Algorithms sound somewhat mysterious and complex. By definition, however, algorithms are basically a sequence of steps or activities, often directed toward solving a problem. The steps to solving a problem may be simple (how to bake a pizza or tie your shoes) or complicated (returning web search results based on an input term or recommending who you should follow on a social networking site). Jacques Ellul warned of the dangers of technique, and by extension technology, as a means to force humanity into states of conformity. No where is this more true than with algorithms. Google’s CEO recognizes the power of algorithms, offering a chilling and prophetic insight into the future role of search engines: “I actually think most people don’t want Google to answer their questions. They want Google to tell them what they should be doing next”.
Globe and Mail’s article on Programming our Lives Away reviews the growing dominance of algorithms in our lives
Increasingly, algorithms are used to determine whether we can get access to credit, insurance and government services. They are posing a challenge to human decision-making in the arts. They are being used by prospective employers to decide if we should be hired. They can determine whether your online business will succeed or fail, and they have revolutionized the world of high finance…
Some companies now use algorithms to make decisions around hiring and firing. At Google, which boasts that “almost every [personnel] decision is based on quantitative analysis,” engineers have developed an algorithm to identify those employees most likely to leave to work for a competitor or strike out on their own. Employee surveys, peer reviews, evaluations, promotion and pay histories feed into the algorithm. It helps us “get inside people’s heads even before they know they might leave,” a Google manager told The Wall Street Journal last year.
At IBM, programmers put together mathematical models of 50,000 of the company’s tech consultants. They crunched massive amounts of data on them – how many e-mails they sent, who got them, who read the documents they wrote – and used this information to help assess the employees’ effectiveness and deploy their skills in the most cost-efficient way.
I’m interested in data analysis and algorithms because of the huge potential for learning analytics to give educators, organizations, and learners better insight into the learning process. Given this interest, at TEKRI we are co-organizing and hosting the upcoming Learning Analytics Conference (Banff, Feb 28-March1, 2011), and I’ll be presenting on the topic of learning analytics December 3 (online) with the Connect@NMC. But huge questions remain as to the appropriate role of analytics to do what is often managed through social interactions – i.e. personalization and adaptivity of learning and content. A significant difficulty that learning analytics needs to address is the possible return to behaviourism where we make decisions about learning only on observable behaviours of learners. Nonetheless, algorithms define our lives and how organizations interact with us. It’s a data-driven world, and the algorithm reigns supreme.