This article was originally posted on 10/22/2016 on EdSurge
James DeVaney, Associate Vice Provost for Academic Innovation
Chris Teplovs, Lead Developer for the Digital Innovation Greenhouse (DIG)
Remarkable breakthroughs happen at public research universities everyday, but bridging the gap between early innovation and widespread adoption is a challenge that these institutions know all too well. This is especially the case when it comes to education technology and curricular innovation.
In 2015 the University of Michigan established the Digital Innovation Greenhouse (DIG) as part of the Office Of Academic Innovation—a group charged with fostering a culture of innovation in learning in order to reimagine the 21st century public research university. DIG works with faculty, staff, and student user communities to grow tools to maturity, and establish pathways to scale through collaboration across and beyond the U-M community. With a team of developers, designers, behavioral scientists, data scientists and student fellows, DIG helps translate digital engagement tools from innovation to infrastructure. In its first year of operation, DIG tools were used by more than 22,000 U-M students and will soon be used by more than a dozen institutions.
DIG has received a steady flow of inquiries and visits from peer universities and edtech innovators. They all ask the same question: How do you get from early-stage innovation and R&D to adoption across an an organization as complex as a public research university?
While it would be silly to offer an overly prescriptive recipe that fails to take each institution’s unique context into account, we think we’re onto something that works. We offer our colleagues at peer institutions and edtech companies nine considerations for cultivating innovation on campus and beyond.
1. Establish clear values and guiding principles.
DIG team members codified our approach in a set of guiding principles that articulates our values, commitments and approaches to fostering innovation. These principles include understanding users and creating a minimum viable product, for example, and we apply them to each project. As an agile partner to faculty innovators and academic units, the DIG team consistently navigates new terrain, and these principles and values provide clarity of purpose. (See how we recently applied the principles to a writing tool called M-Write.)
2. Be impractical. Then consider constraints.
We establish audacious goals in order to transition new digital engagement tools from innovation to infrastructure. Worrying over questions about culture, data and technology could easily constrain our thinking from the start. In order to scale tools to tens of thousands of users within the first 12 months we need freedom to think impractically.
We’ve been fortunate to attract a team with unique talents to cultivate these projects. They are comfortable with the ambiguity inherent to such an endeavor, and understand there is an appropriate time to layer constraints back into our design-thinking approach in order to address implementation and scale proactively.
3. Build a dynamic team.
DIG started with three lead developers who took on a unique set of projects. Part of our mission is to actively share what we learn across campus in order to stimulate new innovative experiments. As we shared our work, new innovators came forward and we quickly realized we needed additional talent to support them. The DIG team identified and prioritized additional needs and quickly added members with expertise in user experience design, behavioral science, data science, software development and innovation advocacy. We continue to grow our capabilities in these areas and more as we foster a culture of learning in innovation at U-M.
4. Welcome talented student contributors.
In addition to growing our full-time staff, we benefit from the engagement of students through our Student Fellows program. Over the course of a year, we hire approximately 20 undergraduate and graduate student fellows who are both mentored in and contribute to areas as diverse as software development, user experience design, graphic artistry, innovation advocacy and data science. Our experiences with our students have helped us to validate and prioritize new capabilities needed to grow our model, including UX design, software development in the MOOC space, and software development aligned with gameful learning.
5. Design a model for agile development that leverages opportunities for discovery and scale.
As a public research university with more than 40,000 students, U-M is one of the largest living laboratories for conducting experiments in academic innovation. By coupling rapid development and deployment with assessment we foster a virtuous cycle of innovation that leads to further discovery. These assessments take a variety of forms ranging from one-on-one interviews with end-users to using techniques from the emerging field of learning analytics.
6. Build products with—rather than for—users.
Instead of ROI, we measure our success by community engagement and educational impact.
As we build minimum viable products with faculty innovators and teams, we move quickly to create learning communities. This results in greater impact on campus and often accelerates knowledge sharing and adoption as well as our due diligence around options for commercialization. We build our tools with our community of users, not simply for them. This attention to community engagement in addition to adoption separates our approach from many off-campus models.
7. Recognize exit as opportunity and not a four-letter word.
We are also committed to ensuring that viable projects thrive once they leave DIG. From the earliest stages, exit plans and concomitant hardening-off plans are developed for all projects that enter the Greenhouse. In some cases the strategy involves commercialization; in other cases it could involve shifting responsibility to our information and technology services group; in a smaller number of cases it could involve closing the project or narrowing development around a particular use case. We find that our process of prototyping and iteration allows us to extract meaning from all projects and continue moving forward in pursuit of our mission.
8. Embrace emergence and continue to strengthen capabilities as new opportunities emerge.
The process of product development in lean startup environments and other highly innovative organizations often incorporates the notion of a “pivot,” which is a “structured course correction designed to test a new fundamental hypothesis about the product, strategy, and engine of growth.” We embrace the concept of pivoting in DIG but we also engage in continuous assessment of our own capabilities, identify new needs, and respond by strategically hiring highly qualified personnel into new roles.
9. Provide links between research and practice.
Our work in DIG is both scholarly and practical. Much of the higher education sector draws a line between teaching and learning on one side, and research on the other. Our work straddles these worlds with a more unifying focus on discovery. As an example, in creating ECoach we are focused on easing the transition to college for all students. We developed this innovative tool, accelerated adoption across campus, and built upon this deployed effort to attract a National Science Foundation $1.9 million grant to further explore how personalization can advance equity on campus.
The approach we have adopted within DIG and across the Office of Academic Innovation—applying lean startup principles to promote academic innovation at a research university—is to the best of our knowledge, a novel one in the educational technology space. We see great promise in a scholarly and practical approach that aligns academic excellence, inclusion and innovation at its core.
Eric Joyce, Marketing Specialist
People disagree. When they do, decision makers are charged with weighing feedback from multiple parties to draw a conclusion about how best to move forward.
“You have decision makers who need to deal with that disagreement, you have many stakeholders who care passionately about the outcomes and they’re all inputs into that decision-making process.” – Academic Innovator, Professor Elisabeth Gerber
Professor Gerber brings the collaborative decision-making experience into the classroom with Policymaker – a customized, hands-on role-playing simulation designed to facilitate new interactive learning opportunities for students. She partnered with the Digital Innovation Greenhouse (DIG) to develop this digital tool with the flexibility to enable instructors to adapt classroom simulations into a variety of disciplines, topics and educational levels.
In Policymaker, learners experience strategy development, collaboration, advocacy and communication with the intention to help students form an appreciation for the complexity of the policy-making process and an understanding of stakeholder diversity while obtaining in-depth knowledge of policy issues. This digital tool places students in a real-world simulation where they explore elements of policy making including how public decisions are made, difficulties and challenges in the decision-making process and what is required of decision makers in our modern and diverse society.
Professor Gerber said residential students benefit from face-to-face engagement with faculty, fellow students and course materials, and she sees an opportunity to enhance these learning experiences with technology.
“One of my key goals is to introduce technology into the classroom simulations that I and other faculty members use in order to create a more engaged experience for the students and to allow me to provide information to the students in a more effective, efficient and probably interesting way.” – Professor Gerber
To learn more about Professor Gerber’s work as an Academic Innovator, watch the Academic Innovators video:
Eric Joyce, Marketing Specialist
The emergence of “big data” has stimulated new opportunities to better understand patterns, trends and associations in human behavior and interaction for individuals and organizations across all industries. These data have also uncovered new privacy concerns requiring an ethical framework for data scientists and other “big data” aggregators.
To help address these concerns, Professor H.V. Jagadish launched his Data Science Ethics MOOC on edX earlier this year examining the complexity of ethics, data ownership and privacy in data science. The course is designed for novices in the field and established data scientists alike. More than 4,500 lifelong learners enrolled in “Data Science Ethics” last May and Professor Jagadish has re-launched the course this fall. Learners explore ethical issues regarding who owns data, ways in which informed consent is granted and how different aspects of privacy are valued throughout the class. Course materials are designed for lifelong learners to explore independently and are published as open educational resources so that they may be used by other educators to supplement their curriculum.
As a leading expert in the field of data science, Professor Jagadish has contributed to Slate Magazine, U.S. News and World Report, the Conversation U.S. and authors his own blog, Big Data Dialog, where he discusses key issues surrounding ethics in data science. He is also a grant recipient from the Bill and Melinda Gates Foundation’s Grand Challenge Explorations family of initiatives designed to stimulate innovation to solve key global health and development problems.
Earlier this year, we discussed the impact of data science and the necessity to establish ethical guidelines in the field with Professor Jagadish. We followed up with him to discuss his experiences during the initial launch of the course and how he incorporates real-world case studies examining emerging ethical dilemmas from a data science perspective.
What questions have emerged in the field of data science and/or surrounding data ethics since first launching the course?
The crucial importance of data science ethics has grown tremendously even within the few months since the course was launched.
The White House put out a report, “Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights,” laying out a U.S. national perspective on Data Science Ethics, and underlining the importance of training such as this MOOC offers.
Cathy O’Neil wrote an excellent, if alarming, book: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. In this book she points out the many ways in which Big Data can hurt the poor, minorities and the underprivileged. I believe that Data Scientists trained to think ethically are the primary solution to the problems she so eloquently points out.
Tell us about the different case studies you incorporate in the course. How do these illustrate the real-world impact of data science ethics?
Data Science has tremendous impacts on society, and we are still understanding these as a society. So there is an interesting news story almost every week. I chose a few of the most interesting stories to make into case studies for the original launch of this course in May. I now have material for 2 or 3 more case studies since then. However, I am resisting the temptation to tinker with the course material too much. So I have not added these new case studies into the course in a full-blown way. Rather, I am planning to introduce them lightly – in discussion.
One other thing we are doing differently in this re-launch is to make the course self-paced, with all the material online on day 1. This gives the students much more flexibility with their timing, at the cost of some dispersion in the discussion threads.
What common ethical concerns may faculty, students, alumni and/or the public encounter from a data science perspective and what can they do to address them?
Even as society as a whole is becoming more aware and more concerned about possible problems that can arise due to the inappropriate use of data science, we still do not have basic data science education as part of the data science curriculum. We need this. We need every practitioner of data science to understand the potential impact of their work on society, to take responsibility for it, and to have enough understanding of the critical issues to be able to exercise good judgment.
In what ways can students apply what they learn in this course to make informed data science decisions or help organizations manage data responsibly and ethically?
In the practice of data science, a data scientist makes so many decisions every day: what variables to include in their model, what training data to use, how to resolve errors and missing values in data, what types of explanations to obtain from algorithms, and on and on. Many of these decisions can have significant downstream impact. The teachings of this course will help the data scientist to recognize these possible impacts and to take them into consideration as they make these every day decisions. The end result is a win-win: better decisions get made, with clear appreciation of societal impacts.
Of course, there will be some situations where we cannot get to a win-win. There may be a conflict between a desired ethical path and other pressing requirements. These will need to be worked out. This course will at least help frame the issues, so that there can be a meaningful discussion that leads to a well-considered decision.
Professor Jagadish’s Data Science Ethics course is available now on edX. Visit the Data Science Ethics page to enroll in this four-week course.
Professor of Electrical Engineering and Computer Science
College of Engineering
University of Michigan