I recently completed the OMSCS course on Education Technology and found it to be one of the most innovative courses I’ve taken. There is no pre-defined curriculum and syllabus, though there are many videos and materials available. Learners have the autonomy and freedom to view the course videos and materials in any order and at their own pace. The course is focused around a big project, and learners pick up the necessary knowledge and skills as they progress on the project.
Here’s my thoughts on the course for those who are looking to enrol as well.
One question I’ve asked myself (and close friends): “What do you think humanity needs most?” For Bill Gates, it was personal computing. For Elon Musk, it’s becoming a multi-planet species and clean vehicles and energy. Personally, my goals are not as lofty—I believe that humanity needs healthcare and education most. This belief, and the availability of these electives, was one of the key reasons I enrolled in OMSCS. Thus, I was elated to get a spot at the immensely popular EdTech course.
There are many rave reviews on how David Joyner is an excellent professor. His courses (i.e., human computer interaction, knowledge-based AI, and education technology) have great reviews and are notable for their rigour and educational value. He is also a strong proponent of scaling education (which I believe is one of the key approaches to improving education). Here’s his recent paper on scaling Computer Science education.
Being keenly interested on how I could use technology (and perhaps data science) to improve education and learning outcomes, I enrolled for the course in Summer 2018.
If you’re looking for a traditional post-graduate level course, you’ll not find it here. There is a surprising lack of obvious structure and step-by-step instructions. For some learners, they found this to be disorienting (initially), with some people getting lost along the way. For others, they found the course structure (wait, didn’t you say there’s no structure?) to be refreshing, allowing them to direct their focus and effort more effectively and learn more.
There’s no structure? What do you mean?
For a start, there are no weekly lectures. There is also no weekly reading list. Right from week 1, you’re immersed in the deep end. Your first assignment requires you to pick a few projects of interest, out of hundreds, and discuss them in an essay. There is a rich repository of curated videos, articles, and papers available from the first week, and you can view all of them in week 1, or none by the end of the course. This can feel like too much freedom for some learners, and slightly overwhelming.
In the next few weeks, students are then asked to think about which track they wish to pursue—development, research, or content. You’ll then conduct a literature review of your proposed track and topic, to understand better about past, related work, and identify your area of contribution.
Personally, I’ve had some experience with research (in college and at work) and development, but not so much in content. Thus, I decided to challenge myself and take on the content track, eventually producing a course on Udemy—more on this later.
Midway through the course, you also get an individualised qualifier question from your mentor. This is based on the track and topic you suggested, and your literature review. Again, this pushes you to do further research and learn about areas you’ve not previously considered.
Do you see the pattern yet?
While there’s no traditional structure (e.g., weekly lectures and readings), there is an overall structure that guides students towards learning. There are weekly assignments, where in order to accomplish them, you’ll have to do your own learning. These weekly assignments are in the form of short essays which help to document your learning and progress. In the process of these weekly writing exercises, and building your final deliverable, you’re learning a lot, in a very focused area—i.e., whatever you need to do to deliver your final project.
Such a (teaching and) learning approach is sometimes referred to as project-based constructionism, which has been found to be very helpful in helping students learn. This is the same approach I adopted for my Udemy course.
With regard to the project itself, students can choose from multiple tracks below, each of them interesting and challenging in their own aspects.
Picking a project thesis that you’re really passionate about is very important, and will help with pushing past the finish line. There were a few instances where I wanted to stop due to burn out from content creation (i.e., slides, scripts, videos). I also doubted if the content I was producing was actually going to be helpful, as it felt like common sense to me. Thankfully, with the topic that I believed in, as well as the pressure of the course, I managed to push through and publish the course on Udemy.
The written submissions (i.e., assignments, qualifier questions, proposal, status checks, final paper, etc.) account for 40% of the overall grade, while the project accounted for 45% (i.e., final deliverable and intermediate milestones). Class participation accounted for the remaining 15% and is gained by providing feedback on other students’ assignments and contributing on the forums.
The instructors were fantastic in this course and were a key reason for the course’s success and effectiveness. Prof Joyner and the TAs were fantastic in providing guidance. Prof Joyner himself was very active on the forums and responsive on important questions. The TAs provided a lot of helpful guidance on how to better structure our papers and projects.
Given that my track was the content track, the learning was mostly focused on online education and course development. To emphasize, this is the amazing aspect of the course where students can choose to focus on their topic of choice in great depth, instead of just breadth across multiple topics.
As part of my research on existing data science education, I found that many courses focus exclusively on the technical aspects and neglect other skills that are key to being effective in data science.
In addition, I researched and learnt a lot about adult learning theory. Adults are different from the traditional college students and have different needs. Therefore, education for adults has to be structured differently.
Also, I got familiar with creating curriculum for online learning, which is very different from a traditional face-to-face lectures. For example, while a traditional lecture is between one to three hours in length, studies have found that the ideal online lecture length is six minutes.
In addition, I had to quickly learn how to film and edit course videos efficiently. For my first video (which was about five minutes in length), it took approximately 3 hours to film and edit—this was a 36x multiplier on time! Eventually, I learnt how to reduce the time required to film and edit to about 5 - 10x of the published video length. Given that I produced more than two hours of lectures in total, this required between 18 - 25 hours for filming and editing. Researching and creating the course outlines, content, etc., and setting it up on Udemy took another 80 - 100 hours.
Lastly, and what I found most valuable, was the refresher on how to write an academic paper. While I had experience writing academic papers back in college, it was not something I had done recently. Prof Joyner and my TA mentor were helpful in providing guidance on how to write a rigorous academic paper. Unexpectedly, across all the grades I received, I did the best on the written component. Perhaps all the practice with writing (e.g., this site, past papers, newsletters at work) had paid off. I’ll be looking to share the paper at an education-based conference soon.
After months of intense, hard work, here’s my labour of love, an online course on Effective Data Science.
Why did I create this course?
As a data science lead, I’m often approached by others on questions related to data science. Through these interactions, I realised there is significant misunderstanding about data science, especially on how to be effective in the field.
There’s the perception that deep technical and programming abilities, olympiad level math skills, and a PhD are required, and will guarantee success. However, my years of experience, first as a data scientist, then as a data science lead suggests otherwise. I’ve also interviewed numerous experts, people who are Chief Data Scientists, CTOs, Heads of Data, that do not agree with the flawed perception.
While a minimum level of technical competency is required, past that threshold, deeper technical abilities are not directly or strongly correlated with effectiveness.
What is it that matters then?
I’ve found that effective data scientists have a similar set of skills that help them create impact and deliver measurable results. Unfortunately, these set of skills are often neglected in both traditional education and online courses. Thus, I decided to create this course on Effective Data Science.
I’ll be the first to admit that the course did not turn out as expected. I had hoped it would be high energy and humorous. But it turned out to be slightly bland with slides and voiceovers. Filming really takes a lot of energy!
One idea is to add interviews with industry experts in future iterations of this course, as well as add more interactive activities.
If you’re interested in trying out the course, you can do so via this link which allows you to access the course at 10% of the original price!
I hope to continue building on the content that’s been developed and share it as widely as possible, perhaps via a series of articles, or a short publication.
In addition, I aim to share about my paper on scaling data science education at an education conference soon.
Questions? Want to follow my journey? Reach out on Twitter @eugeneyan!
I write about 🚀Effective Data Science, 📚Learning, and 📈Career. Get weekly updates.
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