Mine Dogucu, and Wendy Rummerfield. 2022. “The 5Ws and 1H of Term Projects in the Introductory Data Science Classroom.” Statistics Education Research Journal.

Dietrichson, Aleksander. 2022. moodleR: Helper Functions to Work with ’Moodle’ Data. https://cran.r-project.org/web/packages/moodleR/index.html.

Ranzolin, David, Chris Hua, Frederick Solt, Wouter van Atteveldt, and J. Hathaway. 2017. rcanvas: R Client for Canvas API. https://github.com/daranzolin/rcanvas/tree/master.

Ricci, Federica Zoe, Catalina Medina, and Mine Dogucu. 2022. Gradetools: Tools to Assist with Providing Grades and Personalized Feedback to Students. https://github.com/federicazoe/gradetools.

Rundel, Colin, and Mine Cetinkaya-Rundel. 2022. ghclass: Tools for Managing Classes on GitHub. https://cran.r-project.org/web/packages/ghclass/index.html.

Woodard, Victoria, Hollylynne Lee, and Roger Woodard. 2020. “Writing Assignments to Assess Statistical Thinking.” Journal of Statistics Education 28 (1): 32–44.