Sometimes the only way to make progress is to leave something behind.
Love it or hate it, Big Data is changing K–12 education.
Collecting and analyzing student data — including grades, attendance, disciplinary issues and test scores — gives schools and districts new, valuable insights into student performance and behavior.
For example, after my colleagues in Washington helped Spokane Public Schools set up a virtualized data warehouse, the district began using an in-house data analytics system to determine predictors of dropping out and to monitor at-risk students. As a result, graduation rates improved by nearly 8 percent over a three-year period.
Educators from Metropolitan Nashville Public Schools in Tennessee also helped raise graduation rates by providing transparency into student performance data. Accessing data from a centralized dashboard, students, parents and teachers were able to develop customized action plans to promote academic success. The process also led to higher test scores.
Although both of these examples demonstrate how Big Data can improve student outcomes, neither explains how data can impact students’ day-to-day learning experiences as they happen.
A forward-looking bit of technology, affective computing has the power to help educators tailor instruction to students’ needs in real time.
The technology uses passive sensors to monitor computer users’ movements and sounds. Web cameras capture facial expressions, gross body movements and posture, while microphones pick up speech. Additional sensors can even collect physiological data, such as elevated body temperature. Machine learning picks up where the sensors leave off, using algorithms to recognize and interpret affective cues before reacting.
So how would having emotionally intelligent computers change education? For one thing, it would give teachers more insight into which students are struggling with the course material so they could offer immediate assistance. Affective computing could also identify which students are breezing through a subject, so they can be further challenged.
Companies such as Intel are even experimenting with ways that affective computing might enable adaptive learning environments to adjust difficulty levels automatically while also detecting student motivation patterns. And because motivation plays a critical part in students’ engagement levels and performance, recognizing and leveraging these patterns could have a big impact in the classroom.
As with any form of data tracking, affective computing gives rise to privacy concerns that may slow its adoption. But unlike the performance and behavior data typically collected by schools, affective data forces us to question whether — despite the benefits — it is ethical to monitor something as intimate as a student’s emotions.
Which emotions will be tracked? How will the information be used? Where will it be stored? What security protections will be put in place to guard students’ privacy? These are all important questions, the answers to which could make or break affective computing in the education space.
And while we don’t yet know how the privacy conversation will play out, I, for one, am excited to start the discussion if it means bringing the benefits of affective data to K–12 schools.
This article is part of the “Connect IT: Bridging the Gap Between Education and Technology” series. Please join the discussion on Twitter by using the #ConnectIT hashtag.