Students “can understand how they’re doing relative to others in the class,” said Alfred Essa, director of innovation and analytic strategy at Desire2Learn. “It gives them more insight into how they’re performing, and it [could] motivate them to do better.”
Professors and students can view data in multiple formats, such as charts, heat maps, or decision trees, in order to observe trends. At-risk learners are identified quickly, and by the second week of classes, the product can predict what a student’s grade will be by the end of the course, Essa claims.
“Think of it as an early-warning radar system,” said John Baker, president and chief executive officer of Desire2Learn. “We want to have the capability to identify students at risk, not only how they’re doing now, but be able to forecast with incredible precision about where they’re going.”
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Though projection is important, Desire2Learn believes personalization is the key to better learning outcomes. “The promise of learning analytics is to personalize learning,” said Essa. “Less data, more insight.”
Essa and Baker also highlighted the importance of comprehending the social dimension of learning. Desire2Learn recently used predictive modeling to create a sociogram, or a dynamic network graph of interactions among students, to determine which types of students were “isolated,” or more unwilling to work in groups in specific courses.
Essa and Baker believe this type of research can help not only when intervening with at-risk students, but also in advancing enrollment processes.
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