  Knowledge Tracing (KT) is concerned with predicting students' future
performance on learning items in intelligent tutoring systems. Learning items
are tagged with skill labels called knowledge concepts (KCs). Many KT models
expand the sequence of item-student interactions into KC-student interactions
by replacing learning items with their constituting KCs. This often results in
a longer sequence length. This approach addresses the issue of sparse
item-student interactions and minimises model parameters. However, two problems
have been identified with such models.
  The first problem is the model's ability to learn correlations between KCs
belonging to the same item, which can result in the leakage of ground truth
labels and hinder performance. This problem can lead to a significant decrease
in performance on datasets with a higher number of KCs per item. The second
problem is that the available benchmark implementations ignore accounting for
changes in sequence length when expanding KCs, leading to different models
being tested with varying sequence lengths but still compared against the same
benchmark.
  To address these problems, we introduce a general masking framework that
mitigates the first problem and enhances the performance of such KT models
while preserving the original model architecture without significant
alterations. Additionally, we introduce KTbench, an open-source benchmark
library designed to ensure the reproducibility of this work while mitigating
the second problem.
