  We review and evaluate a body of deep learning knowledge tracing (DLKT)
models with openly available and widely-used data sets, and with a novel data
set of students learning to program. The evaluated knowledge tracing models
include Vanilla-DKT, two Long Short-Term Memory Deep Knowledge Tracing
(LSTM-DKT) variants, two Dynamic Key-Value Memory Network (DKVMN) variants, and
Self-Attentive Knowledge Tracing (SAKT). As baselines, we evaluate simple
non-learning models, logistic regression and Bayesian Knowledge Tracing (BKT).
To evaluate how different aspects of DLKT models influence model performance,
we test input and output layer variations found in the compared models that are
independent of the main architectures. We study maximum attempt count options,
including filtering out long attempt sequences, that have been implicitly and
explicitly used in prior studies. We contrast the observed performance
variations against variations from non-model properties such as randomness and
hardware. Performance of models is assessed using multiple metrics, whereby we
also contrast the impact of the choice of metric on model performance. The key
contributions of this work are: Evidence that DLKT models generally outperform
more traditional models, but not necessarily by much and not always; Evidence
that even simple baselines with little to no predictive value may outperform
DLKT models, especially in terms of accuracy -- highlighting importance of
selecting proper baselines for comparison; Disambiguation of properties that
affect performance in DLKT models including metric choice, input and output
layer variations, common hyperparameters, random seeding and hardware;
Discussion of issues in replicability when evaluating DLKT models, including
discrepancies in prior reported results and methodology. Model implementations,
evaluation code, and data are published as a part of this work.
