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Predicting course outcomes with digital textbook analytics

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By Reynol Junco, associate professor, Iowa State University; and faculty associate, Berkman Center for Internet and Society1.

Over the last years, I’ve been researching how real-time behavioral data, collected unobtrusively through technology, can predict learning outcomes. As part of this line of research, I’ve recently published the paper Predicting course outcomes with digital textbook usage data in The Internet and Higher Education.

The study used data collected from student engagement with digital textbooks in order to predict course grades. Two measures of student engagement with the texts were analyzed: an engagement index that was calculated through a linear combination of the number of pages read, number of times a student opened their textbook, number of days the student used their textbook, time spent reading, number of highlights, number of bookmarks, and number of notes. The second analysis included the individual components of the engagement index.

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Are Big Data & Analytics shaping a smarter society?

Every day we generate a huge amount of big data, but we need to resort to analytics to make abstract information meaningful and get valuable knowledge from it. In education, learning platforms let us easily gather an immense quantity of data regarding students’ behaviour, interactions, preferences and opinions. When properly analysed — through learning analytics — all these data might provide useful insight on how to make learning processes more adaptive, attractive and efficient.

Are these techniques allowing us to provide better support to our students? Are we taking advantage of big data and analytics to help shape the citizens of the future?

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