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The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
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LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology)
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In this episode, we briefly review Item Response Theory and Bayesian Network Theory methods for the assessment and optimization of student learning and then describe a poster presented on the first day of the Neural Information Processing Systems conference in December 2015 in Montreal which describes a Recurrent Neural Network approach for the assessment and optimization of student learning called “Deep Knowledge Tracing”. For more details check out:
www.learningmachines101.com and follow us on Twitter at: @lm101talk
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