May 4

CS Tea Talk Series

Thu, May 4, 2017 • 4:00pm - 5:00pm (1h) • CMC 209

Using Computational Methods to Improve Feedback for Learners

Online educational technologies offer learners the opportunity to receive interactive feedback, with the potential to address misunderstandings or re-explain complex concepts. This feedback can be personalized based on this learner's or previous learners' interactions with the technology. In this talk, I’ll share a bit about two current research projects where we use machine learning to try to provide more effective feedback. In the first project, we create a detailed assessment of learners’ algebra misunderstandings by automatically processing their step-by-step solutions to algebraic equations, and then use that assessment to choose a tutorial that targets a specific skill that they’re struggling with. In the second project, we examine how interactive quizzes, like you might have done in Moodle, can use a combination of crowdsourcing and machine learning to identify what feedback is most effective and give that feedback to more students. Both of these projects point to the potential of computational methods for creating smarter educational resources that address individual students' needs, and in each project, consideration of the specific educational problem has led to investigation of computational questions that may be more broadly applicable.

Event Contact: Sue Jandro

Event Summary

CS Tea Talk Series
  • Intended For: Students, Faculty, Staff

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