Over the past year I’ve helped create and run and few online courses. Some have reached tens of thousands of people, and some were aimed at only a dozen. Of everything I achieved in 2012 (finishing a PhD, starting a business, cycling across Alaska and Haiti), it was the standout for me as being the least likely source of a sense of achievement, so I’m sharing my thoughts on the experience.
Figure 1: Interpretive dance predicting the future of education.
In 2010 I ran into Stanford Professor Andrew Ng at a tech camp. He was an already famous academic in artificial intelligence circles, but was talking about a new focus on online education. He asked if I was interested in helping. I said yes, but I never followed up on the invitation. To be honest, I didn’t see it: why would the leading researcher in their field completely change their direction? Wouldn’t we have solved online education by now if it was a viable game changer? One year and more then 70,000 students registered later, Andrew’s inaugural online Machine Learning class had proved the demand.
So when the opportunity came again in late 2011 I didn’t let it pass. By then, the production of Massive Open Online Courses (MOOCs) had spread (to neighboring offices at least) and my then PhD advisors Chris Manning and Dan Jurafsky were creating online courses on Natural Language Processing and looking for help. I volunteered to help out my colleagues and to share our knowledge, but I also just wanted a better understanding what this movement in education was all about (Note: I was among the least involved. The main creators were Chris and Dan themselves, Jane Manning, Adam Vogel, John Lyman, and probably many others!).
Figure 2: The first lecture! Introduction to Natural Language Processing
I also helped launch a new course at Tulane’s Disaster Resilience Leadership Academy: ‘Crisis Informatics and Analytics’. I ran the first month, filling in for Jeannie Stamberger in a program coordinated by Ky Luu. It was at the smaller of the spectrum: I flew to New Orleans to run the first week in person and then delivered the following weeks using a combination of online lectures and a direct stream with the class.
Here are a few observations from both experiences:
1. It’s not that different to existing education
The NLP courses were more or less the same as the Stanford-internal courses that I had taken or taught. The inclusion of quizzes was a nice addition. For a real-time lecture, it’s not really feasible to have pop-quizzes when there are a large number of people, and certainly not in a way to make sure that everyone has understood the material so far.
It is obvious that there is an advantage to self-paced learning via online courses, but I think the ability for the educators to pace the learning has been under-appreciated. You can more easily enforce that someone doesn’t skip ahead then you could in-person.
2. YouTube will win where Flash failed
For a long time, Flash was predicted to begin taking over from HTML as the preferred format for many websites, primarily because of the greater richness in interaction and possibility for animation. This never really happened. Flash pages tended to be poorly indexed by search engines (mainly for technical reasons) which slowed the uptake, with the final blow being the iPhone browser’s deliberate lack of support. Our pages remained largely static.
It looks like we are finally getting this promise fulfilled, but that video will be the medium for online animation and interaction. Where Flash failed to add dynamic content to webpages, YouTube is now successfully adding static and interactive content to video.
At the moment it hasn’t really progressed beyond the quizzes mentioned above and links to further content, but it is likely to become more sophisticated as we become more comfortable with this new approach.
As someone currently looking to recruit top engineers, I like the potential to find people through these platforms. This may be a good revenue model for the education systems and some of the online education platforms are already exploring the option of charging employers for access to student data.
If I was considering hiring an engineer that did not have a background in Natural Language Processing, I would now expect them to take this course in preparation, to demonstrate both their willingness and competence. For engineers themselves, it is a convenient way to begin to re-skill in new areas.
Online education systems don’t need to be massive. Reaching tens of thousands of people is a big step, but so is allowing for more specialized courses.
The ‘Crisis Informatics and Analytics’ course at Tulane was a great example. I was teaching only a dozen students, most of them completing a Masters in disaster management. There aren’t many people who could teach these courses: I am the only person in crowdsourcing and Natural Language Processing circles who has worked in all three of academia, industry and disaster response. As a result there are very few quality papers on either of these topics that are useful for students, and many misleading ones clearly aimed at funding agencies. While I couldn’t physically relocate to teach the course, I really enjoyed the opportunity to connect with the incoming generation of disaster response professionals and to teach them how to objectively evaluate humanitarian technologies.
In general, the internet has been a great place for small, distributed groups of like-minded people. It looks like this will be true for education, too.
5. Reference materials
I found myself using one of the Stanford NLP videos recently:
Figure 3: Good Turing
I was implementing smoothing algorithms and wanted to double-check that I was getting one of them correct. The Stanford NLP video had everything I needed: the formal definition of the functions and an audio explanation. Running this in a screen alongside my code was more convenient than online text or an offline textbook.
As with the course at Tulane, this topic is pretty specialized (the video only has 600 views at this time), but it will also remain current as the algorithm itself will remain useful for a long time.
One prediction for the future
It wouldn’t be fun to write about this without making at least one prediction about the future of online education, so here is mine:
- Our future educators will be robots
Or perhaps more precisely, artificial intelligence will play a central role in online education.
Along with Andrew Ng, Coursera was launched by Daphne Koller who is also a leading academic in machine learning. Coursera’s main competitor from within Stanford is Udacity, launched by another artificial intelligence researcher Sebastian Thrun (and first co-taught with Terry Winograd who also began his career in Natural Language Processing).
Outside of Natural Language Processing, I have also worked in artificial intelligence applied to geographic information systems (my 2003 paper, ‘Complex Spatial Relationships’ now forms the basis of the entry with the same name in Encyclopedia of GIS). Our work stemmed from Rakesh Agrawal’s seminal ‘Fast algorithms for mining association rules’.
I was delighted to finally meet Rakesh this year, but not in a strictly artificial intelligence context. He is also now working in education, researching how digital education can be augmented by automated information retrieval (among other education-focussed projects at Microsoft Research).
We are at a tipping point where it will become cheaper to give every student a reading tablet than to ship physical textbooks to them. Once we are working from purely digital media, we all of a sudden have the ability to automatically extend the content through video, further text, or interactive environments with other students and smart systems. It is exciting to see so many of the best minds in artificial intelligence now applying themselves to this problem.
In short, the people leading the way in online education are also the world’s leaders in artificial intelligence. This makes for a nice symmetry. The Natural Language Processing courses help bring these technologies to a wider audience. In turn, the same technologies are now being applied to online education itself. So the intersection of artificial intelligence and education is where I will be watching (and continuing to help) with the most interest.
December 31, 2012