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Summary:

A new analytics engine from education technology company Desire2Learn uses big data to predict and improve student performance in higher education.

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To help figure out how well they might do in a particular class, college students can turn to faculty-ranking websites, school surveys and their peers. But a new tool from Canadian ed tech company Desire2Learn aims to predict students’ success based not on the experiences of others but on their own past performance.

The company’s analytics engine also helps instructors and students for the duration of a course, warning instructors when students are falling behind on key concepts and offering insights that could help them keep up. “It provides deeper insights to teachers on how to achieve better outcomes, what’s working and what’s not working,” said Desire2Learn CEO John Baker.

Based in Waterloo, Canada, Desire2Learn offers a learning management system for colleges that’s competitive with Blackboard and says more than 10 million students in higher education use its technology. Its new analytics product, which the company calls its Student Success System, builds on technology the company acquired earlier this year in the purchase of DegreeCompass, a course recommendation engine developed at Austin Peay State University.

As students progress through a course and interact with Desire2Learn – by digitally reviewing course materials, submitting homework assignments, communicating with classmates and completing tests and quizzes – the system’s algorithms continuously analyze each student’s personal collection of education data.

Instead of just giving the teacher a dashboard showing students’ grades and completed assignments, it looks across all of the material to isolate the areas in which each student is faltering, suggests pathways for student improvement and predicts their grade at the end of the course.

Before beginning a course, students who have completed one semester can use the tool to predict how they might fare in that course – and Desire2Learn says it’s 90 percent accurate at predicting the letter grade.

The new tool is part of a larger push in education to use data to improve student outcomes. CourseSmart, technology supported by McGraw-Hill, Pearson and other publishers that allows professors to track students’ progress with digital textbooks, similarly gives instructors a steady stream of data showing student engagement and performance (although it doesn’t make predictions). But while some educators are ready to embrace a data-driven approach to education, others feel like it smacks too much of Big Brother, and that could be a potential challenge the adoption of products like this.

Image by Vixit via Shutterstock.

  1. Unfortunately, these data sets just use traditional data warehousing tools to mine the data because they do not measure up to Big Data. Even a large LMS like ours with a few years of 30 million hits a day would not accumulate the data to qualify as Big Data from the estimates we’ve seen.

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  2. Thanks for the comment sneezypb, far too often common recommendation engines are said to use Big Data but in the end, they don’t.

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