25 research outputs found
Towards Interpretable Deep Learning Models for Knowledge Tracing
As an important technique for modeling the knowledge states of learners, the
traditional knowledge tracing (KT) models have been widely used to support
intelligent tutoring systems and MOOC platforms. Driven by the fast
advancements of deep learning techniques, deep neural network has been recently
adopted to design new KT models for achieving better prediction performance.
However, the lack of interpretability of these models has painfully impeded
their practical applications, as their outputs and working mechanisms suffer
from the intransparent decision process and complex inner structures. We thus
propose to adopt the post-hoc method to tackle the interpretability issue for
deep learning based knowledge tracing (DLKT) models. Specifically, we focus on
applying the layer-wise relevance propagation (LRP) method to interpret
RNN-based DLKT model by backpropagating the relevance from the model's output
layer to its input layer. The experiment results show the feasibility using the
LRP method for interpreting the DLKT model's predictions, and partially
validate the computed relevance scores from both question level and concept
level. We believe it can be a solid step towards fully interpreting the DLKT
models and promote their practical applications in the education domain
Quantifying Layerwise Information Discarding of Neural Networks
This paper presents a method to explain how input information is discarded
through intermediate layers of a neural network during the forward propagation,
in order to quantify and diagnose knowledge representations of pre-trained deep
neural networks. We define two types of entropy-based metrics, i.e., the strict
information discarding and the reconstruction uncertainty, which measure input
information of a specific layer from two perspectives. We develop a method to
enable efficient computation of such entropy-based metrics. Our method can be
broadly applied to various neural networks and enable comprehensive comparisons
between different layers of different networks. Preliminary experiments have
shown the effectiveness of our metrics in analyzing benchmark networks and
explaining existing deep-learning techniques