11,717 research outputs found
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),
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DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing
In the field of intelligent education, knowledge tracing (KT) has attracted
increasing attention, which estimates and traces students' mastery of knowledge
concepts to provide high-quality education. In KT, there are natural graph
structures among questions and knowledge concepts so some studies explored the
application of graph neural networks (GNNs) to improve the performance of the
KT models which have not used graph structure. However, most of them ignored
both the questions' difficulties and students' attempts at questions. Actually,
questions with the same knowledge concepts have different difficulties, and
students' different attempts also represent different knowledge mastery. In
this paper, we propose a difficulty and attempts boosted graph-based KT
(DAGKT), using rich information from students' records. Moreover, a novel
method is designed to establish the question similarity relationship inspired
by the F1 score. Extensive experiments on three real-world datasets demonstrate
the effectiveness of the proposed DAGKT.Comment: 12 pages, 3figures, conference:ICONI
Teaching Categories to Human Learners with Visual Explanations
We study the problem of computer-assisted teaching with explanations.
Conventional approaches for machine teaching typically only provide feedback at
the instance level e.g., the category or label of the instance. However, it is
intuitive that clear explanations from a knowledgeable teacher can
significantly improve a student's ability to learn a new concept. To address
these existing limitations, we propose a teaching framework that provides
interpretable explanations as feedback and models how the learner incorporates
this additional information. In the case of images, we show that we can
automatically generate explanations that highlight the parts of the image that
are responsible for the class label. Experiments on human learners illustrate
that, on average, participants achieve better test set performance on
challenging categorization tasks when taught with our interpretable approach
compared to existing methods
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