Education Data Science and Learning Engineering Fellowship
What is Learning Engineering?
Check out our video.
ABOUT THE FELLOWSHIP
Educational Data Science and Learning Engineering Fellowship (the Fellowship) is an outreach program for undergraduate students who are interested in tackling cutting-edge educational problems with engineering solutions.
Supported by UC Berkeley School of Education and Schmidt Futures, the Fellowship will select and sponsor ~25-30 computer science students (or 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.
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.
By the end of the fellowship, fellows will form teams to develop a capstone project, connecting all knowledge and experience gained through these sessions.
These projects are product-oriented design sprints where teams are expected to develop a working prototype along with user research and market analysis.
All teams will give a final pitch of their capstone projects at the graduation ceremony and win a total of $50,000+ grant money as incentives to continue their development.
All fellows are eligible and encouraged to apply for ample research and internship opportunities within the learning engineering professional network, including the guest speakers.
Over $100k stipends will be offered to those who apply and get accepted as RA/intern at these professionals’ sites, along with mentorship and career guidance from these experts.
Topics and Schedule
* Due to COVID19, we have decided to extend the application deadline to December 31st, giving prospective applicants more time to cp
* New Application Deadline: Dec. 31st, 2020, 11:59pm PST
*Due to increased level of anxiety of the resurgence of COVID-19 cases and the final exams, we've extended the deadline for the application.
Q & A
5. How selective is this fellowship?
We expect this program to be HIGHLY selective for the first cohort. We would encourage students to apply early so that not to forget the deadline and write a good application.
6. How long are the fellowship program? What does the format look like, and do I have to stay in the cohort for the entire fellowship?
The fellowship will start in January, 2021 and last about 7 months until July/August. Due to COVID-19 and travel restrictions, we expect the first half of the sessions to be held entirely online via zoom or other video conferencing platforms, though the situation may change during the fellowship.
The format of the fellowship is similar to an online weekly seminar where fellows and guest speakers are gathered to share ideas on specific topics around Learning Engineering.
All fellows MUST stay in the fellowship until it is finished, except for unusual circumstances. Attendance in sessions and group projects are REQUIRED. All fellows will sign an agreement that they will not drop out before being admitted to the program, otherwise they are not admitted.
7. Is this program open for students who are not currently in the U.S.? How do these online sessions work if fellows are not in the same time zones？
This program is open for all students regardless of your presence in the United States. Once we admit all fellows for this cohort, we will try to accommodate different time zones for our guest speakers, fellows, and staff. Also, depending on the situation of the pandemic, we will make adaptions to the program along the way (including hosting in-person activities if applicable).
1. Is this program free of charge?
Yes, this program is completely free for admitted fellows. Plus, there are over $150k stipends and grants available solely for the fellows. In addition, traveling and lodging expenses will be reimbursed if there are in-person meetings and activities, such as the graduation ceremony.
2. Is this a degree program by UC Berkeley? Who is running this program, what’s the relationship with UC Berkeley and Schmidt Futures?
It’s not a degree program by UC Berkeley. It is an independent fellowship program funded by Schmidt Futures, supported and coordinated by UC Berkeley, and executed by EPIC@Berkeley, a student-run organization at UC Berkeley to raise awareness of education innovation and technology.
3. I don’t study computer science, data science, or engineering but majoring in a STEM/education major, can I apply? What is the prerequisite for programming knowledge in order to apply for this program?
This program is created for Computer Science/Data Science students who have experience in working with data/developing CS projects. Although a major/degree is not a strict requirement, you have to show prior experience and/or relevant projects/experiences in the field. The program is technical in nature and expects fellows to be fluid in at least one major programming language such as python or R. Completing an intermediary programming course should serve the minimum requirement to be considered as a fellow.
4. I will be graduating in Spring 2021. Am I eligible for the fellowship?
Yes, all years in an undergraudate program are welcomed. Juniors and seniors who have experience in data science/engineering are highly recommended. Freshmen and sophomores with exceptional achievement and experiences are also considered.