SPUR (2020) 4 (1): https://doi.org/10.18833/spur/4/1/5 Abstract:
Data science methods increasingly are utilized to analyze theoretically derived psychological research questions. This article provides a case study of a student-focused research experience that introduced basic data science skills and their utility for psychological research, providing practical learning experiences for students interested in learning computational social science skills. Skills included programming; acquiring, visualizing, and managing data; performing specialized analyses; and building knowledge about open-science practices. Using examples from their teaching experiences, the authors describe how these skills can be incorporated into an active and engaging student learning experience that culminates in computational social science projects and presentations.
More Articles in this Issue
- ‐ Matt Honoré, Thomas E. Keller, Jen Lindwall, Rachel Crist, Leslie Bienen, and Adrienne Zell
SPUR (2020) 4 (1): https://doi.org/10.18833/spur/4/1/3 Abstract:
The authors developed a novel tool, the CREDIT URE,to define and measure roles performed by undergraduate students working in research placements. Derived
from an open-source taxonomy for determining authorship credit, the CREDIT URE defines 14 possible roles, allowing students and their research mentors to rate the degree to which students participate in each role. The tool was administered longitudinally across three cohorts of undergraduate student-mentor pairs involved in a biomedical research training program for students from diverse backgrounds. Students engaged most frequently in roles involving data curation, investigation, and writing. Less frequently, students engaged in roles related to software development, supervision, and funding acquisition. Students’ roles changed over time as they gained experience. Agreement between students and mentors about responsibility for roles was high.