Opinionated Practices for Teaching Reproducibility: Motivation, Guided Instruction and Practice

Autor/inn/en	Ostblom, Joel; Timbers, Tiffany
Titel	Opinionated Practices for Teaching Reproducibility: Motivation, Guided Instruction and Practice
Quelle	In: Journal of Statistics and Data Science Education, 30 (2022) 3, S.241-250 (10 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Ostblom, Joel)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1080/26939169.2022.2074922
Schlagwörter	Statistics Education; Data Science; Teaching Methods; Replication (Evaluation); College Students; Foreign Countries; Documentation; Student Motivation; Motivation Techniques; Teacher Guidance; Canada + Suchen Sie Ihr Suchwort? Teaching method; Lehrmethode; Unterrichtsmethode; Collegestudent; Ausland; Dokumentation; Schulische Motivation; Motivationsförderung; Lehrerberatung; Kanada
Abstract	In the data science courses at the University of British Columbia, we define data science as the study, development and practice of reproducible and auditable processes to obtain insight from data. While reproducibility is core to our definition, most data science learners enter the field with other aspects of data science in mind, for example predictive modeling, which is often one of the most interesting topics to novices. This fact, along with the highly technical nature of the industry standard reproducibility tools currently employed in data science, present out-of-the gate challenges in teaching reproducibility in the data science classroom. Put simply, students are not as intrinsically motivated to learn this topic, and it is not an easy one for them to learn. What can a data science educator do? Over several iterations of teaching courses focused on reproducible data science tools and workflows, we have found that providing extra motivation, guided instruction and lots of practice are key to effectively teaching this challenging, yet important subject. Here we present examples of how we motivate, guide, and provide ample practice opportunities to data science students to effectively engage them in learning about this topic. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01