Clifford Maxwell, a research assistant at the Clayton Christensen Institute, contributed to this post.
Research is critical for driving many of the technological improvements we take for granted. Airplanes fly farther, computer processors get faster, cell phone batteries last longer, and online-learning software becomes more engaging all because of increased understanding of how inputs and processes lead to desired outcomes. Fortunately, we now live in an era in which technology makes it much more convenient and affordable to gather the data that is needed in order to make these advances happen.
As one example of how technologies for gathering and analyzing data can lead to improvements, consider how new technologies have recently changed the game of professional basketball. For decades, basketball teams have tracked outcomes such as points, rebounds, turnovers, and fouls. But for much of the history of the sport, coaches and players had to rely on their own observations and intuition to figure out what plays, strategies, and techniques would lead to increased points on the scoreboard. Then, at the start of the 2013–14 basketball season, the NBA introduced a new camera-based computerized tracking system called SportVU that records the movements of the ball and every player on the court at a rate of 25 times per second. The data collected from these cameras, now installed in the catwalks of every NBA arena, track much more granular details on gameplay such as how fast each player moves, how far he travels during a game, how many times he touches the ball, how many passes he throws, and how many rebounding chances he has. And now this additional data is challenging popular notions of good practice.
For example, Michael Jordan’s mid-range jump shots and “hero ball” made him the king of 1990s pro basketball. Jordan’s style worked for him because he was truly exceptional, but since Jordan, few basketball players or teams have succeeded with that style of playing. Recently, SportVU data has shown that a pass-oriented offense and highly effective three point shooting —strategies very different from those of the Chicago Bulls during the Jordan era—are often more effective at getting points on the scoreboard. It’s likely no coincidence that in the two years since SportVU data has been available, the two teams that have won the NBA championship have had the most assists and the highest three point percentage in the league.
Unfortunately, in education most classrooms don’t have a convenient and systematic way to gather and analyze data that would help them improve instructional models and teaching practices. Just as in the early days of basketball, teachers have relied on their own observations and reflections to figure out how to improve their practices. An administrator observes each teacher a handful of times during a school year, similar to how a basketball coach watches game footage and then tries to find patterns to share with his players. But such observations lack robust methods for validating perceived patterns or for gathering insights beyond what the the observer noticed during a few brief teaching sessions.
What education needs is a SportVU-like system that can track and analyze student learning patterns, teacher practices, and student learning growth in a more granular way. Fortunately, online learning provides such an opportunity by making it possible to weave measures of student learning and progress into lessons and activities in real time in order to gain insights into the learning patterns and activities that lead to learning growth. For example, as students complete online lessons using the adaptive-learning platform Knewton, the software measures each student’s learning growth and analyzes statistically which activities would be most effective for helping that student make learning gains. It then adapts each student’s progression through the online lessons accordingly.
Although Knewton’s approach incorporates data only from online-learning experiences, it isn’t hard to imagine a similar approach that also includes data from offline activities. In Teach to One classrooms, for example, students take a short assessment to measure their learning growth at the end of every online and face-to-face learning session. Teach to One’s learning algorithm then uses that data to generate a custom playlist of online and face-to-face learning activities for each student on a daily basis. In the process, Teach to One also identifies which learning activities are most effective for helping students learn and improves them accordingly.
Just as in basketball, improved data in education will likely reveal that some of the popular instructional methods and philosophies—such as teaching to students’ learning styles—are actually not as effective as their adherents have imagined. Additionally, better data will also reveal that some overlooked practices do indeed improve student learning. Just as immediate feedback is important for helping students learn, new data systems that can give teachers real-time information on what is and isn’t working in their classrooms can be a powerful tool for helping them improve their practices.
With the advent of online learning, the field of education has an unprecedented opportunity to not only take advantage of new instructional delivery methods, but also to gather data that can lead to new insights regarding how different learning patterns, learning activities, instructional approaches, and teacher practices affect student learning. In turn, these insights may soon lead to some exciting breakthroughs in education.