Personalized Learning and Privacy
4 min read
Personalized learning is frequently discussed, and rarely defined. People can mean many different things when they talk about personalized learning (and arguably, we need to reclaim the term) - but personalized learning can refer to software used for, at least, any of the following:
- Learning the basics;
- Remedial help;
- Reinforcement and review;
- Taking advanced coursework; or
- Credit recovery.
Each of these uses carries a range of additional questions: who is being given online or personalized tools? Who supplies and maintains these tools? For students using personalized systems, how was it determined that software was the best approach for that student? To what extent are educational and pedagogical choices driven by "personalized" tools?
The use of these tools are all over the spectrum. There is a significant different between students at a school with a BYOD program supplementing instruction with Khan Academy, compared to large urban districts using online learning to sidestep class size requirements. Both of these scenarios are different than the approach rolled out in Detroit and the EAA, where student learning is mediated by a proprietary software system, and where single kindergarten classes can swell to as many as 100 students split between several teachers.
Which brings us to the point where it is necessary to state the obvious: show me a single school in an affluent district that is using personalized learning tools to increase class sizes. Take your time. I'll wait. If tech companies offering "personalized learning" as a solution want to have any chance at credibility, they need to expand their focus beyond just learning outcomes, and focus on learning process as well. If and when tech can be used to improve the learning process, and can provide feedback to learners that allows them to make informed and empowered choices, we'll be on to something.
This runs us directly into privacy issues. Belated warnings about an already-existing "market in technological learning tools" looking to "utilize and data-mine ... information in name of 'personalized learning'" miss the point entirely. The personalized learning experience of the affluent and the poor are radically different, and existing laws governing educational privacy weren't designed to address the consequences and implications of combining multiple data sets, or the types of data and analytics that can be derived from click-tracking, mouse tracking, pause times, and/or search strings, to name a few.
The timing and tenor of some of the more recent privacy initiatives are oddly ahistorical. Affluent school districts have been sharing sensitive student data with vendors in the cloud for years, via their LMS and SIS. Affluent schools have been using vendors to help write and store IEPs, often storing sensitive IEP data in the cloud. Yet, these long-standing practices used primarily to benefit students in well off schools continue to draw little to no attention, despite the fact that they violate student privacy in identical ways to inBloom. And we will leave the data collection and privacy policies of ETS - for many students, the well paid and self-appointed gatekeeper to higher education - for another post. While there were a lot of issues with inBloom, if the project had succeeded, it could have given traditionally underserved schools some of the basic infrastructure students in affluent schools have been enjoying for a decade or more.
So, when we talk about personalized learning, we need to begin by acknowledging that not all uses of personalized learning look the same, and that the reliance on personalized learning impacts both the learning environment and the privacy rights of students. The experience of a teacher in the suburbs worrying about bandwidth and administrative blowback if he flips his classroom is very different than the experience of a kindergarten teacher in a class with 100 five and six year olds trying to get them to complete their computerized assessments. There are privacy concerns in both scenarios, and equity issues that become obvious as we examine them.
