Data Analysis: Unpacking Understanding of Students’ Teaming Challenges
Sara Beckman, Senior Lecturer
Business, Haas School
Closed. This professor is continuing with Spring 2024 apprentices on this project; no new apprentices needed for Fall 2024.
Solutions to today’s increasingly “wicked problems” demand teaming across disciplinary boundaries. Teaming in organizations is the “engine of organizational learning…a way of working that brings people together to generate new ideas, find answers and solve problems. But people have to learn to team; it doesn’t come naturally.” (Amy Edmondson Harvard Business School)
Good teaming skills allow teams to leverage diversity to better frame and solve problems and ultimately innovate better. Diverse teams have been shown to either significantly under or out-perform more homogeneous teams. They underperform when they are either blind to the diversity present or treat the diversity with stereotypes. They outperform when they have a learning perspective that allows them to leverage the multiple bodies of knowledge that become available when they take this perspective.
Through this research project, we aim to create a robust curriculum and toolkit for students to be able to learn better about teaming, a skill that is required in any future work they will do. This project entails analyzing the data produced by the teaming questionnaires to discern the core problems that students experience on projects and then creating a user-friendly toolkit for them to use to improve their teaming capabilities and experiences.
This work aims to address the following research questions:
What are the key success factors for making a good team experience on student projects?
How might teaming tools be best provided to students to improve their immediate teaming experiences?
How might a teaming toolkit be best leveraged over time to develop teaming capabilities?
Role: We seek URAP candidates who are comfortable with ambiguity and interested in exploration. We expect to engage in the following activity in the fall:
Data Analysis: Analysis of open-ended responses and Likert-scale questions to peer evaluation surveys. For this work, we need people who are familiar with descriptive statistics, Natural Language Processing, thematic coding, and building Neural Nets and know how to use that knowledge to unpack quantitative and qualitative data.
Qualifications: Qualifications: All URAPs will be expected to be highly motivated, organized, and self-directed. Experience with quantitative and qualitative research analysis. Comfort with ambiguity, curiosity to explore, and interest in learning about teaming and the effects of diversity. Ideally, you will have taken CS 61B and Data 100 or an equivalent.
Day-to-day supervisor for this project: Krina Patel, Graduate Student
Hours: to be negotiated
Off-Campus Research Site: remote
Related website: https://www.teamingxdesign.com/
Social Sciences