"In the process of learning to code, people learn many other things. They are not just learning to code, they are coding to learn," Mitchel Resnick, professor at the Massachusetts Institute of Technology (MIT) Media Lab, wrote in an EdSurge article. "In addition to learning mathematical and computational ideas (such as variables and conditionals), they are also learning strategies for solving problems, designing projects, and communicating ideas." Resnick adds that these skills are useful to everyone "regardless of age, background, interests, or occupation."
We present in this paper an innovative solution to the challenge of building effective educational technologies that offer tailored instruction to each individual learner. The proposed solution in the form of a conversational intelligent tutoring system, called DeepTutor, has been developed as a web application that is accessible 24/7 through a browser from any device connected to the Internet. The success of several large scale experiments with high-school students using DeepTutor is a solid proof that conversational intelligent tutoring at scale over the web is possible.
It's a topic I touched upon recently, and looked at the potential for digital platforms such as Coursera to provide a low-cost means of regularly brushing up our skills and adapting to changes in the marketplace. Sadly, despite thousands of students enrolling on these courses, neither the Department of Work & Pensions or the Department of Education seemed to know what a MOOC was, much less were they being actively used to help people re-train when their livelihoods had been disrupted.
This is based on Andrew Ng's popular machine learning class on Coursera. I didn't see anyone documenting the solutions in R, so I wanted to share mine. Hopefully some people will find this resource helpful. Note that you won't be able to take this class for credit if you use R instead. Assignments are submitted via a Matlab script that references other Matlab files, which I've obviously not completed.