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Knowledge tracing (KT) is the task of using students' historical learninginteraction data to model their knowledge mastery over time so as to makepredictions on their future interaction performance. Recently, remarkableprogress has been made of using various deep learning techniques to solve theKT problem. However, the success behind deep learning based knowledge tracing(DLKT) approaches is still left somewhat unknown and proper measurement andanalysis of these DLKT approaches remain a challenge. First, data preprocessingprocedures in existing works are often private and custom, which limitsexperimental standardization. Furthermore, existing DLKT studies often differin terms of the evaluation protocol and are far away real-world educationalcontexts. To address these problems, we introduce a comprehensive python basedbenchmark platform, \textsc{pyKT}, to guarantee valid comparisons across DLKTmethods via thorough evaluations. The \textsc{pyKT} library consists of astandardized set of integrated data preprocessing procedures on 7 populardatasets across different domains, and 10 frequently compared DLKT modelimplementations for transparent experiments. Results from our fine-grained andrigorous empirical KT studies yield a set of observations and suggestions foreffective DLKT, e.g., wrong evaluation setting may cause label leakage thatgenerally leads to performance inflation; and the improvement of many DLKTapproaches is minimal compared to the very first DLKT model proposed by Piechet al. \cite{piech2015deep}. We have open sourced \textsc{pyKT} and ourexperimental results at https://pykt.org/. We welcome contributions from otherresearch groups and practitioners.

pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models