ABOUT THE FELLOWSHIP:
UC Berkeley’s Educational Data Science and Learning Engineering Fellowship is an outreach program for undergraduate students who are interested in tackling cutting-edge educational problems with engineering solutions. Supported by Professor Zachary Pardos' Computational Approaches to Human Learning Lab and Schmidt Futures, the Fellowship will select and sponsor ~25-30 students in data science or computationally related fields to participate in monthly, weekend-long online sessions with leading experts and researchers in the field of educational data science and learning engineering for 7 months.
GOAL OF THE FELLOWSHIP:
- engage undergraduate CS talents with the latest innovations and puzzles in the field
- nurture their interest and reveal promising career paths in the learning engineering industry and academia, and
- connect a group of like-minded doers who want to make a real impact in education.
COHORT-BASED EXPERIENTIAL LEARNING
There are in total 6 cohort-based, experiential learning sessions led by leading researchers and practitioners in education data science and learning engineering every month throughout the fellowship period.
The value proposition of the cohort model is to maximize the relationship building among experts and fellows and ensure full exposure to learning engineering topics and platforms with hands-on experience. Extended workshops and other hands-on activities, followed by expert presentations, allow fellows to directly apply their knowledge into small projects.
- Equitable and ethical AI
- Education data science & recommender systems
- Natural language processing, cognitive learning sciences, collaborative learning
- Human-computer interaction
- Intelligent tutoring systems and cognitive tutor
- Entrepreneurial landscape and ecosystem in edtech
The Fellowship program is being coordinated by the EPIC student organization.
For more information, please visit: https://www.epicberkeley.org/fellowship
Application link: https://docs.google.com/forms/d/e/1FAIpQLSd4y3eksC_nzRVPyauOV3jcTRPqdX2D...
Deadline: December 31st, 2020