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Deep Knowledge Tracing
[article]
2015
arXiv
pre-print
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Knowledge tracing---where a machine models the knowledge of a student as they interact with coursework---is a well established problem in computer supported education. ...
These results suggest a promising new line of research for knowledge tracing and an exemplary application task for RNNs. ...
DKT is the result of using LSTM Deep Knowledge Tracing. ...
arXiv:1506.05908v1
fatcat:jzxrp3wxzzcfvfayx6srzbyqii
Deep Trustworthy Knowledge Tracing
[article]
2019
arXiv
pre-print
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Knowledge tracing (KT), a key component of an intelligent tutoring system, is a machine learning technique that estimates the mastery level of a student based on his/her past performance. ...
Compared with traditional KT models, deep learning-based KT (DLKT) models show better predictive performance because of the representation power of deep neural networks. ...
Deep Trustworthy Knowledge Tracing In this section, we introduce the side effects of the existing DLKT and propose a Deep Trustworthy Knowledge Tracing (DTKT) to address these side effects. ...
arXiv:1805.10768v3
fatcat:xq75fsfwi5gplmqoujswo6lrsi
Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing
[article]
2021
arXiv
pre-print
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2018 pre-print arXiv:1809.08713v1 fatcat:azxdg72rxvf7vl5rr2ubcbqnry
, and ii) combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing. ...
In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instructions. ...
In this paper we propose a novel model for knowledge tracing, Deep Knowledge Tracing with Dynamic Student Classification (DKT-DSC). ...
arXiv:1809.08713v2
fatcat:mpsegs2rpvghxjc74pj53ji5am
Deep Knowledge Tracing with Transformers
[chapter]
2020
Lecture Notes in Computer Science
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In this work, we propose a Transformer-based model to trace students' knowledge acquisition. ...
One of such extensions is Deep Knowledge Tracing (DKT). The first DKT [6] adopted the Recurrent Neural Network (RNN) architecture from the deep learning community. ...
Table 1 . 1 DatasetTable 1summarizes our findings and compares them to the start-of-the-art Deep Knowledge Tracing model results in the literature, as well as the Bayesian Knowledge Tracing (BKT) model ...
doi:10.1007/978-3-030-52240-7_46
fatcat:5yeu3zff3raw5gpklrlqvck7rm
How deep is knowledge tracing?
[article]
2016
arXiv
pre-print
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tracing or DKT---has demonstrated a stunning performance advantage over the mainstay of the field, Bayesian knowledge tracing or BKT. ...
This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to predicting students' performance as they work through a series of exercises---termed deep knowledge ...
Figure 1 : 1 Figure 1: Deep knowledge tracing (DKT) architecture. ...
arXiv:1604.02416v2
fatcat:wsl4oujmszfgbmfedqndihurlm
Deep Knowledge Tracing with Learning Curves
[article]
2021
arXiv
pre-print
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Knowledge tracing (KT) has recently been an active research area of computational pedagogy. ...
Based on this theory, we propose a Convolution-Augmented Knowledge Tracing (CAKT) model in this paper. ...
Knowledge Tracing with Deep Learning The Deep Knowledge Tracing (DKT) model [28] first applies deep learning on the KT task. ...
arXiv:2008.01169v2
fatcat:ogcrwrc26nahrjfguxawxlapki
Deep Factorization Machines for Knowledge Tracing
2018
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
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We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. ...
Historically, Bayesian Knowledge Tracing (BKT) modeled the learner as a Hidden Markov model (Corbett and Anderson, 1994) , but with the advent of deep learning, a Deep Knowledge Tracing (DKT) model has ...
Conclusion In this paper, we showed how to use deep factorization machines for knowledge tracing. ...
doi:10.18653/v1/w18-0545
dblp:conf/bea/Vie18
fatcat:7g4mst2ebfb45pyiwoxz3l7gb4
Going Deeper with Deep Knowledge Tracing
2016
Educational Data Mining
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Deep Knowledge Tracing (DKT), a pioneer algorithm that utilizes recurrent neural networks to model student learning, reports substantial improvements in prediction performance. ...
We take steps to reproduce the experiments of Deep Knowledge Tracing by implementing a DKT algorithm using Google's TensorFlow framework; we also reproduce similar results on new datasets. ...
Deep knowledge tracing (DKT), the recent adoption of recurrent neural nets (RNNs) in the area of educational data mining, achieved dramatic improvement over well-known Bayesian Knowledge Tracing models ...
dblp:conf/edm/XiongZIB16
fatcat:fg72xpf5xbgpdnvc5b3flhnsem
qDKT: Question-centric Deep Knowledge Tracing
[article]
2020
arXiv
pre-print
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Knowledge tracing (KT) models, e.g., the deep knowledge tracing (DKT) model, track an individual learner's acquisition of skills over time by examining the learner's performance on questions related to ...
CONCLUSION We have proposed qDKT, a novel model for knowledge tracing for educational data. ...
tracing methods. ...
arXiv:2005.12442v1
fatcat:wnixrzqtzbe6boav2eej676n6y
Deep Knowledge Tracing with Side Information
[article]
2019
arXiv
pre-print
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Despite its inherent challenges, recent deep neural networks based knowledge tracing models have achieved great success, which is largely from models' ability to learn sequential dependencies of questions ...
Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems. ...
Recently, one framework named Deep Knowledge Tracing (DKT) that is based on deep neural networks has shown superior performance over previously proposed knowledge tracing models [12] . ...
arXiv:1909.00372v1
fatcat:tv6qncznmjhu5fog5ikww3kngu
Deep Factorization Machines for Knowledge Tracing
[article]
2018
arXiv
pre-print
Preserved Fulltext
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We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. ...
Historically, Bayesian Knowledge Tracing (BKT) modeled the learner as a Hidden Markov model (Corbett and Anderson, 1994) , but with the advent of deep learning, a Deep Knowledge Tracing (DKT) model has ...
Conclusion In this paper, we showed how to use deep factorization machines for knowledge tracing. ...
arXiv:1805.00356v1
fatcat:zyiceyboejbwflam3elyzvjsky
Application of Deep Self-Attention in Knowledge Tracing
[article]
2021
arXiv
pre-print
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This paper proposed Deep Self-Attentive Knowledge Tracing (DSAKT) based on the data of PTA, an online assessment system used by students in many universities in China, to help these students learn more ...
The intelligent tutoring system must model learners' mastery of the knowledge before providing feedback and advices to learners, so one class of algorithm called "knowledge tracing" is surely important ...
In this paper we improved SAKT and proposed an encoderdecoder based model named Deep Self-Attentive Knowledge Tracing (DSAKT). ...
arXiv:2105.07909v2
fatcat:ubp22jwoeba3taso5rkwnur2su
Extending Deep Knowledge Tracing: Inferring Interpretable Knowledge and Predicting Post-System Performance
[article]
2020
arXiv
pre-print
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Recent student knowledge modeling algorithms such as Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Networks (DKVMN) have been shown to produce accurate predictions of problem correctness within ...
We apply this extension to DKT and DKVMN, resulting in knowledge estimates that correlate better with a posttest than knowledge estimates from Bayesian Knowledge Tracing (BKT), an algorithm designed to ...
Algorithms Studied
Deep Knowledge Tracing Deep knowledge tracing (DKT) uses recurrent neural networks to model student performance learning (Piech et al., 2015) . ...
arXiv:1910.12597v2
fatcat:dbgr2vdgu5cqxec4kgr6nhqtyq
Incorporating Rich Features into Deep Knowledge Tracing
2017
Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale - L@S '17
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Such models have included Bayesian Knowledge Tracing (BKT), Performance Factors Analysis (PFA), and more recently with developments in Deep Learning, Deep Knowledge Tracing (DKT). ...
Thus far, however, the model has only considered the knowledge components of the problems and correctness as input, neglecting the breadth of other features collected by computer-based learning platforms ...
Deep Knowledge Tracing Deep knowledge tracing (DKT), introduced in paper [PBH + 15], applies a Recurrent Neural Network (RNN) for this educational data mining task of following the progression of student ...
doi:10.1145/3051457.3053976
dblp:conf/lats/ZhangXZBH17
fatcat:lc76hybcunbxzctht2kzv6sd3i
Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory
[article]
2019
arXiv
pre-print
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memory network (DKVMN) to make deep learning based knowledge tracing explainable. ...
Deep learning based knowledge tracing model has been shown to outperform traditional knowledge tracing model without the need for human-engineered features, yet its parameters and representations have ...
Deep Learning Based Knowledge Tracing Recently, with a surge of interest in deep learning models, deep knowledge tracing (DKT) [14] , which models student's knowledge state based on a recurrent neural ...
arXiv:1904.11738v1
fatcat:szgfp6ex45btjpogz7gawvqucm
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