About: Learning from Interpretable Analysis: Attention-Based Knowledge Tracing

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Learning from Interpretable Analysis: Attention-Based Knowledge Tracing
Creator	Tang, Yong Fung, Cheong Huang, Changqin Pui, Gabriel Yu, Weihao Zheng, Zetao Zhu, Jia
source	PMC
abstract	Knowledge tracing is a well-established problem and non-trivial task in personalized education. In recent years, many existing works have been proposed to handle the knowledge tracing task, particularly recurrent neural networks based methods, e.g., Deep Knowledge Tracing (DKT). However, DKT has the problem of vibration in prediction outputs. In this paper, to better understand the problem of DKT, we utilize a mathematical computation model named Finite State Automaton(FSA), which can change from one state to another in response to the external input, to interpret the hidden state transition of the DKT when receiving inputs. And we discover the root cause of the two problems is that the DKT can not handle the long sequence input with the help of FSA. Accordingly, we propose an effective attention-based model, which can solve the above problem by directly capturing the relationships among each item of the input regardless of the length of the input sequence. The experimental results show that our proposed model can significantly outperform state-of-the-art approaches on several well-known corpora.
has issue date	2020-06-10(xsd:dateTime)
bibo:doi	10.1007/978-3-030-52240-7_66
has license	no-cc
sha1sum (hex)	0b7778a2f614cc664983ee0f7028cd30291dc47c
schema:url	https://doi.org/10.1007/978-3-030-52240-7_66
resource representing a document's title	Learning from Interpretable Analysis: Attention-Based Knowledge Tracing
has PubMed Central identifier	PMC7334689
schema:publication	Artificial Intelligence in Education
resource representing a document's body	covid:0b7778a2f614cc664983ee0f7028cd30291dc47c#body_text
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