241 research outputs found
Enhancing Deep Knowledge Tracing with Auxiliary Tasks
Knowledge tracing (KT) is the problem of predicting students' future
performance based on their historical interactions with intelligent tutoring
systems. Recent studies have applied multiple types of deep neural networks to
solve the KT problem. However, there are two important factors in real-world
educational data that are not well represented. First, most existing works
augment input representations with the co-occurrence matrix of questions and
knowledge components\footnote{\label{ft:kc}A KC is a generalization of everyday
terms like concept, principle, fact, or skill.} (KCs) but fail to explicitly
integrate such intrinsic relations into the final response prediction task.
Second, the individualized historical performance of students has not been well
captured. In this paper, we proposed \emph{AT-DKT} to improve the prediction
performance of the original deep knowledge tracing model with two auxiliary
learning tasks, i.e., \emph{question tagging (QT) prediction task} and
\emph{individualized prior knowledge (IK) prediction task}. Specifically, the
QT task helps learn better question representations by predicting whether
questions contain specific KCs. The IK task captures students' global
historical performance by progressively predicting student-level prior
knowledge that is hidden in students' historical learning interactions. We
conduct comprehensive experiments on three real-world educational datasets and
compare the proposed approach to both deep sequential KT models and
non-sequential models. Experimental results show that \emph{AT-DKT} outperforms
all sequential models with more than 0.9\% improvements of AUC for all
datasets, and is almost the second best compared to non-sequential models.
Furthermore, we conduct both ablation studies and quantitative analysis to show
the effectiveness of auxiliary tasks and the superior prediction outcomes of
\emph{AT-DKT}.Comment: Accepted at WWW'23: The 2023 ACM Web Conference, 202
Deep knowledge tracing with learning curves
Knowledge tracing (KT) models students' mastery level of knowledge concepts based on their responses to the questions in the past and predicts the probability that they correctly answer subsequent questions in the future. Recent KT models are mostly developed with deep neural networks and have demonstrated superior performance over traditional approaches. However, they ignore the explicit modeling of the learning curve theory, which generally says that more practices on the same knowledge concept enhance one's mastery level of the concept. Based on this theory, we propose a Convolution-Augmented Knowledge Tracing (CAKT) model and a Capsule-Enhanced CAKT (CECAKT) model to enable learning curve modeling. In particular, when predicting a student's response to the next question associated with a specific knowledge concept, CAKT uses a module built with three-dimensional convolutional neural networks to learn the student's recent experience on that concept, and CECAKT improves CAKT by replacing the global average pooling layer with capsule networks to prevent information loss. Moreover, the two models employ LSTM networks to learn the overall knowledge state, which is fused with the feature learned by the convolutional/capsule module. As such, the two models can learn the student's overall knowledge state as well as the knowledge state of the concept in the next question. Experimental results on four real-life datasets show that CAKT and CECAKT both achieve better performance compared to existing deep KT models
Deep Knowledge Tracing is an implicit dynamic multidimensional item response theory model
Knowledge tracing consists in predicting the performance of some students on
new questions given their performance on previous questions, and can be a prior
step to optimizing assessment and learning. Deep knowledge tracing (DKT) is a
competitive model for knowledge tracing relying on recurrent neural networks,
even if some simpler models may match its performance. However, little is known
about why DKT works so well. In this paper, we frame deep knowledge tracing as
a encoderdecoder architecture. This viewpoint not only allows us to propose
better models in terms of performance, simplicity or expressivity but also
opens up promising avenues for future research directions. In particular, we
show on several small and large datasets that a simpler decoder, with possibly
fewer parameters than the one used by DKT, can predict student performance
better.Comment: ICCE 2023 - The 31st International Conference on Computers in
Education, Asia-Pacific Society for Computers in Education, Dec 2023, Matsue,
Shimane, Franc
- …