  Knowledge tracing (KT) is the problem of predicting students' future
performance based on their historical interactions with intelligent tutoring
systems. Recently, many works present lots of special methods for applying deep
neural networks to KT from different perspectives like model architecture,
adversarial augmentation and etc., which make the overall algorithm and system
become more and more complex. Furthermore, due to the lack of standardized
evaluation protocol \citep{liu2022pykt}, there is no widely agreed KT baselines
and published experimental comparisons become inconsistent and
self-contradictory, i.e., the reported AUC scores of DKT on ASSISTments2009
range from 0.721 to 0.821 \citep{minn2018deep,yeung2018addressing}. Therefore,
in this paper, we provide a strong but simple baseline method to deal with the
KT task named \textsc{simpleKT}. Inspired by the Rasch model in psychometrics,
we explicitly model question-specific variations to capture the individual
differences among questions covering the same set of knowledge components that
are a generalization of terms of concepts or skills needed for learners to
accomplish steps in a task or a problem. Furthermore, instead of using
sophisticated representations to capture student forgetting behaviors, we use
the ordinary dot-product attention function to extract the time-aware
information embedded in the student learning interactions. Extensive
experiments show that such a simple baseline is able to always rank top 3 in
terms of AUC scores and achieve 57 wins, 3 ties and 16 loss against 12 DLKT
baseline methods on 7 public datasets of different domains. We believe this
work serves as a strong baseline for future KT research. Code is available at
\url{https://github.com/pykt-team/pykt-toolkit}\footnote{We merged our model to
the \textsc{pyKT} benchmark at \url{https://pykt.org/}.}.
