LKT Examples

Initial Loading of Data

The R script sets a seed for reproducibility, loads a dataset into val, and begins preprocessing by renaming a column for clarity. It converts val into a data table for efficient manipulation, then generates unstratified and student-stratified cross-validation folds to facilitate model evaluation. The script calculates time in seconds from a baseline date for each trial, orders the data by student ID and time, and creates a binary response variable based on the outcome. It computes durations for activities and applies functions to model time effects, indicating a comprehensive setup for analyzing learning patterns or predicting outcomes based on historical educational data.

Additive Factors Model

The Additive Factors Model (AFM) evaluates student performance to infer mastery levels of knowledge components (KCs). It considers a student's skill level for each KC, the KC's difficulty, and a learning rate depicting how a student's skill evolves with practice. Utilizing logistic regression, AFM measures these factors, aiding educators in identifying which KCs have been mastered and which require more attention. Unlike adaptive models that adjust predictions based on individual student performances, AFM provides a static snapshot of learning progress, serving as a tool for understanding student learning patterns rather than dynamically adapting to them.

Performance Factors Analysis

LKT Learner Modeling AssistantPerformance Factors Analysis (PFA) advances student evaluations by differentiating between the educational impacts of correct and incorrect responses on knowledge components (KCs). Unlike models that statically gauge skill levels, PFA dynamically updates its assessments of a student's grasp on KCs based on their performance outcomes, allowing predictions to reflect actual learning experiences more accurately. This method facilitates a more precise identification of strengths and weaknesses, guiding educators in customizing their instruction to meet each student's needs based on concrete data of successes and challenges.