Recently, technology has been coming under heavy fire for breeding the “millennial” – always on their phone checking Snap-this or Face-that, too heavily involved with social media to go out and actually be social. The internet is by far the most effective means of transferring instant information to individuals; and while it is an incredible resource, it has caused instant gratification to become standard, dulling down the patience of many for learning and fully understanding. However, there still is a glimmer of hope for online learning – enter the MOOC.
Massive open online courses (MOOC’s for short) are full, university courses that are available online, most of their materials being completely free of charge. The big name providers of MOOC’s include Udacity, Coursera, along with university sponsored programs such as MIT OpenCourseWare. These courses include video lectures, additional classroom materials, and even assignments that test the material being taught. Although timelines for completing given courses are suggested, only a single final deadline is set, so everything is self-paced and can be submitted any time before then. For the more serious student, these courses also offer certificate options that can be purchased as a verification of the subject being understood. Given the breadth and depth that these courses cover, along with the fact that they are usually sponsored by the best universities (Stanford, MIT, Harvard) and institutions in the world (Google), certificates in these courses are quite impressive. So, how effective are these programs?
I hopped on the bandwagon of a friend’s recommendation and spent a few weeks working through Coursera’s Machine Learning course, taught by Andrew Ng of Stanford. Interestingly, Coursera was founded by Andrew Ng and others to provide a platform for offer the course to individuals interested in the subject. What I found so great about Coursera’s program is that it saddled the precarious ridge of being too technical or too watered down. The material was presented in a logical sequence, motivating and building upon itself, and always keeping the information just far enough out of reach that I was not discouraged but still had to work to understand. Professor Ng is very clear in his explanation of topics, and always relates then back to practical applications. Perhaps the greatest aspect of the course was the assignments that accompanied the lecture materials. I never once had to worry about setting up dependencies or fuss over issues that were not related to coding the specific material was working on – this cannot be said about starting off on your own or in a classroom… Instead, project requirements were clearly outlined, and worked directly with the videos describing given topics. Also, instantaneous grading of the assignments kept feedback streamlined (although not always as clear as if a TA or professor were going over a problem set), and made the whole process almost like a game, each new lesson a new level that I was determined to beat.
Overall, I finished the course with a substantial grasp on a field that I thought was magic just a few weeks ago. Of course, this was only an introductory course into the field of machine learning, but it has definitely opened my mind to its widespread applications. More importantly, this experience has made me ask the question – could an online education eventually equal that of a university? Even perhaps surpass it? I do not know the answer to the question, but from the practical skills and understanding that I have gained in a short while with my first MOOC, I will definitely be searching out other opportunities for online learning in the future.