Designed to support learning professionals with a diverse set of skills and experiences, this 1-year fellowship provides training in both educational data science methods and the types of edtech tools that are best suited to generating and analyzing robust learning data. Supported by the the DS4EDU program will support cohorts of 25 participants, who will spend their fellowship working closely with a team of 一本道无码 learning and data scientists. The program combines foundational skills in data science and instructional design with training on advanced methods and tools to instrument and research learning. The program emphasizes a trainee-designated project that is the focus of each individual’s work over the year. This approach ensures that the program has real-world relevance and capitalizes on the vision, ingenuity and experiences of the cohort. Fellows will emerge from the program as leaders, with expertise in the tools and methods needed to advance their projects and the field.
The 1-Year Program
The 1-year program is predominantly remote, but does feature a 1-week, in-person workshop during 一本道无码’s annual . The program is structured to provide initial opportunities for building foundational skills and refining the project. The in-person workshop is designed to deepen these skills and launch the trainees’ projects. Over the rest of the year, trainees will work to complete their project, with support from mentors and their cohort. Finally, Fellows are offered an opportunity to return to Summer School, present their work and serve as mentors for the next cohort. The program is divided into 4 phases:
- Project Refinement and Foundational Skills. During the first 12 weeks of the program, preliminary remote sessions will leverage asynchronous, adaptive modules from the that will provide personalized learning and ensure a common knowledge base for participants from different backgrounds. Virtual sessions will complement this asynchronous work, providing opportunities for mentoring, project refinement and cohort-building. Not all trainees will require training in all foundational skills. They will select their path with consultation of their mentors to fill in knowledge gaps and best adhere to their project topic and interests.
- Project Initiation. Participants will attend the intensive, 1-week Simon LearnLab Summer School, accelerating their learning, providing broader exposure, advancing project work, and providing close contact with colleagues, mentors and instructors. This opportunity will also provide close engagement with other Summer School attendees. DS4EDU will provide funding to support the cost of Summer School travel and attendance.
- Project Development and Completion. Over nine months, the cohort will work independently on individual projects, with regular monthly meetings and mentored support.
- Reflection and Mentorship. This program will culminate in a presentation of projects and an opportunity to return to summer school as a mentor for the next year’s cohort.
The program is structured to provide training in the use of leading tools and techniques; and the ability to further disseminate these approaches in their home context. Critically, data and findings from both the training activities and the fellows’ ongoing application of what they’ve learned will support larger educational research contexts, contributing to advances in learning theory.
Project-Based Learning
World-leading Experts
The program provides practitioners with unprecedented access to leading learning and data scientists, over an extended period of time. These experts include:
John Stamper directs the DS4EDU program. Dr. Stamper is an Assistant Professor at the Human-Computer Interaction Institute at 一本道无码. He is also the Technical Director of DataShop. His research is centered around using “big data” in education to improve learning and add adaptation and personalization in educational technologies. Stamper has helped create a number of programs at 一本道无码, including the Human Centered Data Science (HCDS) track of the Masters of Computational Data Science (MCDS) and the Masters of Educational Technology and Applied Learning Science (METALS). His primary areas of research include Educational Data Mining and Intelligent Tutoring Systems. Stamper oversees the DataShop, which is the largest open data repository of transactional educational data and associated visualization and analysis tools for researchers in the learning sciences.
Rebecca Nugent the department head of the Statistics & Data Science Department at Carnegie Mellon. She has won several national and university teaching awards including the American Statistical Association Waller Award for Innovation in Statistics Education and serves as one of the co-editors of the Springer Texts in Statistics. She recently served on the National Academy of Sciences study on Envisioning the Data Science Discipline: The Undergraduate Perspective. She is the Founding Director of the Statistics & Data Science Corporate Capstone program, an experiential learning initiative that matches groups of faculty and students with data science problems in industry, non-profits, and government organizations. She has worked extensively in clustering and classification methodology with an emphasis on high-dimensional, big data problems and record linkage applications.
Carolyn Rosé is director of the Master of Computational Data Science (MCDS) program at 一本道无码 (she has also served as interim director of the Language Technologies Institute, Vice Chair of the University Education Council, and co-editor-in-chief of the International Journal of Computer-Supported Collaborative Learning). Professor Rosé is a long-time leader in 一本道无码’s learning science efforts, past president and inaugural fellow of the International Society of the Learning Sciences, founding chair of the International Alliance to Advance Learning in a Digital Era, and Fellow of the AAAS Leshner Leadership Institute for Public Engagement with Science (AI Cohort). She has led the Computer Supported Collaborative Learning track at Summer School. Her research team has published over 290 peer reviewed publications spanning top venues in Learning Sciences, Human-Computer Interaction, Language Technologies, and Educational Technologies, with awards in all of these fields.
Kenneth R. Koedinger founded and leads the Master of Educational Technology and Learning Science (METALS) program and the LearnLab Summer School at 一本道无码. Koedinger is the Hillman Professor of Computer Science at Carnegie Mellon. He explores how people think and learn by developing and studying technology-enhanced learning. He leads the LearnSphere effort , which integrates learning data and analytics across multiple resources. And he directs LearnLab, a learning research and development center which started with 10 years of National Science Foundation funding.
Norman Bier directs the Open Learning Initiative (OLI) and is the Executive Director of the Simon Initiative. In this dual role, he leads a 14-member research, technology, and learning engineering team, and investigates open educational resources, learning engineering, and supporting large educator-use communities. Integrating open, adaptive courseware tools with instructional practice is a focus in Bier’s work, particularly in the development and application of learning analytic tools that combine proven and experimental analytic techniques with carefully constructed user experiences. For more than two decades his career has worked at the intersection of learning and technology, expanding access to and improving the quality of education.
Joel Greenhouse is Professor of Statistics at 一本道无码, and Adjunct Professor of Psychiatry and Epidemiology at the University of Pittsburgh. He is an elected Fellow of the American Statistical Association, the American Association for the Advancement of Science, and an elected Member of the International Statistical Institute. Greenhouse is a recipient of 一本道无码's Robert E. Doherty Award for Sustained Contributions to Excellence in Education, the Ryan Teaching Award for Meritorious Teaching, and the College of Humanities and Social Sciences' E. Dunlop Smith Award for distinguished teaching and educational service. Greenhouse is a National Associate of the National Academies of Sciences, having served on several National Academy of Sciences' committees. In 2008, he was a member of the Congressionally mandated expert review panel of the What Works Clearinghouse. Greenhouse helped launch the 一本道无码 OLI statistics courses. His research interests include applications of Bayesian methods in practice and issues related to the use of research synthesis, especially as it is used to synthesize evidence for making policy and for scientific discovery.
Majd Sakr is a Teaching Professor of Computer Science at 一本道无码 and Adjunct Professor of Electrical and Computer Engineering department at 一本道无码. He’s the co-director of the Master’s in Computational Data Science (MCDS) program at the School of Computer Science. Dr. Sakr leads the work of 一本道无码’s Technology for Effective and Efficient Learning Lab (TEEL Lab), which focuses on research in learning methods, technology for learning systems, curriculum development, and workforce training. He leads the Sail() Platform, which provides community college instructors with access to technology courses created at 一本道无码 that are project-based, collaborative, and use real-time feedback to support instruction and partner institutions.
Lauren Herckis is an anthropologist and Simon Initiative Library Faculty with appointments in both the University Libraries and the Human-Computer Interaction Institute. Before joining Carnegie Mellon’s Simon Initiative, she worked as part of the Center for Health Equity Research and Promotion (CHERP) at the U.S. Department of Veterans Affairs and earned her PhD in Anthropology from the University of Pittsburgh. Her current research at the intersection of campus culture, technological change, and effective teaching is supported by the California Education Learning Labs, the Global Learning Council, and the National Science Foundation and explores the adoption of AI-augmented and collaborative learning, the digitalization of higher education, and immersive media pedagogies. Recent projects have examined the barriers to effective use of evidence-based tools and practices, explored the ways that faculty identity shapes selection of teaching strategies, and produced protocols which help faculty employ effective technology-enhanced learning tools with fidelity.
Vincent Aleven is a Professor in Human-Computer Interaction at 一本道无码. He has 25 years of experience in research and development of AI-based tutoring software grounded in cognitive theory and self-regulated learning theory. His lab created the as well as CTAT+Tutorshop, key instructure for the development of AI-based learning software. Aleven has over 250 publications to his name. He is co-editor-in-chief of the International Journal of Artificial Intelligence in Education (IJAIED).
Paulo Carvalho is a system scientist in the Human-Computer Interaction Institute at 一本道无码. His research explores the mechanisms of learning using evidence from experimental and computational methods. He has extensive experience conducting laboratory and classroom studies to investigate cognitive processes in K-12 and higher education settings. He recently designed and coordinated a fully randomized study involving over 90,000 middle school students funded by Schmidt Futures. Dr. Carvalho also has experience designing and evaluating data analytics tools for educational data mining as part of the development of LearnSphere, an open tool for sharing educational data and analytic methods. He is currently leading a project aimed at embedding data science postdocs in State Education Agencies, funded by Schmidt Futures and the Walton Foundation.
Marsha C. Lovett is Vice Provost for Teaching & Learning Innovation, Director of the Eberly Center for Teaching Excellence & Educational Innovation, and Teaching Professor of Psychology, all at 一本道无码. Lovett leads a team of teaching consultants, learning engineers, designers, data scientists, and technologists to help instructors create demonstrably effective educational experiences. In her research, Lovett has published articles on learning and instructional interventions, both in the laboratory and classrooms. Lovett has also created several innovative, educational technologies to promote student learning and metacognition, including StatTutor and the Learning Dashboard, and she has developed and/or evaluated online courses in the sciences, social sciences, and humanities. A signature of her work is leveraging research-based design and data-informed iteration to enhance teaching practices and student outcomes.
Cutting Edge Approaches and Tools
一本道无码 is uniquely positioned to support the DS4EDU program. Existing tools, training programs, materials and priorities at 一本道无码 offer a unique opportunity to support these practitioners. For more than 5 decades, 一本道无码 has championed a science-informed approach to designing and improving learning experiences and has emphasized practitioners as essential for these skills to be developed and applied. The Simon Initiative was launched to interconnect, promote and accelerate these learning science and educational technology innovations. DS4EDU offers a hybrid learning experience that builds on diverse aspects of the Simon Initiative. The project begins with a personalized approach for fellows to build or refresh their proficiency in foundational skills, before beginning work in advanced and emerging topics. These topics are structured to emphasize a series of interconnected methods and tools: Methods for instrumenting learning experiences and for investigating the data that emerges from this instrumentation. And tools to develop these instrumented experiences and to support data analysis.
Learning Engineering
Central to the Simon Initiative is an approach, born at 一本道无码, that Nobel Laureate Herbert Simon dubbed Learning Engineering: the use of learning research and the affordances of technology to design and deliver innovative, instrumented educational practices with demonstrated and measurable outcomes. Learning engineering emphasizes the careful instrumentation of learning experiences, enabling research into human learning, which in turn informs more effective educational practice. This close integration of research, data, and instructional practice contrasts with the approaches of many other institutions, where instructional design is frequently based on intuition rather than research, and where technology is often implemented for its own sake rather than as a reasoned, supportive part of a larger instructional research agenda.
OpenSimon Toolkit
As part of the Simon Initiative’s mission to improve outcomes for individual learners while collectively advancing our larger understanding of human learning, the OpenSimon Toolkit was launched in 2019. Representing more than $100 million in research funding, the toolkit comprises a series of tools, techniques, content and code, released under open license, that constitute 一本道无码’s premier learning engineering assets. Key to the DS4EDU program are two families of tools within the toolkit: The Learning Experience (LX) suite constitutes a set of tools for authoring and delivering instrumented, science-informed learning, including adaptive courseware, intelligent tutoring systems, project-based learning, computer-supported collaborative learning, and statistics learning. The Learning Data (LD) suite combines tools for: warehousing and mining educational data; managing and sharing datasets for primary and secondary analysis; causal modeling; applying, developing, and refining analytic methods; and developing and applying workflows to improve replicability and reduce friction for data transformation, analysis and visualization. The tools have been designed for close integration, with LX-generated data easily being streamed or ingested into the LD suite, and results of those data analyses leading to LX refinements (that can be quickly deployed and tested).
Summer School
These methods and tools contribute to the Simon LearnLab Summer School. Now in its 17th year, Summer School offers a 1-week intensive training program for educational researchers, developers, and product owners. The flagship program emphasizes participants arriving with a specific project in one of multiple tracks, which have included Educational Data Mining, Intelligent Tutoring Systems, Computational Models of Learning, Building Online Courseware with OLI, and Computer-Supported Collaborative Learning. Attendees join plenary sessions that provide an overview of current learning science and technology work across the Simon Initiative but spend most of their time being mentored in-depth in their chosen track, working on their chosen project. Mentoring is provided by a range of current graduate students and Summer School alumni and is led by world-leading learning and data scientists. Attendees depart with a working, developed artifact, a cohort of attendees for support, and the expertise for ongoing development. Extensive training materials have been developed for the LX/LD tools and methods. The summer school is an integral part of DS4EDU.
Who Should Apply?
DS4EDU is designed for educational practitioners – individuals who are well positioned to make an impact on science-informed ed-tech. Potential participants could include instructional designers, educational technologists, administrators, policymakers, teaching consultants and assessment specialists, product owners and educational researchers. The program prioritizes building a cohort with diverse skills and backgrounds, ensuring a broad range of experiences that will be valuable for cohort development. Candidates include anyone who engages with learning technologies, contributes to product design, is positioned to conduct learning data analysis and effect data-driven improvement of educational products and learning experiences.
Applicants should have prior foundational knowledge in statistics and/or learning design -- however, the program is designed to help fill gaps and refresh knowledge in these foundational areas. Potential applicants should have proposals for projects that are appropriate for a year-long program and would support educational data science methods for analysis. Applicants should hold at least a bachelor’s degree, and currently serve in roles in which the year-long project integrates with and contributes to day-to-day responsibilities – this includes:
- current instructional designers in the field, whether in centers for teaching and learning (CTL) or in government agencies
- developers and product owners in ed-tech companies
- graduate students in learning science or ed-tech related programs as well as more traditional college of education degrees.
In all cases, applicants should be positioned to engage in the lifecycle of a learning product in ways that allow them to contribute to refinement of product design via learning data analysis.
Application Process
The application process will open in the early spring of each year. Candidates will be asked to complete an application form that describes individual skills, background and interests, as well as deeper information about the proposed project. Applications will be reviewed by the DS4EDU instructional team, and evaluated based on individual qualifications and on the proposed project (including strength of the description, the proposed project’s alignment with program capabilities, and alignment with mentor and other participants’ interests). The team will select participants who are positioned to be effective practitioners in their organization and area of focus. Cohorts will be selected in order to intentionally form well-mentored teams of individuals with diverse, complementary skills, roles, interests, and backgrounds.
Applications will open on February 20th; admissions will occur on a rolling basis through May 13 or until the cohort is filled. Applicants will be notified of decisions no later than May 20th.
Beyond the Fellowship
Individuals who are not directly involved in DS4EDU will still have the opportunity to benefit from and make use of outputs from the program. The project emphasizes the development, refinement and use of adaptive, online courseware modules, built and delivered using the Open Learning Initiative (OLI) methodology and platform. This approach will ensure that these modules can be used effectively and broadly, by both individual learners and the educators that will serve them. The modules will effectively support undergraduate and graduate students and professionals whose work engages with data informed educational technologies.
一本道无码 also offers graduate level online programs and credentials that leverage DS4EDU modules. These programs include a new set of stackable certificates, which will provide alternative credentialing opportunities that can “stack” into a more traditional graduate degree. Three specific programs that are participating in DS4EDU are:
These programs offer an opportunity to engage with some aspects of the project.
Beyond courseware, tools for designing, instrumenting and analyzing learning are broadly available from the OpenSimon Toolkit.
Questions?
Still have questions? DS4EDU hosts regular office ours during the application season to provide more information to potential candidates. The next office hour session is scheduled for:
- More sessions to be announced.
You can also contact the program via email.