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Applications of Analytics

Adaptive Learning

Category: Prescriptivec Analytics

Adaptive learning is a step beyond learning recommendations in the sense that the learning environment itself changes (or ‘adapts’) to events in the learning experience (Sonwalkar, 2007). For example, “Adaptive learning systems — like IBM Watson and Microsoft Power BI — have the advantage of continually assessing college students’ skill and confidence levels.” (Neelakantan, 2019). Early adaptive learning applications were expert systems based on explicit knowledge representations and user models, that is, they were based on statements and rules (Garrett & Roberts, 2004). More recently, the ‘black box’ methods characteristic of contemporary analytics, such as neural networks, have been employed. “Their popularity has stemmed from their ability to classify students, share characterizations, and simulate and track learners ’cognitive progress’” (Almohammadi, et.al., 2017). A widely publicized startup launched in 2015, Knewton, advertised that it could disrupt the textbook industry by creating adaptive learning out of open educational resources (OER). (del Castillo, 2015))

Examples and Articles

Adaptive Learning is Personalized
“The key to making these systems so adaptive is artificial intelligence built into them which monitors student’s inputs and responses and adjusts the difficulty of content accordingly.” Don Ford on July 9, 2018 Direct Link

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