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Rainy Wortelboer
Rainy Wortelboer
Faculty Collaborator: Karianne Bergen
About:
Rainy has been working with Professor Karianne Bergen to develop a new DATA First Year Seminar (FYS) course at Brown University. Currently, there are no first-year seminars in DATA, MATH, APMA, or CSCI at the institution. This semester, their focus was to outline the course structure and potential modules. With feedback from Brown students and resources from data science professors at peer institutions, they finalized the focus of the DATA FYS course: students will explore various forms and roles of data science, investigate its historical context and ethical implications, and practice communicating data effectively. They drafted modules for the course, including readings, discussion questions, assignments, and active learning activities. Specifically, they created a scaffolded Colab notebook to teach the fundamentals of data analysis and designed assignments for a potential capstone project. In this capstone project, students will apply data science to their fields of interest by analyzing existing datasets or creating their own, including an ethical analysis and visualizations.
Transcript
Transcript
Project Goals
Current Situation: No FYS in DATA, MATH, APMA, CSCI.
Goal: Design a FYS in DATA.
Outline course structure.
Create potential modules.
Ideal Course:
Should not be tied to a specific domain.
Encourage students to see data everywhere and apply it to their perspectives and interests.
Market Research: Survey
Survey: First Year Seminar Experience Survey.
Likes:
Small discussion format.
Active learning.
Wishes:
More diverse topics.
Interactive components.
Feedback:
"Not learning a new topic but learning a new way of thinking."
Market Research: Existing Courses
Brown University FYS:
Deep dive or overview.
Mostly humanities-based.
Data Science Courses at Peer Institutions:
CS 39: Technology Society and Power (UC Berkeley)
FS 154: Weird Data (Princeton)
Decision Points: FYS
FYS Characteristics:
Transition to college.
Learn how to learn.
Discovery and action.
Module: Transition to College.
Final Project: Group work applying learned concepts to their fields of interest.
Decision Points: Learners
Diverse Learners:
Apply data science to fields of interest.
No prerequisites.
Module: Origins of Data Science.
Accomplishments: Finalized Focus
Course Description:
Explore fundamentals of data science.
Critically examine its history.
Communicate data effectively.
Hands-on application to students’ fields of interest.
No coding experience necessary.
Accomplishments: Modules
Drafted Modules:
Transition to College
What is Data?
Origins of Data Science
Data Literacy
Where Does Data Come From?
Data Curation & Analysis
Data Science Resources
Applying Data Science
Accomplishments: Major Assignments
Data Diary:
Students keep a Google Sheets data diary.
Analyze recorded data during the curation/analysis module.
Scaffolded Colab Notebook:
Teach fundamentals of data analysis using recorded data.
Accomplishments: Final Group Project
Options:
Analyze existing datasets.
Create and analyze their own data.
Scaffolded Assignments:
Propose topic.
Proof of exploration.
Methodology.
Presentation & Paper.
Reflection
Skills Learned:
Project management.
Learner-centered course design.
Active learning activities.
Low stakes assessments.
Finding and using my voice in meetings.
Future Work
Tasks:
Evaluate and find more readings.
Draft syllabus.
Course proposal deadline: March 8th.
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