Dynamic Key-Value Memory Networks for Knowledge Tracing

Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One important purpose of KT is to personalize the practice sequence to help students learn knowledge concepts efficiently. However, existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing either model knowledge state for each predefined concept separately or fail to pinpoint exactly which concepts a student is good at or unfamiliar with. To solve these problems, this work introduces a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student's mastery level of each concept. Unlike standard memory-augmented neural networks that facilitate a single memory matrix or two static memory matrices, our model has one static matrix called key, which stores the knowledge concepts and the other dynamic matrix called value, which stores and updates the mastery levels of corresponding concepts. Experiments show that our model consistently outperforms the state-of-the-art model in a range of KT datasets. Moreover, the DKVMN model can automatically discover underlying concepts of exercises typically performed by human annotations and depict the changing knowledge state of a student.Comment: To appear in 26th International Conference on World Wide Web (WWW), 201

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

From classical to current: Analyzing peripheral nervous system and spinal cord lineage and fate

During vertebrate development, the central (CNS) and peripheral nervous systems (PNS) arise from the neural plate. Cells at the margin of the neural plate give rise to neural crest cells, which migrate extensively throughout the embryo, contributing to the majority of neurons and all of the glia of the PNS. The rest of the neural plate invaginates to form the neural tube, which expands to form the brain and spinal cord. The emergence of molecular cloning techniques and identification of fluorophores like Green Fluorescent Protein (GFP), together with transgenic and electroporation technologies, have made it possible to easily visualize the cellular and molecular events in play during nervous system formation. These lineage-tracing techniques have precisely demonstrated the migratory pathways followed by neural crest cells and increased knowledge about their differentiation into PNS derivatives. Similarly, in the spinal cord, lineage-tracing techniques have led to a greater understanding of the regional organization of multiple classes of neural progenitor and post-mitotic neurons along the different axes of the spinal cord and how these distinct classes of neurons assemble into the specific neural circuits required to realize their various functions. Here, we review how both classical and modern lineage and marker analyses have expanded our knowledge of early peripheral nervous system and spinal cord development

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