LALN - Bayesian Knowledge Tracing with Python for Researchers/Practitioners

Learning Analytics Learning Network

LALN - Bayesian Knowledge Tracing with Python for Researchers and Practitioners

Title: Bayesian Knowledge Tracing with Python for Researchers and Practitioners Summary: Bayesian Knowledge Tracing (BKT) is a common statistical model used in intelligent tutoring systems to help personalize and adapt material by continually assessing students&#x27; skill mastery. While the research community around BKT has been active, there has been a high barrier to entry given the lack of fast and easy to use software libraries. This workshop will introduce participants to the first BKT library for Python, a computationally efficient implementation that allows for easy replication of many model variants from the literature and the flexibility to define novel models. 
The workshop will consist of 35 minutes of lecture on the principles of Knowledge Tracing, 10 minutes on past libraries and the advantages of the pyBKT library, 45 minutes of tutorial, an hour of hands-on break-out group activities, and 30 minutes of share-out. 
Learning objectives include: Get familiar with the fundamentals of BKT and model variants, learn to train a BKT model on common student response datasets and to customize pyBKT models to test hypotheses about learning. 
Organizers: Zachary Pardos (https://gse.berkeley.edu/zachary-pardos), Frederic Wang, Anirudhan Badrinath - University of California, BerkeleyTarget audience: Practitioners, Researchers, StudentsTarget level: Introductory to intermediate (Python knowledge recommended)Delivery date: 5 October 2021, 4:00pm-7:00pm (US Pacific Time)Location: https://berkeley.zoom.us/j/99216048002Resources: https://github.com/CAHLR/pyBKT

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LALN - Bayesian Knowledge Tracing with Python for Researchers/Practitioners

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LALN - Bayesian Knowledge Tracing with Python for Researchers/Practitioners

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