A Mentor Academy

Christopher Brooks, Director of Learning Analytics and Research, Office of Academic Innovation and Research Assistant Professor, School of Information

Rebecca Quintana, Learning Experience Designer

MOOC learners have an insatiable desire for more content, more examples, and more problems to try. This is particularly true in MOOCs where learners are developing new skills, such as creating data visualizations using Python. Learners require multiple opportunities to practice their emerging skills in order to become more proficient in a domain. Instructors and course staff may be hard-pressed to find the time to create the volume of examples and problems that would satisfy the appetite of MOOC learners. Further, because we operate in a global classroom (Kizelcic, Saltarelli, Reich, & Cohen, 2017), instructors want to provide diverse examples and authentically situated problems in an effort to cultivate an inclusive learning environment.

An instructional team from the University of Michigan (U-M) developed a Mentor Academy to address these challenges. Christopher Brooks (Research Assistant Professor at U-M’s School of Information) and Rebecca Quintana (Learning Experience Designer at U-M’s Office of Academic Innovation), along with an educational technology professor, two graduate students, and a community engagement specialist, developed and supported a two-week instructional program to guide mentors in the creation of authentic programming problems. We invited learners who had completed the Introduction to Data Science with Python course to help the course team generate new content, by engaging them in the creation of problems that will be used in future versions of the course. Our program is based on the concept of learner-sourcing, where learners contribute novel content for future learners, while engaging in a meaningful learning experience themselves (Kim, 2015). We wanted to create an apprentice-like experience, where mentors could interact with each other and with university professors and instructors, providing them a “higher touch” experience in which they could both learn and give back. The program included video lectures about how to locate open data sets, how to write authentic problems, instructor-moderated discussions, and live video chats with mentors and instructors.


Python code on the left of the image with a chat window on the right with two students on a webcam and a chat box
Left: The Jupyter notebook environment. Right: Live video chats.


120 mentors participated, joining us from a variety of countries including Brazil, Canada, China, India, and the US. We ran four sessions of the program, and mentors worked in smaller cohorts of about 30. Mentors located local datasets and wrote authentic problems that leveraged these datasets. The problems were written in the Python programming language, using the Jupyter notebook environment. Mentors shared their problems on the programs’ discussion boards, and improved their problems through feedback from fellow mentors and the instructional team. Mentors created a number of high quality problems, equivalent to the ones that the instructor was creating. The problems were diverse and covered a wide range of topics, skill sets, and interests. For example, a mentor from Germany created a problem about beer prices in Germany, while a mentor from the US created a problem about the California wildfires. The problems are going to be deployed in Introduction to Data Science with Python in February 2018 for new learners in the course.

This exploration was just stage one of the Mentor Academy. With the success we have seen here we are planning to turn this into a permanent fixture for us to experiment with how lifelong learners can be lifelong mentors — giving back to global learners while continuing to “level up” their own skills. Plans for 2018 include involving these mentors in new course design (for agile just-in-time review of new course material as it is created), as well as connecting mentors with learners in peer help networks, bringing on-demand collaborative problem solving to data science learning environments.

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