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A Knowledge-Fusion Ranking System with an Attention Network for Making Assignment Recommendations

In recent decades, more teachers are using question generators to provide students with online homework. Learning-to-rank (LTR) methods can partially rank questions to address the needs of individual students and reduce their study burden. Unfortunately, ranking questions for students is not trivial...

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Autores principales: Jin, Canghong, Zhou, Yuli, Ying, Shengyu, Zhang, Chi, Wang, Weisong, Wu, Minghui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773453/
https://www.ncbi.nlm.nih.gov/pubmed/33424959
http://dx.doi.org/10.1155/2020/6748430
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author Jin, Canghong
Zhou, Yuli
Ying, Shengyu
Zhang, Chi
Wang, Weisong
Wu, Minghui
author_facet Jin, Canghong
Zhou, Yuli
Ying, Shengyu
Zhang, Chi
Wang, Weisong
Wu, Minghui
author_sort Jin, Canghong
collection PubMed
description In recent decades, more teachers are using question generators to provide students with online homework. Learning-to-rank (LTR) methods can partially rank questions to address the needs of individual students and reduce their study burden. Unfortunately, ranking questions for students is not trivial because of three main challenges: (1) discovering students' latent knowledge and cognitive level is difficult, (2) the content of quizzes can be totally different but the knowledge points of these quizzes may be inherently related, and (3) ranking models based on supervised, semisupervised, or reinforcement learning focus on the current assignment without considering past performance. In this work, we propose KFRank, a knowledge-fusion ranking model based on reinforcement learning, which considers both a student's assignment history and the relevance of quizzes with their knowledge points. First, we load students' assignment history, reorganize it using knowledge points, and calculate the effective features for ranking in terms of the relation between a student's knowledge cognitive and the question. Then, a similarity estimator is built to choose historical questions, and an attention neural network is used to calculate the attention value and update the current study state with knowledge fusion. Finally, a rank algorithm based on a Markov decision process is used to optimize the parameters. Extensive experiments were conducted on a real-life dataset spanning a year and we compared our model with the state-of-the-art ranking models (e.g., ListNET and LambdaMART) and reinforcement-learning methods (such as MDPRank). Based on top-k nDCG values, our model outperforms other methods for groups of average and weak students, whose study abilities are relatively poor and thus their behaviors are more difficult to predict.
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spelling pubmed-77734532021-01-07 A Knowledge-Fusion Ranking System with an Attention Network for Making Assignment Recommendations Jin, Canghong Zhou, Yuli Ying, Shengyu Zhang, Chi Wang, Weisong Wu, Minghui Comput Intell Neurosci Research Article In recent decades, more teachers are using question generators to provide students with online homework. Learning-to-rank (LTR) methods can partially rank questions to address the needs of individual students and reduce their study burden. Unfortunately, ranking questions for students is not trivial because of three main challenges: (1) discovering students' latent knowledge and cognitive level is difficult, (2) the content of quizzes can be totally different but the knowledge points of these quizzes may be inherently related, and (3) ranking models based on supervised, semisupervised, or reinforcement learning focus on the current assignment without considering past performance. In this work, we propose KFRank, a knowledge-fusion ranking model based on reinforcement learning, which considers both a student's assignment history and the relevance of quizzes with their knowledge points. First, we load students' assignment history, reorganize it using knowledge points, and calculate the effective features for ranking in terms of the relation between a student's knowledge cognitive and the question. Then, a similarity estimator is built to choose historical questions, and an attention neural network is used to calculate the attention value and update the current study state with knowledge fusion. Finally, a rank algorithm based on a Markov decision process is used to optimize the parameters. Extensive experiments were conducted on a real-life dataset spanning a year and we compared our model with the state-of-the-art ranking models (e.g., ListNET and LambdaMART) and reinforcement-learning methods (such as MDPRank). Based on top-k nDCG values, our model outperforms other methods for groups of average and weak students, whose study abilities are relatively poor and thus their behaviors are more difficult to predict. Hindawi 2020-12-23 /pmc/articles/PMC7773453/ /pubmed/33424959 http://dx.doi.org/10.1155/2020/6748430 Text en Copyright © 2020 Canghong Jin et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Jin, Canghong
Zhou, Yuli
Ying, Shengyu
Zhang, Chi
Wang, Weisong
Wu, Minghui
A Knowledge-Fusion Ranking System with an Attention Network for Making Assignment Recommendations
title A Knowledge-Fusion Ranking System with an Attention Network for Making Assignment Recommendations
title_full A Knowledge-Fusion Ranking System with an Attention Network for Making Assignment Recommendations
title_fullStr A Knowledge-Fusion Ranking System with an Attention Network for Making Assignment Recommendations
title_full_unstemmed A Knowledge-Fusion Ranking System with an Attention Network for Making Assignment Recommendations
title_short A Knowledge-Fusion Ranking System with an Attention Network for Making Assignment Recommendations
title_sort knowledge-fusion ranking system with an attention network for making assignment recommendations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773453/
https://www.ncbi.nlm.nih.gov/pubmed/33424959
http://dx.doi.org/10.1155/2020/6748430
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