Open-source Tools for Training Resources – OTTR. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Savonen, Candace; Wright, Carrie; Hoffman, Ava M.; Muschelli, John; Cox, Katherine; Tan, Frederick J.; Leek, Jeffrey T.
• Is Part Of: Journal of Statistics and Data Science Education. Volume 31:Issue 1(2023)
• Page Start: 57
• Page End: 65
• Publication Date: 2023-01-02
• Subjects: Data science -- Education -- Informatics -- Open-source -- Tools
• DOI: 10.1080/26939169.2022.2118646
• Languages: English
• ISSNs: 2693-9169

Abstract

Abstract: Data science and informatics tools are developing at a blistering rate, but their users often lack the educational background or resources to efficiently apply the methods to their research. Training resources and vignettes that accompany these tools often deprecate because their maintenance is not prioritized by funding, giving teams little time to devote to such endeavors. Our group has developed Open-source Tools for Training Resources (OTTR) to offer greater efficiency and flexibility for creating and maintaining these training resources. OTTR empowers creators to customize their work and allows for a simple workflow to publish using multiple platforms. OTTR allows content creators to publish training material to multiple massive online learner communities using familiar rendering mechanics. OTTR allows the incorporation of pedagogical practices like formative and summative assessments in the form of multiple choice questions and fill in the blank problems that are automatically graded. No local installation of any software is required to begin creating content with OTTR. Thus far, 15 training courses have been created with OTTR repository template. By using the OTTR system, the maintenance workload for updating these courses across platforms has been drastically reduced. For more information about OTTR and how to get started, go to ottrproject.org. Supplementary materials for this article are available online.