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Part 4 in my Top 10 Ed-Tech Trends of 2011 series

If data was an important trend for 2011, I predict it will be even more so in 2012. That's the world we're living in. That's the world we're moving into.

More of our activities involve computers and the Internet, whether it's for work, for school, or for personal purposes. Thus, our interactions and transactions can be tracked. As we click, we leave behind a trail of data--something that's been dubbed "data exhaust." It's information that's ripe for mining and analysis, and thanks to new technology tools, we can do so in real time and at a massive, Web scale.

There's incredible potential for data analytics to impact education. We already collect a significant amount of data about school and students (attendance, demographics, test scores, free and reduced lunches, and the like), but much of it is administrative and/or siloed and/or unexamined.

Earlier this year, I interviewed George Siemens for O'Reilly Radar about the ways in which data and analytics has the potential to improve teaching and learning. He argued that

Education is, today at least, a black box. Society invests significantly in primary, secondary, and higher education. Unfortunately, we don't really know how our inputs influence or produce outputs. We don't know, precisely, which academic practices need to be curbed and which need to be encouraged. We are essentially swatting flies with a sledgehammer and doing a fair amount of peripheral damage. Learning analytics are a foundational tool for informed change in education. Over the past decade, calls for educational reform have increased, but very little is understood about how the system of education will be impacted by the proposed reforms. I sometimes fear that the solution being proposed to what ails education will be worse than the current problem. We need a means, a foundation, on which to base reform activities. In the corporate sector, business intelligence serves this "decision foundation" role. In education, I believe learning analytics will serve this role. Once we better understand the learning process--the inputs, the outputs, the factors that contribute to learner success--then we can start to make informed decisions that are supported by evidence.

But Siemens cautions,

We have to walk a fine line in the use of learning analytics. On the one hand, analytics can provide valuable insight into the factors that influence learners' success (time on task, attendance, frequency of logins, position within a social network, frequency of contact with faculty members or teachers). Peripheral data analysis could include the use of physical services in a school or university: access to library resources and learning help services. On the other hand, analytics can't capture the softer elements of learning, such as the motivating encouragement from a teacher and the value of informal social interactions. In any assessment system, whether standardized testing or learning analytics, there is a real danger that the target becomes the object of learning, rather than the assessment of learning. (emphasis mine)

Despite the promise of personalized learning through analytics and data, what we've actually seen this year is an increasing emphasis on standardization (or rather, standardized testing).And as such, most of the stories about education data this year have been stories about testing. Stories about dismal test scores. Stores about teachers' performance tied to those student test scores. Stories about cheating.

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Audrey Watters


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