EDM 2024 Tutorial Schedule

July 14, 2024

8:30 AM - 9:00 AM: Features of components as predictors (Phil and Luke)
  • Basis of LKT models: features for data components
  • Computing features for levels in identifier columns per student
9:00 AM - 9:30 AM: Data preparation (Phil)
  • Requirements for running models and preprocessing
  • Time-based features and time-formatted data
  • Assistance with data formatting and special cases
9:30 AM - 10:00 AM: Previous classes of LKT model (Phil)
  • Review of LKT model varieties (AFM, PFA)
  • Examination of LKT function inputs, outputs, and feature examples
  • Creation and fitting of AFM, PFA models with recency feature
10:00 AM - 10:15 AM: Break

10:15 AM - 10:45 AM: Searching for optimal feature & Crossvalidation (Luke)
  • Introduction of stepwise and LASSO feature search methods
  • Comparison of LKT models to deep learning approaches
  • Crossvalidation of complex models using LKT functions
10:45 AM - 11:15 AM: Creating new features (Phil)
  • Hands-on tutorial on adding features to LKT codebase
  • Feature engineering using LKT
  • Open-source contributions to the R package on CRAN
11:15 AM - 11:45 AM: Application to Optimal Learning (Luke and Phil)
  • Example of using fitted LKT model for practice scheduling
  • Code flow examination for learning sequence problems
  • Discussion of model features' influence on interpretation and ITS use
11:45 AM - 12:15 PM: Application to proficiency reporting (Luke)
  • Demonstration of using knowledge tracing model for student grouping
  • Transforming model outputs into category labels
  • Accounting for practice items related to multiple skills simultaneously