Article Type

Full-Length Paper

Publication Date





Objective: This paper describes a project to revise an existing research data management (RDM) course to include instruction in computer skills with robust data science tools.

Setting: A Carnegie R1 university.

Brief Description: Graduate student researchers need training in the basic concepts of RDM. However, they generally lack experience with robust data science tools to implement these concepts holistically. Two library instructors fundamentally redesigned an existing research RDM course to include instruction with such tools. The course was divided into lecture and lab sections to facilitate the increased instructional burden. Learning objectives and assessments were designed at a higher order to allow students to demonstrate that they not only understood course concepts but could use their computer skills to implement these concepts.

Results: Twelve students completed the first iteration of the course. Feedback from these students was very positive, and they appreciated the combination of theoretical concepts, computer skills and hands-on activities. Based on student feedback, future iterations of the course will include more “flipped” content including video lectures and interactive computer tutorials to maximize active learning time in both lecture and lab.

The substance of this article is based upon poster presentations at RDAP Summit 2018.


data information literacy, flipped instruction, backwards design, active learning, graduate education, STEM education, research data management, data science, R, RStudio, Excel, Unix


The authors would like to acknowledge Jake Carlson and Marianne Stowell Bracke, who developed an earlier iteration of this course in Purdue’s College of Agriculture and who left the authors in charge of the earlier course, giving us a chance to iterate. The authors would also like to acknowledge Nastasha Johnson for assistance in developing the LIBR pilot of the course and Noel Diaz and Stephan Miller for developing and supporting the technical infrastructure for the course.

Corresponding Author

Pete Pascuzzi, PhD, Purdue University, 3053 WALC, 304 Centennial Mall Drive, West Lafayette, IN 47907, 765-494-2871; ppascuzz@purdue.edu

Rights and Permissions

Copyright Pascuzzi & Sapp Nelson © 2018

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Supplement1_1152.pdf (814 kB)
Supplemental File 1: Sample Lab Instruction (.pdf printout)

Supplement2_1152.pdf (505 kB)
Supplemental File 2: Questions from post-course evaluation

compMapping.tif (1054 kB)
Figure 1: Select Data Information Literacy Competencies Mapped to Computer Skills