I’ve won the College of Computing Outstanding Graduate Teaching Assistant Award

This was announced a few weeks ago, but since I received the award itself today, I figured I’d wait until now to announce it here: I’ve been selected as the recipient of the 2015 Georgia Tech College of Computing Outstanding Graduate Teaching Assistant Award for my work on the Fall 2014 Knowledge-Based AI class in the OMSCS. This post is half news post, half blog post.

First of all, I’m grateful to Ashok, the professor for the course (and my PhD adviser) and to the students in the course for their unbelievably kind words during the nominations. Thank you as well to the College of Computing for ultimately selecting me.

This experience has inspired a number of thoughts in my mind, though, that I hope to explore in the coming weeks. One major question I have is: is the role of the teaching assistant more significant in online classes than in on-campus classes? So far, the key determining factor I’ve observed in how well a class is run in the OMS is how involved the head TA is. I could see how that may be true in on-campus classes as well, but in an online class, an enormous responsibility falls on the TA to manually recreate elements of the class experience that happen naturally on-campus. Do we need to re-explore how we choose head TAs based on this potential increased importance? Do we need to reconsider what the responsibilities of a head TA in an online class even ought to be? Do we need to re-examine the qualifications a head TA for an online class ought to have?

The second question I have is a little arrogant, but I’ll mention it anyway. It’s not surprising that I was a good TA in Fall: I have ten years of teaching experience, ten years of experience interacting on online forums (an underrated skill in this setting), ten years of experience constructing assessments in other domains. On top of that, I had more time to devote to TAing the class than most TAs have: my dissertation work was done, and my primary responsibility for the semester was TAing the class. Under those circumstances, it’s no surprise that I did a pretty good job.

We can’t hire someone like me to be a TA every semester. The skillset I just described commands a high salary. Even if it didn’t, simply finding people with that skillset is difficult. Finding 30 people with that skillset, each to run a class? That’s absolutely impossible. We can’t just have a bunch of me TAing every course every semester. So the question then becomes: how do we replicate some of what went well in our course in classes that don’t have the same resources?

That second question is what I’ve been exploring in my new role working with all the classes. That second question is also what I hope to explore soon in this space. Hopefully I’ll catch BuzzFeed’s eye with “10 Mind-Blowing Tricks Your TA Should Use In Your Class!”

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Taking stock of the MOOCs landscape: past, present, future

I found myself thinking out loud about the evolution of the MOOC landscape so far. To get feedback and share my view on the past, present, and future of the landscape, I’ll share this.

In the beginning, MOOCs were focused on openness: make the content available and the people will come. And the people did come, in thousands and hundreds of thousands, but we quickly found that the experience in these MOOCs was not actually replacing the experience of the in-person class. No one would argue that taking a MOOC was actually equivalent to taking the comparable college course.

So, MOOCs evolved. Some of the directions in which they reinvented themselves were aimed more at differentiation: MOOCs became shorter, less directly analogous to comparable college classes, more varied in the range of topics covered, and more specific in the number of topics covered in any one MOOC. As an analogy, instead of a poetry MOOC, we’d have a series of MOOCs each focused on individual poets or movements in poetry. These changes were driven by market demand, even though they took MOOCs away from their original position as open-access college courses.

However, other MOOCs evolved in the opposite direction. In other places, MOOCs recognized the difference between traditional college classes and MOOCs, and they moved to rectify these differences. I, personally, would identify three key areas in which the early MOOCs differed from traditional classes: feedback, accreditation, and context. A major criticism of MOOCs has been the lack of interaction. It’s been suggested that MOOCs are fine for autodidacts, but the majority of learners need feedback to improve; the learning sciences supports this as well. Students also do not take college courses simply to learn; the valuable diploma or credential at the end of the journey is one, if not the, major motivating factor behind participation in traditional college. Students also rarely take classes in vacuums: each class builds on previous classes, and in turn becomes the foundation for future classes.

Initially, MOOCs missed these elements of traditional college courses. There was little feedback due to the difficulties in scaling personalized feedback. MOOCs were meaningless on resumes and in portfolios; anyone could claim to have completed any MOOC, and there was little knowledge as to what completing a particular MOOC actually meant. MOOCs were mostly one-off courses, absent the context of a broader unit.

In contrast to the differentiation mentioned previously, MOOCs have also evolved to address these initial weaknesses. This is clearly evident in three of the most prominent MOOC providers: Coursera, edX, and Udacity (note my prior disclaimer).

Coursera’s Verified Certificates and specializations address all three of these issues. A Verified Certificate carries with it an assertion that the work was, in fact, completed by the individual, mimicking the need for a form of accreditation. Specializations are series of courses developed to provide a broader view of a particular topic, injecting the context that is missing from one-off courses. Specializations culminate in a capstone project, where (I presume; I haven’t completed one yet) the student receives the feedback that is lacking in traditional MOOCs.

Udacity’s Nanodegree credentials are similar. A human coach interacts with the student, interviewing them live on the skills they have obtained, allowing the assertion that the individual receiving the credential really mastered the skills. Nanodegrees focus on jobs’ entire skillsets rather than individual skills, providing critical context. Throughout the program, students receive feedback from coaches, peers, and external code reviewers on their work, allowing for a true iterative feedback and refinement cycle that one would expect in a traditional classroom.

Of course, Coursera and Udacity differ in critical ways. Coursera maintains (in my opinion) openness as the guiding principle; Verified Certificates are kept relatively cheap ($50 per course at present, leading to ~$300 for a specialization depending on the number of courses). Although these changes inch toward the functions of traditional college courses, they remain distant: the identity verification is easy to fool, and the amount of feedback still pales in comparison to traditional courses. Udacity moves to more closely address those traditional college functions: identity is asserted through real-time face-to-face interaction with a coach rather than an automatic system, and feedback is available constantly from coaches, peers, and external reviewers (both of which connect to the higher cost of a Nanodegree credential than a Coursera specialization). However, the underlying nature of the moves is the same: both have moved to provide a form of accreditation (or at least identity verification), individualized feedback, and broader context.

These changes should not be overly surprising. MOOCs have followed a traditional Gartner hype cycle: the initial hype was overblown, but MOOCs are starting to find their place. I would argue we’re at the beginning of the slope of enlightenment with regard to MOOCs, well on our way to the plateau of productivity. Some might argue we’re further along; students are already getting jobs based on completion of these online programs, which can certainly be argued to be indicative of a productive industry.

All that begs the question: what’s next? What’s next for the MOOC landscape? For me, the elephant in the room goes back to accreditation. Academic honesty is a major problem for traditional education, and it is exacerbated in online education. When you can’t see your student, how do you guarantee the student is, indeed, responsible for the work? Coursera addresses this through pictures after each assessment and keyboard pattern matching, but these only guarantee that the student was at the computer when the work was submitted. It’s trivial to have a friend send you answers. Until this can be resolved, I do not foresee Coursera’s certificates having much value in the market. Udacity’s measures are effectively impossible to circumvent, but they present a challenge for scale: there is a linear relationship between the number of students and the amount of employee time necessary to verify them.

Accreditation is not as simple as identity verification, however. Even if I knew that a person I was interviewing had personally completed a particular Coursera course, I still would likely not put much stock in it. The majority of Coursera courses I’ve taken can be passed without learning anything simply by gaming the quizzes and assessments. Accreditation of these courses and programs needs to not only focus on identity verification, but also the strength of the material and the accompanying assessments. What can we actually assert that a student completing a given class has learned? Until that element of accreditation comes along, I do not foresee the simply identity verification approach as sufficient to make these credentials worthwhile. (Sidenote: I do not mean the above as a general critique of Coursera; as far as learning is concerned, I appreciate that iterative improvement on assessments is permitted, and it connects strongly to Coursera’s emphasis on openness and accessibility.)

In the absence of accreditation, validity can be built in other ways. Graduates from the Nanodegree program at Udacity, for example, do not need to rely solely on the value of the program name because they also leave with a portfolio of projects demonstrating their knowledge. Some Coursera specializations mimic this as well, and it is possible that reputations may build up organically over time based on the strength of past program graduates. However, I feel there is a more efficient possibility for MOOCs and other online classes to go through a true accreditation process to verify the value and reliability of a given course. With such a process, demand for these credentials would rise as their value on a resume or application would rise as well. This would also open up demand for MOOCs to much broader populations: schools and universities could supplement their course catalogs with accredited MOOCs, entire degree programs could be constructed based on MOOCs from numerous different universities, and current classes could benefit from global audiences working in tandem with traditional students. In my opinion, accreditation is the chasm standing between the present state of MOOCs and the promise of MOOCs.

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Disclaimer: I Work for Udacity

Disclaimer: I work for Udacity. I have to give that disclaimer in many things I write because I’m writing about things very core to Udacity’s business. There’s the risk for a major conflict of interests. You’d be entirely within your rights to dismiss anything good I say about Udacity as mere self-preservation.

So, I want to make something perfectly clear: I don’t believe in Udacity because I work for Udacity. I work for Udacity because I believe in Udacity.

When I started work with Udacity a little over a year ago, I came aboard initially very specifically to work on our Knowledge-Based AI class for the Georgia Tech OMSCS. The true “development” phase of the class ended in December of 2014 with the end of the first offering. At that point, it would have been entirely natural for me to leave Udacity: the project I joined to work on was done, and so it was time to move on to other things. The timing would have been perfect, frankly: I defended my dissertation four days after the course ended. If there was ever a time to switch, that was it.

Truth be told, when I joined Udacity, that was probably the closest thing I had to a “plan”. I honestly didn’t have a real full plan, but I know that I wasn’t looking past the Knowledge-Based AI course at first. I knew I’d have options at the end of my PhD, and I already had more than a couple connections waiting for me to graduate.

Udacity, however, won me over. Never before in my life had I been so consistently surrounded with people that shared my passion for education. I had never seen such a group that was so obsessed with not only educating people, but with learning how to educate people. Outside academia, I had never seen a group of people so invested in the science side of education, in experimenting and testing and failing and learning. Perhaps most significantly, I had never seen a group of educators who so adamantly loved their students. I don’t mean ‘love’ as in simply enjoyed; I mean ‘love’ in the truest sense of the word, a group that earnestly wanted to improve the lives of their students.

It’s for that reason that I say: I work for Udacity because I believe in Udacity, not the other way around. There are lots of other reasons I believe in Udacity, from the amazing corporate culture to the relevance of the OMS and Nanodegree programs in addressing the cost of higher education to the opportunity to work with a student body that I love as well, but it all starts with the above. As an educator, I cannot imagine a more perfect place to work (setting aside the fact that my ultimate passion is K-12 education, not college education).

Of course, you don’t have to believe any of this. The value of much of what I write in this blog depends on me being earnest, and this is exactly the kind of thing I’d write to convince you I’m earnest. I hope I’ve at least convinced you to give me a chance, however.

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