Analysis of Knowledge Tracing performance on synthesised student data
Panagiotis Pagonis Kai Hartung Di Wu Munir Georges Sören Gröttrup
Knowledge Tracing (KT) aims to predict the future performance of students by
tracking the development of their knowledge states. Despite all the recent
progress made in this field, the application of KT models in education systems
is still restricted from the data perspectives: 1) limited access to real life
data due to data protection concerns, 2) lack of diversity in public datasets,
3) noises in benchmark datasets such as duplicate records. To resolve these
problems, we simulated student data with three statistical strategies based on
public datasets and tested their performance on two KT baselines. While we
observe only minor performance improvement with additional synthetic data, our
work shows that using only synthetic data for training can lead to similar
performance as real data.