MOOC-Raking, Part I: Intro and Quality of Material

(Apparently one pronounces the acronym for Massive Online Open Course “mook.” I did not know this until hearing them discussed at a teaching workshop on distance learning.)

As a post-secondary instructor and someone who loves taking classes, I’ve been fascinated by the MOOC model of learning, in which universities and experts from around the world offer free versions of their most celebrated courses to anyone on the Internet who can login and click “sign me up.” At first glance, this seemed wonderful! Not only can I learn about all the subjects I wished I had time to take in undergrad, but I can check out the pedagogy of some of the world’s most praised professors and learn what works and what doesn’t.

It’s a bit disingenuous to say “at first glance,” because I still do really enjoy the MOOCs in which I’m participating. I’m taking four courses at the moment, and I intend to keep on taking more throughout the summer. But as I complete more and more MOOCs, I’m starting to learn more about the format as a whole and what I need as a MOOC learner to facilitate my success in a course.

Obviously, I wrote this blog entry to muse on these things, but first I’d better let you know on what MOOCs I’m basing my thoughts:

(Although the number of organizations offering MOOCs is growing, I take MOOCs through Coursera, which is exactly the right format for me. I know I’m capable of learning things on my own, but that’s just the thing: without the deadlines of Coursera, I wouldn’t bother completing the material.)

Cryptography I (Stanford University) – COMPLETED
Game Theory (University of British Columbia) – COMPLETED
Learn to Program: The Fundamentals (University of Toronto) – COMPLETED
Design: Creation of Artifacts in Society (University of Pennsylvania) – COMPLETED
How Things Work 1 (University of Virginia) – COMPLETED
Introductory Human Physiology (Duke University) – IN PROGRESS
Learn to Program: Crafting Quality Code (University of Toronto) – IN PROGRESS
A Beginner’s Guide to Irrational Behaviour (Duke University) – IN PROGRESS
A History of the World Since 1300 (Princeton University) – ABANDONED
Introduction to Digital Sound Design (Emory University) – ABANDONED

The first thing you might ask is, why did I abandon the courses I abandoned? My own time restrictions were probably the most important contributing factor, but the major reason I chose to drop certain courses in particular was their instructors’ lecturing approach.

Even in bum-in-the-chair in-person courses, I’ve never enjoyed listening to lectures. I just don’t learn well from listening to other people talk; I’d much rather read. I spent most of my time in high school and university doing homework, writing stories, and/or solving crosswords during class because I felt too conscientious to skip (more on this later) but couldn’t bring myself to listen when I could ingest the material in a tenth of the time by reading the textbook.

MOOCs solve part of this problem for me because I can speed up the lecture speed using my video-player settings — practically a miracle. The other part gets solved because the lecturers whom universities choose to feature in MOOCs are often their most celebrated. Coursera lecturers tend to have lively presentation styles, engaging material, and casual, confident approaches.  However, when a lecturer’s style doesn’t match my needs, or when, due to the subject matter, I can’t speed up my videos and distort the audio, I drop the course.

The big question many have about MOOCs is: how do they compare to real-life not-free university courses?

Obviously, nine courses isn’t a respectable sample size. But speaking purely from personal experience, I think the amount and quality of material available through MOOCs can be equivalent, particularly for theory-based science courses, math courses, and computer science courses. However, the effectiveness and standards of evaluation are not.

Let me expand on the first part of that. The most successful MOOCs I’ve taken have been ones that have the goal of imparting knowledge. Their purpose is to expand what you know. Most of my science and math courses on Coursera appear to have this format. They ask students to understand and memorize facts, whether those facts are about algorithms, programming languages, human physiology, or mathematical concepts.

But for many reasons, MOOCs can’t replicate some of the most important tacit learning of real-life courses, science or not. Even most knowledge-based courses have a lab component. In first-year chemistry, we had to spend three hours each week doing experiments under the watchful eyes of our TAs. In psychology, we didn’t have to do our own lab work, but we could gain bonus points by participating in departmental studies as experimental subjects. Obviously, most of these group, hands-on activities have no equivalent in MOOCs.

This is particularly salient in humanities-based courses, which tend to evaluate students on academic skill sets and ways of thinking rather than facts and figures. To transmit these skills, traditional attendance-based university courses often include activities such as writing papers, giving live presentations, creating artistic projects, and  interacting with classmates (think, for example, of language courses that require students to practice their new speaking skills with one another). Although there are some humanities courses on Coursera, and although many try to replicate these experiences online, so far, none I’ve encountered seems successful.

First, some learning experiences just aren’t suited to online education. For instance, I was a Drama major in undergrad, and although I can picture the theory portion of our courses being available online, there’s no way, not with today’s technology, that MOOCs can replicate the most valuable parts of our program: working on a major production with other students, auditioning for shows, climbing way up a scaffold to hang lights, etc.

But more importantly, even with aspects like writing essays or researching papers that can be done on one’s own, evaluation is still a problem: the expert or team of experts running the course can have a computer evaluate multiple choice quizzes or numerical answers, where there’s a clearly correct answer and a clearly incorrect answer. Unfortunately, qualitative marking by the instructors is impossible with such a large group.

Which isn’t to say that many MOOCs don’t try to find a way around this bottleneck. But I’ll take that up in Part II.