Learning Lab is no longer accepting Statements of Intent for the Data Science Grand Challenge. Please join our community to find out about future fund opportunities.
In 2016, the National Academies of Sciences, Engineering, and Medicine (NASEM) convened a committee to “set forth a vision for the emerging discipline of data science at the undergraduate level” given the centrality of data and data-driven work to scientific discovery and societal change in the modern era. Often regarded as the intersection between computation, statistics, mathematical foundations, and domain specific knowledge, NASEM’s Data Science for Undergraduates (DS4U) report describes data science as encompassing a broad array of activities, including “data collection, storage, integration, analysis, inference, communication, and ethics.” Learning Lab’s Grand Challenge seeks to incentivize public higher education institutions to embrace data science as an opportunity to build new pathways, modernize majors, attract historically underrepresented students into STEM, and deepen both civic and interdisciplinary learning.
By offering the following grants and promoting collaboration amongst awarded projects through a cohort model, Learning Lab hopes to promote the buildout of a data science educational infrastructure that will further educate and engage faculty, mobilize intersegmental collaboration, and create both clarity and plenty in the options students can pursue for their interest and future careers.
Through this RFP, Learning Lab intends to award the following categories of grants:
The following recommendations from the National Academies’ Data Science for Undergraduates report further inform the foundations of this Grand Challenge, providing both rationale and guideposts relative to the grant opportunities, which are further described below:
Recommendation 2.1 – Pathways | Faculty
Academic institutions should embrace data science as a vital new field that requires specifically tailored instruction delivered through majors and minors in data science as well as the development of a cadre of faculty equipped to teach in this new field.
Recommendation 4.1 – Pathways | Interdisciplinary
As data science programs develop, they should focus on attracting students with varied backgrounds and degrees of preparation and preparing them for success in a variety of careers.
Recommendation 2.2 – Pathways
Academic institutions should provide and evolve a range of educational pathways to prepare students for an array of data science roles in the workplace.
Recommendation 2.4 – Pathways | Faculty | Interdisciplinary
Ethics is a topic that, given the nature of data science, students should learn and practice throughout their education. Academic institutions should ensure that ethics is woven into the data science curriculum from the beginning and throughout.
Recommendation 5.3 – Pathways
Academic institutions should ensure that programs are continuously evaluated and should work together to develop professional approaches to evaluation. This should include developing and sharing measurement and evaluation frameworks, data sets, and a culture of evolution guided by high-quality evaluation. Efforts should be made to establish relationships with sector-specific professional societies to help align education evaluation with market impacts.
Recommendation 5.1 – Pathways | Faculty | Interdisciplinary
Because these are early days for undergraduate data science education, academic institutions should be prepared to evolve programs over time. They should create and maintain the flexibility and incentives to facilitate the sharing of courses, materials, and faculty among departments and programs.
Recommendation 5.2 – Faculty
During the development of data science programs, institutions should provide support so that the faculty can become more cognizant of the varied aspects of data science through discussion, co-teaching, sharing of materials, short courses, and other forms of training.
Recommendation 2.3 – Interdisciplinary
To prepare their graduates for this new data-driven era, academic institutions should encourage the development of a basic understanding of data science in all undergraduates.
Learning Lab intends to award the following categories of grants:
Up to 3 awards | ≤$1.3 million each | Over 3-4 years
Up to 5 awards | ~$200K to ~$350K each | Over 2-3 years
Up to 9 awards | ~$100K to ~$200K each | Over 2-3 years
Grand Challenge Cohort Coordinator
Up to 1 award | ≤$500k | Up to 5 years
Pathways Development Grants
These grants, up to $1.3 million each to be expended over three to four years, will be awarded to institutions that develop 2-to-4-year pathways (community college to CSU or UC) in data science that result in relevant degrees and certificates that respond to industry needs and opportunities. Institutions may develop data science options in existing degree and certificate programs or develop new majors, minors, or certificates. Two-year pathways or certificates that are developed must segue to a four-year pathway. At least two institutions from different segments must be part of the project team and work in collaboration to build a seamless pathway. Additional collaboration with high schools is welcome. Pathways that are developed should demonstrate how they can attract students from varied backgrounds (especially students historically underrepresented in STEM) and degrees of preparation and prepare them for success in a variety of careers that require data acumen. For these grants, scale and reach of these pathways as well as potential to close STEM equity gaps will be a significant factor in making an award. Learning Lab intends for this award to incentivize acceleration of the pathway approval process.
Faculty Development Grants
These grants, between $200,000 and $350,000 each to be expended over two to three years, will be awarded to institutions that are committed to increasing faculty capacity to teach in the data science field. Approaches can include (but are not limited to) enhancing the ability of data-science adjacent faculty to teach data science, incentivizing data-science adjacent graduate students to teach data science, accessing existing faculty development programs, or creating new programs. At least two institutions from different segments must be part of the project team and work in collaboration to build, enhance, or extend a program of faculty development. For these grants, prior success in developing and administering faculty development programs and ability to improve faculty development programs will be important considerations for awarding the grant. The proposed reach of a faculty development program that is able to attract faculty from more than one segment will be strongly considered.
 By data-science adjacent, we mean, for example, faculty in math, statistics, and computer science, or in applied fields.
Interdisciplinary Collaboration Grants
These grants, between $100,000 – $200,000 to be expended over two to three years, will be awarded to institutions that are committed to exploring curricular collaboration between data science and application domains (e.g., business, medicine, natural science, social sciences, or engineering) and/or across domains in the humanities, such as philosophy, rhetoric, history, and literary studies. Because the focus of this grant is to work across disciplines, intersegmental collaboration is encouraged but not required. For these grants, commitment at the department level (minimum of two departments must be involved in these grants) and sustainability beyond the grant period will be important factors to consider, as will the potential for newly designed or redesigned courses to attract students from varied backgrounds (especially students historically underrepresented in STEM) and levels of preparation. For these grants, scale and reach of these courses will be a significant factor in making an award. Promotion of data and computational proficiency across non-STEM majors will also be valued.
Cohort Coordinator Grant
This grant of up to $500,000 for up to five years will support a team that will serve a critical role in promoting collaboration across awarded projects. Because the Cohort Coordinator will be serving projects that have varying goals and approaches given the three different types of grants (see above), a significant factor in making the award will be the diversity and strength of expertise and experience possessed by the Cohort Coordinator team members related to data science as a discipline, group facilitation skills, knowledge of pedagogical and curricular innovations, understanding of articulation and transfer programs and policies, and best practices in faculty professional development among other factors.
To apply for a Learning Lab Grand Challenge Grant, projects must:
 Other institutions, their employees, or private consultants may be contracted as sub-grantees on the project if their expertise would support project implementation.
Please note that the Grand Challenge Coordinator Statement of Intent is due on December 9, 2022; however, the window for submitting an application will not open until April 10, 2022, with the deadline being May 29, 2022.
Learning Lab recommends that Cohort Coordinator applicants tailor their application responses to reflect the breadth of Grand Challenge project awardees, which will be announced by May 12, 2023*.
Only California institutions of public higher education, including segment central offices, may apply. An institution(s) of public higher education may apply solely for the Coordinator grant or may apply for both a Grand Challenge grant and a Coordinator grant.
The grant application process consists of three stages:
All applicants must submit a Statement of Intent as well as a Self-Assessment and Initial Proposal. Learning Lab’s Selection Committee will then invite a select group of applicants to submit Final Proposals. All applicants must submit their application materials through Learning Lab’s online Grand Challenge Application (linked below).
Statement of Intent
All applicants for the Grand Challenge Project and/or Cohort Coordinator awards must file a Statement of Intent that identifies the anticipated host and partnering institutions and provides the names of PIs/co-PIs as well as brief project information. The deadline to file a Statement of Intent has passed and Learning Lab is no longer accepting submissions.
Project Summary, Self-Assessment(s), Institutional Cover Letter(s), and Initial Proposal
Teams applying for Grand Challenge Project awards that have submitted a Statement of Intent by the deadline will be asked to fill out the following summary information regarding their project on Learning Lab’s Grant Portal and submit a Self-Assessment (per institution), an Institutional Cover Letter(s), and an Initial Proposal. The deadline to file the Project Summary, Self-Assessment(s), Institutional Cover Letter(s), and Initial Proposal is 5:00pm PT on Wednesday, February 1, 2023.
* Reminder: Cohort Coordinator applications will be submitted after Grand Challenge awardees have been selected and notified in May 2023. Cohort Coordinator applicants will be provided more information as the process progresses toward the selection/notification date.
College of the Canyons
Kathy Kubo is a Mathematics Instructor at College of the Canyons.
Professor Kubo helped create the college’s redesigned statistics pathway and led a faculty training program to dramatically scale the number of sections offered. Canyons’ statistics pathway was honored by the California Community Colleges Board of Governors, and in 2015, the Campaign for College Opportunity recognized Professor Kubo for her leadership in transforming developmental mathematics at the community college level. In 2021, the college won a national Bellwether Award for the work she and her colleagues helped lead to develop its innovative approach to assessment and course sequence redesign in English and mathematics.
Professor Kubo participated in a National Science Foundation grant on statistics education, worked with Stanford University’s Open Learning Initiative to revise their Concepts of Statistics courseware, and was a writing team member for the American Statistical Association (ASA) Two-Year College Data Science Summit. She was selected as a 2019 Fellow for the ASA Section on Statistics and Data Science Education Fellowship Program for High School and Two-Year College Teachers. Professor Kubo worked with the California Acceleration Project and coordinated the Chancellor’s Office Statistics Institute, introducing best practices in pedagogy for teaching introductory statistics at the community college level.
She holds a master’s degree in Mathematics from the University of California, Los Angeles.
Associate Dean for Curricular Affairs
Professor, Computer Science
Dr. Leonard serves as the Associate Dean for Curricular Affairs at Occidental College. In this role, she provides administrative oversight, stewardship, and overall management of the College’s curriculum, including the Core Program, the College Writing Program, and the College’s majors and minors. Additionally, Dr. Leonard serves as Professor of Computer Science. Prior to Occidental College, Leonard was an Associate Professor of Mathematics at CSU Channel Islands.
Dr. Leonard has a long history with undergraduate research, most recently culminating in a $1.4 million NSF grant to fund the Center for Undergraduate Research in Mathematics, which she directs. Other research interests include mathematical modeling for computer graphics, computer vision, and other data science applications. Dr. Leonard has extensive background in increasing representation in the computational sciences, particularly for minoritized genders, including significant work with the Association for Women in Mathematics (AWM), where, among other roles, she is currently President. She has organized several workshops and conferences to highlight and promote women’s work and engaged with multiple initiatives to promote diversity.
She holds a Bachelor of Science degree from the University of New Mexico and a Master of Science and Ph.D. from Brown University.
Executive Director, Data Science Academy
North Carolina State University
Dr. Rachel Levy is the inaugural leader of the Data Science Academy (DSA) at North Carolina State University (NCSU). She incubates data science research partnerships within NC State, across North Carolina and beyond, led development of the DSA’s ADAPT course model (All-Campus Data Science Accessible Project Based Teaching and Learning), and communicates about data science with national and international audiences.
Prior to joining NC State, Dr. Levy was an American Mathematical Society Congressional Policy Fellow at the American Association for the Advancement of Science. She previously served as deputy executive director and staff principal investigator at the Mathematical Association of America, as well as associate dean for faculty development at Harvey Mudd College.
Dr. Levy has received funding from government grants, foundations and private donations to enable her research in applied mathematics, education, and professional development. She has developed professional networks, written numerous publications, taught as an interdisciplinary educator, and has sparked national and international cooperation in education policy.
She holds Bachelor of Arts degrees in mathematics and English from Oberlin College, a Master of Arts in educational media and instructional design from the University of North Carolina at Chapel Hill, and a Master of Science and Ph.D. in applied mathematics from NC State.
Vice President for Undergraduate STEM Education
Executive Director of Project Kaleidoscope
Dr. Kelly Mack is the Vice President for Undergraduate STEM Education and Executive Director of Project Kaleidoscope at the American Association of Colleges and Universities (AAC&U). In this capacity, Dr. Mack provides leadership for the organization’s mission level commitments to quality and inclusion in STEM through the delivery of world class professional development that is aimed at empowering our nation’s STEM faculty and administrators to competitively educate and retain more STEM students. Prior to joining AAC&U, Dr. Mack was the Senior Program Director for the National Science Foundation ADVANCE Program while on loan from the University of Maryland Eastern Shore where, as a Professor of Biology, she taught courses in Physiology and Endocrinology for 17 years.
Dr. Mack’s holistic approach to STEM reform is grounded in a strategic vision that foregrounds inclusion as an immutable factor for achieving excellence in undergraduate STEM education. Her leadership in STEM reform has led to: significant increases in the capacity of STEM faculty to implement culturally responsive pedagogies, major shifts in the ways in which leadership development for STEM faculty is delivered, and the expansion of both physical and virtual convening platforms for knowledge generation, exchange, and dissemination.
Recognized as a national thought leader in higher education, Dr. Mack’s work has been highlighted in Diverse Magazine and U.S. News and World Report. Currently, she is an advisor to several institutional transformation initiatives at NSF-funded ADVANCE institutions and is a member of the National Academies Roundtable on Systemic Change in Undergraduate STEM Education and the Howard University School of Arts and Sciences Board of Visitors. She is also co-founder and chair of the board of the Society of STEM Women of Color, Inc., and has served as member of numerous board and national committees.
Dr. Mack earned the BS degree in Biology from the University of Maryland Eastern Shore and, later, the PhD from Howard University in Physiology. She has had extensive training and experience in the area of cancer research with her research efforts focusing primarily on the use of novel antitumor agents in breast tumor cells, as well as the use of bioflavonoids in the regulation of estrogen receptor positive (ER+) and estrogen receptor negative (ER-) breast tumor cell proliferation. Most recently, her research efforts have examined STEM leadership development and the impact of mindfulness on STEM faculty self-efficacy.
Associate Professor, Department of Geography
Associate Director, Center for Human Dynamics in the Mobile Age (HDMA)
Faculty Member, Big Data Analytics program
San Diego State University
Dr. Atsushi Nara is an Associate Professor of Geography, Associate Director of the Center for Human Dynamics in the Mobile Age, and Faculty Member of the Big Data Analytics program at San Diego State University.
Dr. Nara’s research interests are in Geographic Information Science, spatiotemporal data analytics, modeling behavioral geography and complex urban-social systems, and geocomputation. He has the depth of experience and technical expertise in data collection, data integration, database management, sensor technologies, and software development for conducting transdisciplinary GIScience research. He employs data analytics, simulation models, and GIS to study human dynamics, movement behaviors, location-based social networks and their contexts applied to urban dynamics, evacuation and disaster responses, public health, and system management in a complex hospital setting. Dr. Nara contributes to the research and development of a school-to-college curriculum pathway in geocomputation education that accounts for the diverse aspirations and job prospects of students. Dr. Nara also contributes to the development of an open data center and health science knowledge repository for the SDSU HealthLINK Center.
He holds a Bachelor of Science degree from Shimane University, a Master of Science from University of Utah, and a Ph.D. from Arizona State University.
Associate Professor of Teaching
Dr. Bob Pelayo is an Associate Professor of Teaching and currently serves as Vice Chair for Undergraduate Studies in the UC Irvine Mathematics department. His primary responsibilities and professional interests include novel curriculum development at both the college and high school levels.
Dr. Pelayo is involved with several grants focused on pedagogical and curricular interventions; in particular, he is the Principal Investigator on the California Learning Lab-funded BioCalculus Preparation, Engagement, and Application (BioCalc PEA) program. Dr. Pelayo also has a leadership role in the development of the new AP Precalculus course and exam. Prior to joining UC Irvine, Dr. Pelayo founded the Data Science program at his previous institution, the University of Hawai`i at Hilo. He also serves as the faculty advisor for the UC Irvine Mathematics department’s Data Science concentration and is a Co-Principal Investigator on the National Science Foundation-funded Southern California Data Science Fellowship program.
He earned a Bachelor of Arts degree in Mathematics and Psychology from Occidental College and a Ph.D. in Mathematics from the California Institute of Technology.
Dr. Candace Thille is an associate professor in the Graduate School of Education and in the Neurosciences Interdepartmental Program at Stanford University. She is the faculty director for workplace learning, Stanford Accelerator for Learning.
Previously, Dr. Thille was Amazon’s director of learning science and the founding director of the Open Learning Initiative (OLI) at Carnegie Mellon University and at Stanford University. Her work is in applying the results from research in the science of learning to the design and evaluation of technology mediated learning environments and in using those environments to conduct research at the intersection of human and machine learning. Dr. Thille serves on the board of directors for ETS and has served on the board of directors of the Association of American Colleges and Universities; as a fellow of the International Society for Design and Development in Education; on the Assessment 2020 Task Force of the American Board of Internal Medicine; on the advisory council for the Association of American Universities STEM initiative; and on the advisory council for the National Science Foundation Directorate for Education and Human Resources. She served on the working group of the President’s Council of Advisors on Science and Technology (PCAST) that produced the Engage to Excel report and on the U.S. Department of Education working groups, co-authoring the 2010 and 2015 National Education Technology Plans.
She holds a bachelor’s degree from the University of California, Berkeley; a master’s degree from Carnegie Mellon University, and a doctorate from the University of Pennsylvania.
The documents within this section are intended to be helpful resources as your project team develops a proposal. There is no requirement for their use (with the exception of the Budget Template). Click on the buttons below to download.