  Intelligent Tutoring Systems have become critically important in future
learning environments. Knowledge Tracing (KT) is a crucial part of that system.
It is about inferring the skill mastery of students and predicting their
performance to adjust the curriculum accordingly. Deep Learning-based KT models
have shown significant predictive performance compared with traditional models.
However, it is difficult to extract psychologically meaningful explanations
from the tens of thousands of parameters in neural networks, that would relate
to cognitive theory. There are several ways to achieve high accuracy in student
performance prediction but diagnostic and prognostic reasoning is more critical
in learning sciences. Since KT problem has few observable features (problem ID
and student's correctness at each practice), we extract meaningful latent
features from students' response data by using machine learning and data mining
techniques. In this work, we present Interpretable Knowledge Tracing (IKT), a
simple model that relies on three meaningful latent features: individual skill
mastery, ability profile (learning transfer across skills), and problem
difficulty. IKT's prediction of future student performance is made using a
Tree-Augmented Naive Bayes Classifier (TAN), therefore its predictions are
easier to explain than deep learning-based student models. IKT also shows
better student performance prediction than deep learning-based student models
without requiring a huge amount of parameters. We conduct ablation studies on
each feature to examine their contribution to student performance prediction.
Thus, IKT has great potential for providing adaptive and personalized
instructions with causal reasoning in real-world educational systems.
