Civil Rights, Elusive Sleep, Technology are Next Teach-Out Topics

Four new Teach-Outs in August and September will focus on technological advances that have changed the way we live, civil rights and civil liberties in the current political environment, and sleep deprivation.

Expanding the University of Michigan Teach-Out Series

This article was originally posted on 5/31/2017 on the edX Partner Portal

James DeVaney, Associate Vice Provost for Academic Innovation

In September 2016, University of Michigan’s President Mark Schlissel charged the Office of Academic Innovation and the U-M community to launch a set of rich and interconnected experiments to explore the future of education at the University of Michigan. One of many ideas that surfaced through the President’s Academic Innovation Initiative was the U-M Teach-Out Series which was conceptualized in January and February and launched in March 2017.

University of Michigan Teach-Out SeriesWith the Teach-Out, we combine the global reach of MOOCs with a model designed to explore new approaches to just-in-time teaching and learning. Yet the Teach-Out is very much connected to the ongoing Michigan saga. Teach-outs are modeled after the historic U-M teach-ins, which started fifty-two years earlier in March of 1965 in response to military action in Vietnam. Faculty considering how to best respond to President Lyndon Johnson’s escalation of troops into the country created a marathon educational event designed to activate public concern and elevate public discourse. Within a year, teach-ins were conducted at 35 other college campuses, and a few years later the model inspired the first Earth Day.

Throughout U-M’s 200-year history, we’ve leveraged academic innovation to expand our community and realize our public mission. MOOCs have provided new ways to think about how to best disseminate our broad portfolio of scholarly work with the world and how to connect our intellectual power with lifelong learners and decision-makers across society. MOOCs have helped to reframe several important conversations around teaching and learning, knowledge dissemination, openness, and inclusive learning environments, to name a few. We see significant room for further innovation as we continue to embrace compassion, openness, personalization, and inclusivity in higher education.

We believe the Teach-Out will accelerate our ability to bring new individuals and communities into unprecedentedly open and inclusive learning environments and offers a concrete contribution to the design of the compassionate public square for the information age. Through the Teach-Out model we will continue to explore opportunities to make the great public research university even more open in the future. We will unbundle our expertise from the disciplines and rebundle around problems that demand our attention. We are now seeing the MOOC evolve beyond minimum viable product and can point to a sizeable wave of second order experiments that move us closer to a future where anyone committed to lifelong learning and listening can fully participate.

We held our first four Teach-Outs between March 31st and May 14th and have released a new call for proposals to solicit ideas from instructional teams to create opportunities for learners around the world to come together with our campus community in conversation on topics of widespread interest. We have seen significant support for this model across disciplines and expect the next wave to reflect an even broader range of academic expertise and experience with learning technologies.

As with all pilots, we’re thinking actively about measures of success. We’re looking at relatively simple metrics like reach and participation, learner satisfaction, and change in understanding. We’re collecting different kinds of information as well. We are exploring the extent to which different Teach-Out approaches help us to effectively share our broad portfolio of scholarly of work. We’re looking at faculty involvement and the benefits and challenges of team instruction and multidisciplinary teaching teams. We’re thinking about different ways to measure our ability to connect U-M’s broad intellectual power to the problems most import to society. We’re looking at engagement from other institutions and our ability to attract new communities of learners. We’re exploring ways to capture whether learners are exposed to new ideas and perspectives. We’re interested in the relevance of the Teach-Out model to decision-makers at all levels of society. And we’re capturing the different ways that these global community learning events can become resources for different learners in different learning environments.

A Picture is Worth a Thousand Words: Understanding Learners Through Visualization

Kara Foley, MOOC Assessment Fellow, University of Michigan Ross MBA and M.A. Educational Studies

Eejain Huang, Data Visualization Fellow, Combined Program in Education and Psychology, Statistics

Filip Jankovic, Research Assistant & Data Science Course Coordinator, MSE Industrial and Operations Engineering

Why Data Visualization?

In the virtual world, learners’ background and needs are assumed and indistinct, creating one of the biggest challenges for massive online courses: finding out who the learners are.

Here at the Digital Education and Innovation Lab (DEIL), we have devoted a lot of time and resources into developing effective initiatives to transform education. As an essential element in this process, collecting and interpreting learner feedback is at both the beginning and end of every MOOC course.

Screen shot of dataThree surveys were designed with iteration, assessment, research and marketing needs in mind. However, useful information is often hidden from decision makers because of the considerable amount of data gathered from these surveys.

To better explore this large amount of survey data, understand our learners and adapt courses according to their needs and feedback, DEIL created the DEIL Data Team to tackle these issues.

Data visualization was chosen as an ideal approach to interpret big data based on several reasons. First, data visualization can serve as a summarization, a guidance, a metaphor and eventually a gateway to understanding data. Second, data visualization can convey learning analytics in a digestible, actionable format for the audience. Like the old proverb says, “sometimes a picture is worth a thousand words.”

Who We Are and Our Journey

Our team has three members from diverse backgrounds who have contributed different expertise. Eejain Huang is a Ph.D. candidate working in Combined Program in Education and Psychology as well as a masters degree in statistics. Filip Jankovic is a U-M graduate with a Master of Science and Engineering degree in Industrial and Operations Engineering and is currently working with Dr. Christopher Brooks on the Applied Data Science with Python Coursera Specialization. Kara Foley is a recent graduate and has obtained her Master of Business Administration degree and Master of Arts degree in Educational Studies.

Eejain Huang, Filip Jankovic and Kara Foley in discussion while sitting around a table with laptops in front of large screen with a visualization of the globeKara was hired as a MOOC Assessment Fellow in January 2016 to evaluate which data visualization tool(s) would both be feasible to implement and highly automated. With input from additional faculty and staff across the University, we opted to reformat and clean the data using R and construct the visualizations in Tableau. Kara created initial prototypes of dashboards, and saw the challenges of data manipulation and advanced visualization design that needed to be addressed by a larger team with additional skills.

Eejain then began her full-time role in the summer of 2016 to clean the data and reshape it into a format that could be used to create Tableau visualizations. Much work had been spent on optimizing data structures to accelerate processing time, cleaning text responses with regular expressions and creating unique identifiers to link different surveys.

Finally, when the data was ready for creating and refining the visualizations using Tableau, Eejain created the Tableau dashboard. Filip joined our team in August of 2016 bringing with him his expertise in data science guiding best practices for visualization design. Filip helped ensure the visualizations represented the data accurately and effectively – an essential step when creating dashboards that allow decision makers to make well-informed decisions.

As dashboard revisions were made, we shared the visualizations with faculty and staff for their feedback. This involved hosting multiple meetings with different teams within the Office of Academic Innovation, and also conducting individual interviews with people who have specialized expertise in different steps of shaping a MOOC course including Dr. Christopher Brooks, Laura Elgas, Dr. Gautam Kaul, Noni Korf, David Lawrence-Lupton and Dr. Donald Peurach. Through this process we have learned about main priorities, key data points of interest and potential implications of this data.

What Did We Find and Some Insights

Currently, we are still experimenting on different ways to represent data and are seeking the best ways to distribute our findings. However, even at this transitioning stage, we think our findings may serve as an insightful guide for understanding learners and transforming MOOCs.

Illustration of a visualization

Although we recognize that our sample size is a small percentage of our learners, we discovered some trends that instructors may find useful for course design. While the majority of our respondents come from the United States and self-identified as Caucasian/ European / Russian, learners from developing countries constitute the next largest proportion of respondents. We noticed a difference in gender participation in our MOOCs, and in higher education degree attainment. Gender was a factor as well in self-ratings regarding confidence levels for language and use of technology. Regarding degree levels, these played an important role in influencing learners’ choice about participating in a course, their expectations about the course and their motivation for participating.

When looking at the dashboard, we found that many of the demographics and student feedback changed from course to course. This view can provide important insights to instructors to help them better understand their learners.

In conclusion, our findings showcased how diverse our learner community is and how different their needs can be. Nationality, ethnicity, gender, educational background and prior experience etc., all contribute to their expectations of, and experience during, the course. Thus, we hope our visualization can help course designers understand who their audience is, what they want, as well as how to make their content more accessible and eventually create a more targeted, personalized and engaged experience for every learner around the globe.

Faculty who are interested in learning more about MOOC survey data may reach out to the Office of Academic Innovation directly at