Can Data Determine How Learning Happens
Today’s classroom isn’t just a place for education – it’s also a laboratory, and teachers are expected to collect huge amounts of data, with the goal of improving learning outcomes. Despite the best intentions, however, this emphasis on educational data is especially onerous for already overworked teachers, meaning they need better tools to assist with collecting that data. That’s where new recording strategies can help.
Colleges were among the first to place a heavy emphasis on analytics because of their greater resources and research-driven agendas; and as such, they were the first to realize the value of educational data. For example, facing low graduation rates, colleges examined student records and discovered that students were struggling with English classes, even as they were thriving in other subject areas. Based on that data, colleges were able to address shortfalls in entering students’ reading and writing skills and develop programs to enable them to succeed.
Unlike with college students, it can be much more challenging to collect and assess data on younger children, but schools and researchers are developing new strategies to this end that tend to be particularly reliant on technology. For example, many schools are testing classroom recording systems using audio and video data. These systems employ multiple cameras, both fixed and PTZ, to record classroom activities and analyze student engagement and response. Teachers and researchers can then play back classroom sessions to identify trouble areas. Having classroom recordings can also help observers determine whether problems are a result of instructional issues or challenges an individual student is having.
While recording the classroom can provide valuable insights, they often require an enormous amount of human labor to analyze, which is why a growing number of schools are testing out AI systems to supplement their data collection and analysis programs. For example, using the AI-based program Thinkster, teachers can see how students think through a problem, allowing them to identify why they might have gotten a problem wrong and what interventions are necessary. Microsoft has also developed a machine learning system that can assess whether a student is on track to graduate and identify which students need support to meet their educational goals.
A growing portion of classroom instruction today is done with students working independently on computers so that their work can be individually paced to meet their needs.
