Connectivism is a learning theory first introduced in 2005, by two separate academics: Siemens [Sie18] and Downes [Dow05]. Rooted in constructivism, connectivism is “a learning theory for the digital age” that emphasizes the fact that in the 21st century, much knowledge is externalized from human minds, in the form of the internet (and more recently, artificial intelligence, or AI). In our hyperconnected world, there is less emphasis on, or need for, individuals to remember specific facts or procedures, because there are huge amounts of information readily accessible when the knowledge is needed. As well, information is increasingly vast, complex, and changing. So learning becomes not just learning and remembering facts, but learning how to use specialized online knowledge bases, and “connect” information between them.

This is very true in data science. Practitioners rarely know all the details of how to use a particular programming language — the names of every possible command, or how to use them. Instead, data scientists rely on the documentation for these programming languages on the internet. This includes the official documentation, questions and answers posted on help forums such as Stack Exchange, written tutorials, YouTube videos, books, and more. Figuring out how to do something new is virtually a daily occurrence when working in data science, and so the ability to know how to find and evaluate the necessary information — and connect it across sources to solve your problem — is just as important as one’s existing coding skills.

While this knowledge is externalized in digital technology, it is, ultimately, the product of human knowledge and human effort. Thus, like constructivism, connectivism emphasizes the importance of social interaction in learning — but this social interaction may be asynchronous, such as when one person records a YouTube tutorial and someone else watches it months later.

Connectivism informs the mindset you should bring to this course#

There is little emphasis on memorizing information, except to the extend that knowledge becomes more ingrained as you use it. Instead, the course emphasizes an attitude of continuous improvement and “life hacking”, built on skills of properly understanding and characterizing a problem, doing the appropriate searches to find the necessary information to solve the problem, and then applying that information to deliver the solution. In doing so, you must be a critical evaluator of the information you are finding (since not all information on the internet is created equal). As well, this course encourages and rewards students for contributing to the class knowledge base, through demos, peer teaching, peer assessments, and team projects.