This post is in conjunction with the EdX course Data, Analytics, and Learning.
This week I am jumping back on the MOOC bandwagon by starting the Data, Analytics, and Learning course presented by George Siemens and others. I’ve been out of the MOOC space for a while as I’ve been concentrating on my coursework, but as my research interests have drifted towards measurement and learning analytics, I’ve really felt compelled to engage in some more formal coursework. While I understand the basic concepts of what Learning Analytics is trying to achieve, I’ve often felt frustrated that the methodology is out of reach for most people in instructional technology. It seemed that most early researchers in the field (especially educational data mining) are coming from the world of computer science and artificial intelligence. How can those who are trained in instructional theory use these often highly technical tools to fulfill the purposes of learning analytics, which is to provide information to instructors and administrators about the process of learning in a holistic context? My hope is that will provide some specific methodologies to spark ideas for implementation of learning analytics projects in my own research agenda.
As far as the structure of the course, I like the experimentation with the multi-layered MOOC. It seems like an interesting way to compensate for the problems that may come with each type of MOOC. While I’m all for social learning, my school and research schedule will probably not allow me to be involved in the class as much as I want, so currently I’m planning on following the more content driven path. If time allows, I would love to use the social tools to connect with other researchers that are engaged in LA, but I will have to see how the course progresses.
Well all, here’s to a great few months. I’m looking forward to sharing the discoveries that I find in the course.