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.
Module 1: Bayesian knowledge tracing
Module 1 Learning Lab Overview
Bayesian knowledge tracing Conceptual Overview
Bayesian knowledge tracing Case Study
BKT-BF walkthrough:Mac PC
Bayesian knowledge tracing Badge Activity
Bayesian knowledge tracing Readings
Module 2: Performance Factor Analysis & Logistic Knowledge Tracing
PFA&LKT Conceptual Overview
Performance Factor Analysis Case Study
Logistic Knowledge Tracing walkthrough
PFA&LKT Badge Activity
PFA&LKT Readings
Module 3: Item Response Theory & ELO
IRT&ELO Conceptual Overview
IRT&ELO Case Study
IRT&ELO Badge Activity
IRT&ELO Readings
Module 4: Deep Knowledge Tracing
DKT Conceptual Overview
DKT Case Study
DKT Badge Activity
DKT Readings
Module 5: Memory Algorithm
Memory Algorithm Conceptual Overview