Knowledge Tracing
Knowledge Inference prepares scholars to leverage techniques that model the knowledge of a student at a specific point in time as they interact with coursework and assessment activities. Techniques introduced in these modules include Bayesian, Logistic, and Deep Knowledge Tracing.
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Github |
Repository for Instructors |
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Posit Cloud | Workspace for Students |
Module 1: KT Basics
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Conceptual Overview |
Bayesian Knowledge Tracing |
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Code Along | |
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Readings & Reflection |
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Case Study | |
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Badge |
Module 2: Network Management & Measurement
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Conceptual Overview |
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Code Along | |
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Readings & Reflection |
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Case Study | |
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Badge |
Module 3: Groups, Positions, & Egocentric Analysis
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Conceptual Overview |
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Code Along | |
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Readings & Discussion |
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Case Study | |
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Badge |
Module 4: Statistical Inference & Network Models
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Conceptual Overview |
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Code Along | |
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Readings & Discussion |
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Case Study | |
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Badge |
Microcredential
The culminating activity for the SNA Modules is designed to provide you some space for independent analysis of a self-identified data source. To earn your SNA Microcredential, you are required to demonstrate your ability to formulate a basic research question appropriate to a social network context, wrangle and analyze relational data, and communicate key findings. Your primary goal for this analysis is to create a simple data product that illustrates key findings by applying the knowledge and skills acquired from the essential readings and case studies.
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Microcredential |