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Can data and analytics help to improve student learning?

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Worldwide Industry Marketing Leader, Government & Education, IBM Analytics

The education landscape is shifting rapidly and with it the manner in which students learn. The latest generation of students—digital natives—is the most digitally connected of any previous generation. They’ve grown up on technology such as Facebook, Instagram, Twitter and YouTube composed of many social media avenues to communicate and share information about themselves, consume content and navigate through every part of their daily lives.

This trend naturally impacts learning as well. Over the last several years we’ve seen an explosion of digital content for learning. The rise of massive open online courses (MOOCs) and massive open online degree programs (MOODs) has opened up learning opportunities to the masses. Organizations such as the Kahn Academy and Kaplan University launch online learning institutions, and those institutions take off. Traditional brick-and-mortar elementary and secondary schools and higher education institutions also offer a range of online learning programs. This shift toward a heavy online focus begs the question: what is the best way for a student to learn?

http://www.ibmbigdatahub.com/sites/default/files/teched_blog.jpgRasmussen College published a study in February 2014 that compared online learning with traditional learning to determine the best approach for successful student outcomes. It looked at factors such as flexibility, discipline and social interaction and concluded that a blended curriculum combining both online courses with traditional learning offered a well-suited approach. And that blended approach depends on the student. But for each student, what should that blend look like?

Through data and analytics, education institutions can track enormous amounts of learning data. Traditional learning management systems such as the Design2Learn (D2L) platform track vast amounts of information that can shed light on student learning. By combining that information with other patterns we know about students, a clear picture of students’ preferred learning modes surfaces. Then, by applying deep analytics to the learning data, we can understand more clearly how a student learns.

Depending on the field of instruction—American literature versus mathematics—learning styles can vary, even for the same student. For example, a student may excel in a self-paced learning program in American literature with a heavy emphasis on an online approach that enables students to read at their own pace and meet once a week to discuss the materials. But that same student may need a more structured, hands-on, classroom-style learning approach for math, one that is heavy on traditional learning approaches with some online learning capabilities.

Today, the potential of analytics-driven approaches to personalized learning helps students learn at their own pace and consume content at a rate at which they are comfortable. And these approaches can free teachers and instructors to focus on those students who need extra assistance.

Read the white paper, Analytics for Achievement, to learn more about the use of analytics in education.

Personalize student learning