When the iPhone was introduced in 2007, much of the technology behind it had already existed for several years. “Technically speaking,” as management design professor Kalle Lyytinen puts it, “the iPhone was not very innovative.” What was innovative was the design and features, which “unlocked a new era of human-computer interaction.”
Edtech is at a similar moment today. The technology itself is not the challenge. Computing power, AI, and machine learning developments have made possible a whole new range of tools that can accelerate, differentiate, and support learning in myriad ways. But, as became evident during the pandemic, too many of these tools remain, like early mobile devices, unwieldy and frustrating to use, for both teachers and students.
As computer technology’s role in education develops during the coming years, it is absolutely vital that edtech designers and learning engineers focus on the human-computer interaction that attends to and augments the classroom experience for teachers and students. Edtech needs to become more user-centered and better integrated into education, in classrooms and beyond.
The study of human-computer interaction has a long history. Since the 1960s, designers, researchers, and programmers have worked to help human beings harness computing power to benefit their day-to-day lives. In addition to iPhone touchscreens, their insights have brought us the mouse, the desktop, and virtual reality. But complex interactions like those involved in education present different and subtle challenges for designers.
In education, designers must account for the ongoing use of the same platform by both students and teachers, in a context that also involves parents, school counselors, and others. They must deeply understand real-world teaching and learning environments to determine how best to help facilitate often complex and difficult to quantify classroom interactions. And, finally, [pullquote]the differences in student populations must be accounted for so that designers aren’t engaging in a one-size-fits-all approach that too often turns out to be one-size-fits-none.[/pullquote]
First, let’s look at how human-computer interaction can be improved for teachers. Computers are good at taking rapid measurements and making straightforward inferences, and they can do it at scale. For teachers who are often unable to quickly gauge student understanding at as fine-grained a level as they would like, this can be extremely useful, especially when they’re teaching in large classes with populations that vary significantly in background and prior knowledge. The problem here is that many edtech tools fail to provide rich data to teachers and, even when they do, they often fail to make it accessible and legible for teachers to use quickly to tailor instruction to student needs.
One promising format for this kind of information is the “dashboard.” These have become crucial to learning technologies. They’ve been used to deliver real-time reporting on student performance to teachers, as well as to make predictions about future performance so that teachers can intervene proactively with struggling students.
But there’s still a great deal that can be done to improve dashboards and learn how they can be more effective. Evidence shows, for example, that simply showing teachers raw data is of limited usefulness. Dashboards that can instead automatically identify specific problem areas for students and promising types of interventions would be far more useful, teachers say.
Designers and researchers should do more to create easy-to-use dashboards that can have a real-time impact in the classroom, alerting teachers to problem areas and helping them use classroom time as efficiently and productively as possible. Quick notifications and updates can help teachers get a quick glimpse of which students are struggling, what topics to focus on, and how to quickly shift to better support students.
On the student side, there is much that can be done to create human-computer interactions that prove more engaging and beneficial for students and that are better adapted to real-world classroom environments. Companies like Carnegie Learning have already made considerable strides in developing “cognitive tutors” that are shown to improve learning in randomized control trials. Despite a good research base for some of these tools, more can still be done to understand how to make these systems more effective for students.
For example, closer research attention to “wheel spinning,” where students are struggling unproductively for an extended period of time, can help researchers understand more about how students are interacting with the technology. These insights can, in turn, lead to improvements in the technology’s design and implementation. Tutoring software can learn to detect when a specific student is frustrated and respond with timely support, or notify a teacher when it is unable to address a student’s frustration. The result will be a more sophisticated form of blended learning, where technology is used for routine and repetitive learning tasks and knows on its own when to loop in a human teacher when the learner isn’t progressing.
This type of research is absolutely vital if we are to leverage technological advancement and find the right role for learning technology in the classroom. As the pandemic has shown, edtech can’t — and shouldn’t — ever replace the classroom environment, but if designers pay closer attention to how edtech is integrated into that environment it could unlock a new era of innovation in education.
Ryan Baker is an Associate Professor at the University of Pennsylvania, and Director of the Penn Center for Learning Analytics. His lab conducts research on engagement and robust learning within online and blended learning, seeking to find actionable indicators that can be used today but which predict future student outcomes. Baker has developed models that can automatically detect student engagement in over a dozen online learning environments and has led the development of an observational protocol and app for field observation of student engagement that has been used by over 150 researchers in seven countries. Predictive analytics models he helped develop have been used to benefit over a million students, over a hundred thousand people have taken MOOCs he ran, and he has coordinated longitudinal studies that spanned over a decade. He was the founding president of the International Educational Data Mining Society, is currently serving as Editor of the journal Computer-Based Learning in Context, is Associate Editor of the Journal of Educational Data Mining, was the first technical director of the Pittsburgh Science of Learning Center DataShop, and currently serves as Co-Director of the MOOC Replication Framework (MORF). Baker has co-authored published papers with over 400 colleagues.
Your donation will support the work we do at brightbeam to shine a light on the voices who challenge decision makers to provide the learning opportunities all children need to thrive.