Improve Online Learning with Basic Algorithms: To Design Personalized Online Learning Experiences, Take a Cue from Players like Google and Amazon

By Littlewood, Chris | Talent Development, August 2015 | Go to article overview

Improve Online Learning with Basic Algorithms: To Design Personalized Online Learning Experiences, Take a Cue from Players like Google and Amazon


Littlewood, Chris, Talent Development


If you've Googled something, sent an email, checked your bank balance, or bought something online today, you've put some powerful algorithms into play. As you use the Internet, algorithms work quietly behind the scenes to give you a personalized online experience.

The widespread use of algorithms has been driven by the need for tailored content, and fueled by the rich data generated from our Internet usage. Algorithms are now transforming the training industry because personalization increases the relevance and impact of online learning.

There's much we can learn from other sectors that are farther down this road, but there also will be challenges unique to our industry. What can we learn from other organizations that use algorithms to reach their target audiences, and what do we need to figure out for ourselves?

Borrowing algorithms from other sectors

There are many types of algorithms to study and learn from. There are highly specialized trading algorithms that catalyze the stock markets, encryption algorithms that allow payments to be made securely online, and identification algorithms that will recognize an individual from his facial features. But the algorithms most relevant to training are those that personalize your Internet experience.

Amazon, for example, uses algorithms to figure out what you're likely to buy and to adapt its webpages to include an offer of that item. It does this in several ways, but one of the most effective is incredibly simple: When I view a product on Amazon, the webpage will quickly adapt to offer me items that "customers who viewed this also viewed." Amazon is using other customers as a link between two products, and hoping that I, like them, am interested in both. This is one example of an algorithm we can use in training: figuring out which parts-or types-of online learning might be relevant to a learner based on other learners' activity.

Search engine algorithms do two things: They rate websites based on quality (which sites are most authoritative, useful, and entertaining, for example) and relevance (the connection between the words you search for and the website you want). Google, for example, uses search terms, website quality scores derived from its PageRank algorithm, and its index to return search results that are relevant to the user.

Search engine algorithms are doing what we want to do in online learning: selecting content for an individual based on what they know about her. Although online learning programs aren't set up like search engines, they can still allow us to gather information about learners and connect them with the content they most need.

At Filtered, we teach just what our students need to learn, meaning that we strip out material that a student already knows or that isn't valuable in his work. When a student starts training with us, we ask him questions to assess his current levels of mastery and his learning needs. Then as the student progresses through the training program, we ask him for feedback on each module. It's these two data sets-a set of learner profiles, and the degree to which each of those learners found each training module useful-that allow us to connect future learners with relevant training material based on their profiles alone.

Five principles for using algorithms

These five principles underlie all personalization. Some of these apply to the use of algorithms for general personalization; some are particular to training.

Principle 1: Evidence basis. Personalize content based on evidence from users. …

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