More than any other time in human history, knowledge is both ubiquitous and accessible. The digital realm of cyberspace contains a vast universe of information comprised of incalculable bits of data. As Siemens (2004) stated, however, “knowledge that resides in a database needs to be connected with the right people in the right context in order to be classified as learning.”
In this age of digital and social networks, knowledge is everywhere. No longer must we buy into the “sage on a stage” notion of learning in which expert faculty pour knowledge into the empty vessels that are students. With the help of technology, we are moving from a teacher-centered paradigm to a learner-centered paradigm; from didactic instruction to collaborative knowledge construction.
Siemens (2012) argued that we must align “teaching and learning with the way in which information is created, negotiated, and shared through digital and social networks.” The challenge for educators is to leverage these networks in such a way that facilitates access to reliable sources of information while supporting knowledge creation and collaboration.
So how do we achieve this? Jose Ferreira, founder and CEO of Knewtoncognoscenti
Online education is on the cusp of massive change, and only 100 cognoscenti know about it – Jose FerreiraFerreira is talking about adaptive learning platforms. Knewton mines data, logging information about students’ behavior and performance (e.g. keystrokes, clickstreams
Arizona State University used the system for their remedial math courses and found that half of the students completed the course a month early, drop rates reduced from 13% to 6%, and pass rates increased from 66% to 75%. An intuitive dashboard allowed instructors to observe which students were struggling at a concept-by-concept level. The system even uses gaming mechanicsLearning Genome Project
Adaptive learning personalizes the learning experience. Drawing from a massive repository of information, an adaptive system can serve up what the student prefers and needs. For example, if the system recognizes that the student prefers videos over articles and struggles with two-tailed but not one-tailed T tests (for those who had statistics), then the system can offer that particular information to the student via the student’s preferred medium. Instead of using one academic resource for every student, the system can take information from various academic resources to create a unique learning pathway for every student based on that particular student’s preferences and needs. This is all driven by a recommendation engine
Here are some other organizations developing adaptive learning platforms:
Siemens, G. (2004). Connectivism: A learning theory for the digital age. Retrieved from http://www.elearnspace.org/Articles/connectivism.htm
Siemens, G. (2012). MOOC’s for the win. Retrieved from http://www.elearnspace.org/blog/
Other Articles of Interest: