The real benefit of having an education learning management system (LMS) is the ability of professors to track learning and for universities to save students before they drop out. This not only will improve student learning, but also prevent the loss of revenue from students who drop out.
Learning analytics is growing field that is advancing thanks to the proliferation of data made available through today’s education LMS platforms. Thanks to the dashboard properties within education LMS platforms such as Canvas, D2L, and Blackboard, professors can now track when students look at assignments, how many have looked at it, and when assignments are submitted electronically.
Academic institutions have always had data available to them, but it was limited to the basics such as demographics, attendance, average marks in a course across years or different professors, and a student’s grade performance history.
People go into teaching because they want to create learning experiences for students and to help them succeed. Learning analytics can be used to predict which students may be at risk of dropping out, not just based on demographics, but also online behaviors within the education LMS platform. Massive open online courses (MOOCs), by their very nature, have multiple data points available on student behavior. Professors and administrators who are able to flag students at risk of dropping out can reach out before to increase retention and performance.
There has been criticism of learning analytics. The site Hack Education, founded by Audrey Watters, points out that the insights to date have barely been better than going to class make you a better student and poorer students struggle more, most likely due to increased responsibility outside of the classroom. She warns that the underlying data contains biases that could steer underprivileged students to less demanding courses that lead to lower paying careers.
Source: SoftwareReviews Education Learning Management Systems, Report Published January 2019
Learning analytics can help students learn and educators adjust their course content to better serve students. Ideally, it will create customized experiences and personalize learning in MOOCs. However, as with any data collection, privacy and potential discrimination through biased data are serious concerns. Today, there is no standardized format and this will be an interesting field to follow as learning analytics matures.