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Deep Knowledge Tracing with Transformers [chapter]

Shi Pu, Michael Yudelson, Lu Ou, Yuchi Huang
2020 Lecture Notes in Computer Science  
In this work, we propose a Transformer-based model to trace students' knowledge acquisition.  ...  The use of question-skill associations allows the model to learn specific representation for frequently encountered questions while representing rare questions with their underline skill representations  ...  For example, the Dynamic Key-Value Memory Networks [11] explicitly maintained knowledge components and knowledge states.  ... 
doi:10.1007/978-3-030-52240-7_46 fatcat:5yeu3zff3raw5gpklrlqvck7rm

Deep Knowledge Tracing [article]

Chris Piech, Jonathan Spencer, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas Guibas, Jascha Sohl-Dickstein
2015 arXiv   pre-print
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.  ...  Acknowledgments Many thanks to John Mitchell for his guidance and Khan Academy for its support. CP is supported by NSF-GRFP grant number DGE-114747.  ... 
arXiv:1506.05908v1 fatcat:jzxrp3wxzzcfvfayx6srzbyqii

Visual Knowledge Tracing [article]

Neehar Kondapaneni, Pietro Perona, Oisin Mac Aodha
2022 arXiv   pre-print
We collect three challenging new datasets from real human learners in order to evaluate the performance of different visual knowledge tracing methods.  ...  valuable data for applications like autonomous driving.  ...  Acknowledgements: Thanks to the anonymous reviews for their valuable feedback.  ... 
arXiv:2207.10157v2 fatcat:dk23nyh6hjb5zhlo7lkbsk6uae

Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing [article]

Youngduck Choi, Youngnam Lee, Junghyun Cho, Jineon Baek, Byungsoo Kim, Yeongmin Cha, Dongmin Shin, Chan Bae, Jaewe Heo
2020 arXiv   pre-print
Secondly, different combinations of queries, keys and values for the self-attention layer for knowledge tracing were not extensively explored.  ...  In this paper, we propose a novel Transformer based model for knowledge tracing, SAINT: Separated Self-AttentIve Neural Knowledge Tracing.  ...  ideal for knowledge tracing tasks.  ... 
arXiv:2002.07033v4 fatcat:4hezbch5ojdudd7n7miij5xmpa

Contrastive Learning for Knowledge Tracing

Wonsung Lee, Jaeyoon Chun, Youngmin Lee, Kyoungsoo Park, Sungrae Park
2022 Proceedings of the ACM Web Conference 2022  
Further analysis shows how our methods contribute to improving knowledge tracing performances.  ...  This paper introduces a contrastive learning framework for knowledge tracing that reveals semantically similar or dissimilar examples of a learning history and stimulates to learn their relationships.  ...  ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers for their valuable comments and helpful suggestions.  ... 
doi:10.1145/3485447.3512105 fatcat:aoagt5sfsnbwpcbthigzztu6ba

Towards Temporality-Sensitive Recurrent Neural Networks through Enriched Traces

Thomas Sergent, François Bouchet, Thibault Carron
2020 Educational Data Mining  
Recurrent Neural Networks have seen a surge of popularity in the recent few years thanks to Deep Knowledge Tracing.  ...  While the focus has mostly been on the network architecture, we propose here a novel framework where traces are enriched with information relative to the temporality before they are used to train the network  ...  We would like to thank the reviewers for their thoughtful comments and efforts towards improving our paper. This work is funded by Lalilo.  ... 
dblp:conf/edm/SergentBC20 fatcat:slw6gq2s65fbrlt453v4xl7kre

Application of Deep Self-Attention in Knowledge Tracing [article]

Junhao Zeng, Qingchun Zhang, Ning Xie, Bochun Yang
2021 arXiv   pre-print
Experimentation on the data of PTA shows that DSAKT outperforms the other models for knowledge tracing an improvement of AUC by 2.1% on average, and this model also has a good performance on the ASSIST  ...  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  ...  Dynamic Key-value memory network (DKVMN) [11] , although utilized Memory Augmented Neural Network [12] for Knowledge Tracing to become more interpretable [13] , still faces the same problem as DKT  ... 
arXiv:2105.07909v2 fatcat:ubp22jwoeba3taso5rkwnur2su

Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory [article]

Chun-Kit Yeung
2019 arXiv   pre-print
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  ...  tracing (DKT) [14] and dynamic key-value memory network (DKVMN) for knowledge tracing [22] .  ... 
arXiv:1904.11738v1 fatcat:szgfp6ex45btjpogz7gawvqucm

Context-Aware Attentive Knowledge Tracing [article]

Aritra Ghosh, Neil Heffernan, Andrew S. Lan
2020 arXiv   pre-print
In this paper, we propose attentive knowledge tracing (AKT), which couples flexible attention-based neural network models with a series of novel, interpretable model components inspired by cognitive and  ...  Knowledge tracing (KT) refers to the problem of predicting future learner performance given their past performance in educational applications.  ...  CONCLUSIONS AND FUTURE WORK In this paper, we have proposed attentive knowledge tracing, a new method for knowledge tracing that relies fully on attention networks.  ... 
arXiv:2007.12324v1 fatcat:b6kxptk2unfndbli4lrhwo5rzq

NTM-Based Skill-Aware Knowledge Tracing for Conjunctive Skills

Qiang Huang, Wei Su, Yuantao Sun, Tianyuan Huang, Juntai Shi, Huihua Chen
2022 Computational Intelligence and Neuroscience  
This paper proposes a neural Turing machine-based skill-aware knowledge tracing (NSKT) for conjunctive skills, which can capture the relevance among the knowledge concepts of a question to model students  ...  Knowledge tracing (KT) is the task of modelling students' knowledge state based on their historical interactions on intelligent tutoring systems.  ...  Self-attentive knowledge tracing (SAKT) proposes a self-attention-based KT model to model the students' knowledge state, with exercises as attention queries and students' past interactions as attention  ... 
doi:10.1155/2022/9153697 pmid:35936980 pmcid:PMC9348931 fatcat:nkflppngmnfm3gmcdsm4wsqy7y

An Approach for Combining Multimodal Fusion and Neural Architecture Search Applied to Knowledge Tracing [article]

Xinyi Ding, Tao Han, Yili Fang, Eric Larson
2021 arXiv   pre-print
Knowledge Tracing is the process of tracking mastery level of different skills of students for a given learning domain.  ...  However, most existing deep learning based knowledge tracing models either: (1) only use the correct/incorrect response (ignoring useful information from other modalities) or (2) design their network architectures  ...  Knowledge tracing based on deep neural networks The first work to propose Deep Knowledge Tracing (DKT) was from Piech et al. [1] .  ... 
arXiv:2111.04497v1 fatcat:kfzjrjwuwfennklwr53j4h3nwq

Empirical Evaluation of Deep Learning Models for Knowledge Tracing: Of Hyperparameters and Metrics on Performance and Replicability [article]

Sami Sarsa, Juho Leinonen, Arto Hellas
2022 arXiv   pre-print
The evaluated knowledge tracing models include Vanilla-DKT, two Long Short-Term Memory Deep Knowledge Tracing (LSTM-DKT) variants, two Dynamic Key-Value Memory Network (DKVMN) variants, and Self-Attentive  ...  Knowledge Tracing (SAKT).  ...  We are grateful for the grant by the Media Industry Research Foundation of Finland which partially funded this work. We thank the reviewers for their valuable comments that helped improved this  ... 
arXiv:2112.15072v4 fatcat:i6pkahldsnbmxn45zpvtyk5zf4

A Literature Review of Knowledge Tracing for Student Modeling : Research Trends, Models, Datasets, and Challenges

Ebedia Hilda Am, Indriana Hidayah, Sri Suning Kusumawardani
2021 Journal of Information Technology and Computer Science  
Modeling students' knowledge is a fundamental part of online learning platforms. Knowledge tracing is an application of student modeling which renowned for its ability to trace students' knowledge.  ...  Knowledge tracing ability can be used in online learning platforms for predicting learning performance and providing adaptive learning.  ...  The first author would like to thank Lembaga Pengelola Dana Pendidikan (LPDP) for supporting this research.  ... 
doi:10.25126/jitecs.202162344 fatcat:zgfx2velsvftjgcjkhqjbmsqve

Tracing and Enhancing Serendipitous Learning with ViewpointS [chapter]

Stefano A. Cerri, Philippe Lemoisson
2017 Lecture Notes in Computer Science  
These may allow to forecast the acquisition of knowledge as well as sentiment from previous events during interactions.  ...  Keywords: Brain-aware individual and collective models of human learning, Serendipitous knowledge and sentiment acquisition, Informal Learning, Web-interactive Knowledge construction and exploitation,  ...  interact with the KG (construction) or with the KM (exploitation).  ... 
doi:10.1007/978-3-319-67615-9_3 fatcat:ianswiashrbwfoa6gnixkgeba4

When is Deep Learning the Best Approach to Knowledge Tracing?

Theophile Gervet, Ken Koedinger, Jeff Schneider, Tom Mitchell
2020 Zenodo  
Logistic regression - with the right set of features - leads on datasets of moderate size or containing or containing a very large number of interactions per student, whereas Deep Knowledge Tracing leads  ...  Given a learner's history of past interactions with an ITS, a learner performance model estimates the current state of a learner's knowledge and predicts her future performance.  ...  Dan Bindman for enlightening discussions about how to evaluate and visualize the performance of the alternative models considered here.  ... 
doi:10.5281/zenodo.4143614 fatcat:tsxvcwwa6vfgpa2waomm3dg3cu
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