This summer I had the opportunity to work with Dr. Anthony Robinson to map MOOC students. Anthony wanted to know more about the geographic patterns of MOOC students in his Maps and the Geospatial Revolution class. We wanted to know such things as: Where are the students? Where are the highly engaged students? Where are the non-English speaking students? Where are students who are employed/unemployed? Are there geographic patterns to the gender of MOOC students?
The goal was to answer all these questions with maps. We wanted to see if we could show the all of the geographic patterns instead of simply saying something like, “X% of students come from the United States”. As cartographers, we believe geographic information is often at its best when visualized on a map.
What’s a MOOC?
It’s a Massive Open Online Course. Anyone can take these courses for free. Check out Coursera (one of the many MOOC providers) to see what you can learn from college professors without putting on pants or paying a dime.
Anthony’s MOOC is a basic introduction to maps and cartography. He keeps it simple for a novices and it has been wildly successful. Over 100,000 people have taken his class in one of the three offerings. That means over 100,000 people around the world are interested in learning about cartography. See guys, maps are cool!
The interesting thing about the students in this type of learning environment is they can technically be from anywhere. All you need is an Internet connection which is easier to come by than ever. We have even seen students who used satellite connections to learn about maps. One cool thing about Anthony’s MOOC is it did not require students to watch the videos, although you are sorely missing out on Anthony’s dry sense of humor without them – the course is designed so students in places with less then perfect internet can still pass the class.
Data, data, data
Coursera, the course provider of Anthony’s class, keeps track of the students who take the MOOC. Researchers can then use this data for multitudes of different purposes. Coursera records the quizzes, page views, forum posts, and video views of each student. Students are also encouraged to take a voluntary survey at the beginning of the class which takes account of their age, gender, occupation, etc. In addition, Anthony had the IP addresses of his students geocoded so we could have detailed locations for everyone who took the class. Combined, we had a wealth of data on the tens of thousands of students all over the globe who had signed up for this particular MOOC.
Mapping this type of data is tough. First, you have to decide on a projection. Since a large portion of the students live in the northern hemisphere you don’t want a projection that distorts that area too much. Anthony was a big proponent of the Goode projection which we both felt reminded us of a 1970s pull-down classroom map. It is equal area, which let us make hexbin maps, and amazingly doesn’t distort most of the world too badly. Goode is Good despite its reminder of burnt orange, brown, and bubble lettering. We hope you like our updated sleek 21st century black, grey, and neon maps with the retro projection.
OMG it’s Hexbins guys!
We also really wanted hexbin maps because they are all the rage in cartography right now. Luckily Sterling had already made the hexbins for this project and was kind enough to share with me. As stated above, you should really have an equal area projection when you make a hexbin map because otherwise it totally defeats the purpose of having bins of the same size. Your GIS can help you find an equal area projection, but unfortunately there only a few good ones to choose from especially for mapping MOOC data, where we need to show the whole world at a time.
I found in most cases I really liked looking at the basic point maps. Ask a question, e.g. “Where are the people who completed at least one assignment?” Write a query in your GIS and you are done. No need for complicated interpretation or making a legend. These maps are quick and to the point. Literally.
Engagement Index Map
Anthony was insistent upon making a map of an engagement index. Basically this involved figuring out what engagement in a MOOC meant. Since I actually was enrolled in Anthony’s MOOC all three times it was offered, you would think I would know what that means, except I have never gotten past the second week and have failed the course three times. Let’s just say I wasn’t very engaged.
So what did we count into our engagement index?
- Video views
- Forum posts
- Page views
Using quantiles, I broke each of the categories up into bins. For instance, if you had less than three video views, you were categorized as 1 in the video view category. Between 3-5 video views gave you a 2. Six to 13 video views gave you a 3, and more than 14 video views gave you a 4. I did this for all of the categories (video views, forum posts, and page views), added them up, and that gave us the engagement index, which ranged from 0 to 16. The pattern of engagement is quite interesting.
Bivariate Hexbin Map, now we’re talkin’!
We thought it would be cool to know where people were engaged and where people passed the course. This is most easily done with a bivariate map. Josh Stevens wrote a very informative blog about this a couple months ago. You’ll notice some very interesting geographic patterns in this bivariate map. Three places to focus on here: the US, Europe, and India.
Non-English Forum Posts
We also had data on who was making forum posts, who was making non-English forum posts, and how many posts these people were making. I made a simple proportional symbol map of just that.
It has been known for some time that the majority of MOOC students are male, but we really wanted to know whether this varied spatially. Turns out it does. In the US, more than other places, there is less of a disparity between the genders, however that even varies within the United States. The gender ratio map versus a map of men and a map of women is great for showing where things are more or less equal. You can compare it to the individual point maps of men and women in the class. Which map do you like best? What interesting patterns do you see with gender in these maps?
Finally, the first offering of the maps MOOC took place in 2013, during the height of some serious economic struggles for many people, especially in places like Spain and Greece. We wanted to see the patterns of MOOC students’ gender and employment status. These two maps are pretty interesting. We mapped employed vs. unemployed men and women. I think Europe is the most interesting especially in Eastern Europe and Spain. There are many more unemployed women taking the class than men especially compared to the employed cohort of each gender. The US has some similar patterns. What do you think might be the cause?
Anthony has been a close friend and mentor for many years, and I owe him a debt of gratitude for how much he has helped me navigate my career; however, we have never actually worked directly together unless you count taking Intro to Cartography with him in 2007. It was the most nerve wracking professional experience of my life. This person thinks you are awesome, and the fear of letting them down is immense. It ended up being fun to get back to my roots with the person who inspired me to become a cartographer in the first place.