Online-based subscription services such as Netflix and Pandora are incredibly popular, in part due to their predictive algorithms that can match users up with new favorite movies, TV shows, and songs. Similarly, social media companies and retail stores collect and analyze information about their users in order to direct appropriate advertisements to them, and even to know what they may want before they are even aware (which may be where this stuff gets a little unsettling).
Though television, radio, and retail have all changed significantly due to the digital tools now available to us, education has largely remained stagnant, using technology more as a static supplement than a game-changer.
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| Lisa Tagliaferri, 2013 |
There is, however, an increasing call to action to use technology more aggressively within the education sector.
Freakonomics Radio did a podcast in 2010 comparing a bad radio station to the public-school system, because a traditional radio station (unlike Pandora) is trying to throw a pitch down the middle to appeal to the whole spectrum of listeners, similar to what an instructor may have to do in a classroom. They profiled School of One, which creates customized learning for individual students that is reactive on a day-to-day basis and module-based.
The education and creativity expert Ken Robinson has also been discussing education reform for years, stating that the education system needs to break out of its Industrial Revolution structure, as in this 2010 video. Cathy N. Davidson has also discussed how education has not been as transformed by technology and recent scientific findings as it could be, and suggests new directions to take in her 2011 book Now You See It.
More recently, Daniel Jarratt has worked on an algorithm to transform college recommendation, which is available via PossibilityU. The Chronicle of Higher Education calls it a “Netflix-like algorithm,” that uses 80 variables to suggest colleges similar to ones that a student is already interested in.
What if program-based algorithms could also be used in higher education to assist with choosing a major or take the discomfort out of semester-to-semester scheduling? Programs could discover what interests a student and how she performs in certain disciplines within the first two years of coursework, then predict and suggest a suitable major. A database could hold all of the required core courses of an institution, and match a student up to an ideal schedule while keeping him on track to complete his degree in four years.
On a daily basis, algorithms could react to students’ performance, as with School of One, to help students with the autodidactic learning that is often required at the college level. The program may be able to take into account how students may spend their time in general terms — that is, perhaps a gaming curriculum as followed at the Middle School level at Quest to Learn could also work for an individual undergraduate student with a penchant for gaming.
Algorithms for Learning
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