Dr. Chuck Inspires Programming Educator of the Future

Adam Levick, Market Research and Analytics Analyst

A 15 year old high school student has recently decided to take on a new role this summer: teaching an introductory programming class to her peers. This particular class will help those peers learn how to program in Python on a Raspberry Pi computer. Inspired by Chuck Severance’s Programming for Everybody (Python), a free, massive, online course, Emma decided she wanted to pass on the knowledge she gained to those like her:

“Because I learned so much from the course on Coursera, I wanted to share that with more kids and more students like me… I wanted to help them learn more about programming.”

Emma plans on teaching the class in the last week of July, and anticipates that class’s size to be approximately 5-7 students. With all content under a Creative Commons license, Dr. Chuck has made his course easily remixable and sharable by any student.

Nick, Emma’s father, also had some advice for students interested in taking Dr. Chuck’s course:

“Many folks remark the course gets harder after a bit… I would say stay in there, that’s when the learning happens.”

Dr. Chuck learned about Emma’s plan at office hours in Ann Arbor MI. But Dr. Chuck doesn’t just offer office hours on campus. He has recorded them all over the world, including Italy, Slovenia, and the Philipines just to name a few. Engaging with students and offering them a course that they can “Use, Re-use, and Re-mix” is a primary part of his mission. He wishes the best for Emma in her pursuit to bring programming education to those around her.

To learn more about the course and/or enroll: Programming for Everybody (Python)

Follow the conversation: #PR4E


Precision Learning?

Gus Evrard, Thurnau Professor of Physics and Astronomy

In a 2013 Nature Medicine article, Alla Katsnelson noted a shift in the lexicon of modern clinical medicine; the framework once known as personalized medicine had morphed into precision medicine. Might educators soon be following suit?

The idea that knowing an individual’s genomic structure may lead to improved medical treatment holds great promise, but the fact that all humans share a common basic physiology poses fundamental limits on how personalized medicine may become. Instead, the concept that each of us is a machine with a slightly different biochemical instruction set means that: i) we can be categorized based on those differences and, ii) treatments can be made more effective through precise understanding of how biochemical variations drive physiological changes associated with disease. The practice of precision medicine will be powered by data (it’s sexier to say Big Data), including laboratory data on underlying biochemistry and clinical data on treatment outcomes.

In the world of education, the concept of personalized learning is now associated with a host of efforts that seek to promote better outcomes for individuals across the full spectrum from kindergarteners to life-long learners. At Michigan, DEI is promoting a number of strategic projects that seek to develop and apply new technological solutions aimed at enhancing teaching and learning for all individuals.

Academic Report Tools (ART) is one such project. Michigan is a very large university, and no one can hold in their heads the rich and ever-evolving detail of the curriculum offered by its 19 schools and colleges. Think of ART as providing a “paint-by-numbers” approximate view of this complex reality. The service currently allows faculty and staff to easily view historical enrollment and grading patterns (including co-enrollment data among pairs of courses), providing fragmentary pictures to be painted that impart some knowledge. Are Engineering students outperforming LSA students in the first-semester course in Statistics? ART slices into the historical data and quickly provides the answer.

We are now beginning to plan the next generation service that will include tools aimed at our students. Personalizing these services is important, in that the needs of (say) a Master’s student in the School of Information are likely to differ from those of (say) a first-year undergraduate in LSA who’s thinking of a dual philosophy and physics degree but might want to do biopsychology instead. What can connect these students is the desire to view some historical collection of data that paints a useful fragmentary picture, one that may inform an impending decision.

Most students (and tuition-paying parents) would ideally like a very high degree of personalization, something along the lines of, “Based on your current standing, taking this particular set of N courses in the following order will essentially (>95% confidence) guarantee a career in discipline X and a high lifestyle happiness index five years after graduation.” Even in our overtly data-rich world, this is currently too much to ask.

Instead of this high degree of personalization, tools for precision learning could follow the lead of medicine. Every student is a proverbial snowflake (unique in detail and, thereby, above average in some particular measure), but viewed from a distance she or he can be classified by a relatively modest set of attributes. Opening windows into how students with different attributes have tied into curricular and co-curricular activities at Michigan, and to careers beyond campus, is a service of great interest to educators, students and parents alike.

Of course, higher education is a dynamic enterprise, so using past or current trends as an indicator of future performance will entail risks.

Being precise means having little uncertainty. Thankfully, the large size of the existing student record database at Michigan means that there is low-hanging fruit that ART services for students can pick. We can paint courses by their numbers, allowing views of student attribute composition, instructor evaluations, and historical grade distributions. Another practical issue students need help with is a better way to formulate and choose among options for multi-term course selection. This is particularly important for juniors and seniors who have declared multiple majors.

While precision learning services don’t aspire to the higher ideals of personalized learning, their utility may render the subtle shift in lexicon of interest only to academics.


Collaborative Massive Research: MOOC Research Summit 2015

Christopher Brooks, Research Fellow, School of Information and Director of Learning Analytics and Research, DEI

Millions of learners have engaged in learning through Massive Open Online Course platforms such as Coursera, NovoEd, FutureLearn and EdX.  With content provided by many of the world’s best universities, these platforms have delivered university-style courses on subjects ranging from the fine arts and humanities to those in medicine and the physical sciences.  In addition to diversity of content and domain, there are broad differences among courses with respect to pedagogy, length, and accreditation mechanism.  The University of Michigan has been deeply involved in exploring this new teaching and learning paradigm, and has offered up nearly 30 courses on the Coursera platform in the last three years to more than 3 million learners.

But behind the curtain of this new instructional delivery mechanism there is a vibrant research community digging deeply to understand how scaled-up education environments can help us learn new things about teaching, learning, and education.  Through the Learning Education and Design Lab (LED), Michigan is participating in this research.  Recent examples include investigating how alumni and students are using MOOCs to further their education, what effect signing up for a MOOC with a friend has on your likelihood to finish, and how predictive models might be formed to help provide an early warning system for student success.

To go beyond understanding individual courses, research needs to be conducted across platforms, universities, and disciplines. On Monday June 22nd the Office of Digital Education &  Innovation (DEI) will be hosting a MOOC Research Summit, with representatives from 15 higher education institutions across  five countries joining to discuss how collaborative inter-institutional research can help us better understand MOOC learners and their needs.  The goal of this summit is to identify learning science questions that MOOCs might be particularly helpful in shedding light on, and to develop an agenda to investigate these questions in a pragmatic and scholarly manner. The summit provides the next example of Michigan’s emphasis on fueling transparency and collaboration around our scholarly and practical approach, as demonstrated through the MOOC Data Policy. We’re looking forward to helping to set the direction of this emerging research community

Helping Faculty Navigate Copyright in their MOOCs

Mike Daniel, Director, Policy and Operations for Digital Education & Innovation

Faculty are presented with a myriad of challenges and opportunities when creating Massive Open Online Courses (MOOCs). These can range from pedagogical considerations like instructional and assessment design to technical considerations such as determining whether their video content will be recorded in a studio or out in the field. At a more basic level there are day-to-day logistical considerations. One area that can be particularly nuanced is deciding when and how to incorporate content (video, audio, visual or textual) into MOOCs.Incorporating relevant materials into a course is helpful and often necessary in order for faculty help students best achieve intended learning outcomes.

And while selecting the right materials is a major part of instruction, copyright law frequently informs content selection in digital course materials, making copyright an important consideration in online course design. Faculty often need help navigating copyright as they design and create their online courses. From a legal, financial, and reputational standpoint, it is important for educational institutions like the University of Michigan to develop a framework to assist faculty as they create MOOCs, one that enables reasonable, informed copyright-related decisions from the start.

The Office of Digital Education & Innovation (DEI) at the University of Michigan and the Copyright Office at the University of Michigan Library (UCO), recognized the need to work with faculty on the front end of designing their MOOCs to help navigate key issues related to copyright. IThus, DEI and UCO developed a copyright clearance process and a copyright guidance document as a starting point for faculty to think about copyright as they select course materials.

The DEI Copyright Guide details this clearance process to help faculty to make initial determinations considering copyright. . This guide provides information that allows faculty to proactively consider key aspects of copyright (e.g. subject matter of copyright, copyright duration and public domain, licensing, including Creative Commons licenses, attribution, fair use, and notice and takedown) when designing, creating, and producing digital content for their MOOC. In addition to the Copyright Guide, DEI has plans to create a suite of video resources for faculty around copyright.

While norms around MOOC course creation continue to evolve, we are working together to create practical, effective processes respectful of copyright while facilitating innovative course creation. Empowering faculty with knowledge in this area while supporting copyright awareness will result in more engaging and powerful U-M MOOCs, more robust pedagogical experimentation, and legal remixing and reuse of digital content across a variety of on-campus and online courses.

Why gameful? Why GradeCraft?

Cait Holman, PhD Candidate, School of Information – @chcholman
Barry Fishman, Professor, Learning Technologies, School of Information & School of Education – @BarryFishman

The next time you see someone playing a videogame, stop and watch for a moment: they are engaged in what they’re doing. They may have an intense look about them, and it doesn’t seem like what they’re doing is what we’d call ‘easy’ – they lose a round, they get frustrated, but they keep playing. They come back. They try it again. When they finally win they may start all over again, playing the game in a completely different way, looking beyond linear progress for additional opportunities – things like secret routes, new interactions, and different ways to collaborate with a community of other players. There is something about the experience of playing these games that makes them spaces where people of all ages and interests are willing to work hard, persist past failure, and learn how to succeed. Gameful learning is a new pedagogical approach that takes inspiration from the engagement we see in videogames, and uses it to reimagine what traditional learning environments could be like – particularly, what role students can play and how the design of assessment systems supports student engagement.

First, a disclaimer: gameful learning is not about making school easy…or even fun. This is about designing environments where students are encouraged to focus less on their final grades, and more on the craft of learning; where they are motivated to face down the very real struggles of mastering challenging new material, but persist day after day and are able to see progress; where they take responsibility for their learning, and make self-aware choices regarding how they can best learn and be assessed on their development of content mastery. We know from psychological research that this mode of self-driven, creative, resilient drive to progress, termed “intrinsic motivation,” is best supported when individuals feel like they have meaningful control over their work (autonomy), are facing challenging but doable work (competency), and feel connected to the people around them (belongingness).

So how do we create these environments? We begin by flipping the frame from an assessment system where everyone starts with 100%, because that is a lose-as-you-go scheme, where each new grade decreases a student’s class average (the current normative model for grading systems), to a 0-based, earn-up model, where each assessment increases the student’s cumulative points and represents his/her progress towards mastery. Then we build a series of optional assessment pathways so that students have autonomy over how to engage with course content. These can take a variety of forms – sometimes the content lends itself to students developing deep personalized specializations, as an offshoot of the core content studied collectively. At other times it means students complete different forms of assessment around the same content – with some choosing to take an exam, while others might write an essay or do a group project.

These changes introduce a logistical challenge: students need to know how the range of choices build towards their course grade. In gameful systems, instructors typically create more assignments than students actually to need to complete to earn an A in the course. This can be as many as 1.5 – 2 times the number of assignments in a normal course. In our early experiments with gameful grading systems, we observed a huge amount of excitement from students, but also a lot of confusion. We realized we needed technology to support students in gameful courses, and so we created GradeCraft, a platform to pair with (or replace) a traditional learning management system and allow students to visualize their own progress and plan for their future. You can see a short video about GradeCraft and how it supports gameful instruction here. Follow us at @gradecraft.

We’ve spent the last three years growing the pedagogy and GradeCraft in parallel, iteratively designing GradeCraft to support the best practices we’re discovering in the classroom. This work has been developed in partnership with DEI and the Learning Analytics Task Force. Now we’re thrilled to announce that, thanks to the generous support of the University of Michigan Third Century Fund, we have the opportunity to continue to grow the gameful learning community here at Michigan, and make GradeCraft available to all.