Personalized Learning Systems- Worthy of the Hype?
Despite significant advances in technology for learning, today’s education systems remain largely “one-size-fits-all”, ignoring the individuality of students and forcing them into artificial timelines for learning. As a result, there has been considerable recent interest in personalized learning systems (PLS) such as instructional modules that enable self-paced learning and interactive computer programs designed to respond to the learner’s questions. While successful, PLSs have been extremely difficult to realize without major investments of time, money, and expertise. Moreover, recent studies indicate that they do not always facilitate improved learning. In this presentation, we will debate the pros and cons of the current PLS approaches (e.g., “content-centric / rules-based systems” vs. “data centric / machine learning algorithms”). We will also discuss how ideas and research findings from cognitive science can be used to improve the efficacy of PLS.
Additional Supporting Materials
- Does personalization produce sufficient improvement in student learning to justify the costs of a more complex learning system?
- How can we leverage “big data” machine learning algorithms to construct more efficient and cost-effective personalized learning systems?
- How can ideas and research findings from cognitive science be used to improve the efficacy of personalized learning systems?
- Richard Baraniuk, Victor E. Cameron Professor, Rice University
- Andrew Butler, Postdoctoral Research Scholar, Duke University
Richard Baraniuk, Victor E. Cameron Professor, Rice University
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