… over the mainstay of the field, Bayesian knowledge tracing or BKT. In this article, we attempt
… of deep learning. To answer the question posed in our title, knowledge tracing may be a do…

… In particular, we will see how we obtained new state-of-the-art results on 5 datasets out of 8
(bridge06, assistments09, assistments12, assistments17, and spanish) through minor feature …

… Knowledge tracing is the task of modelling student knowledge over time so that we can
accurately predict how … The knowledge tracing problem is inherently difficult as human learning is …

… Driven by both noble goals (testing the reproducibility of scientific findings) and some selfish
ones (how did they do so much better at predicting student performance?!), we set out to …

… In this work, we adopt EdNet, the largest student interaction dataset publicly available in
the education domain, to understand how accurately both deep and traditional models predict …

… We present the chronic evolution for each KT category and discuss how … -Deep Knowledge
Tracing that extends DKT by adding auxiliary student features such as previous knowledge, …

… to a knowledge state matrix, in order to explicitly track their knowledge acquisition across
multiple knowledge concepts… Each exercise may be quantified for how much it affects students’ …

… understand the key information of the system operation, such as how the system evaluates
the learner’s current knowledge status and how his or her weak skills are identified. From the …

… RNNs have been applied to the knowledge tracing task in the past to understand how …
Knowledge Tracing [13] [7], sometimes outperform the initial version of Deep Knowledge Tracing (…

… as tokening the words of knowledge components for different exercise representations.
Similarly, a wide and deep approach can be explored in how the features are represented within …