{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T18:45:55Z","timestamp":1760985955026,"version":"3.37.3"},"reference-count":36,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T00:00:00Z","timestamp":1608681600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Science and Technology Plan Project","award":["2021C02060","2021C01164","2019C03005","LY21F020003","Y201941372"],"award-info":[{"award-number":["2021C02060","2021C01164","2019C03005","LY21F020003","Y201941372"]}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["2021C02060","2021C01164","2019C03005","LY21F020003","Y201941372"],"award-info":[{"award-number":["2021C02060","2021C01164","2019C03005","LY21F020003","Y201941372"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Project of Education of Zhejiang Province","award":["2021C02060","2021C01164","2019C03005","LY21F020003","Y201941372"],"award-info":[{"award-number":["2021C02060","2021C01164","2019C03005","LY21F020003","Y201941372"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2020,12,23]]},"abstract":"<jats:p>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\u2019 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\u2019s assignment history and the relevance of quizzes with their knowledge points. First, we load students\u2019 assignment history, reorganize it using knowledge points, and calculate the effective features for ranking in terms of the relation between a student\u2019s 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-<jats:inline-formula><a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><a:mi>k<\/a:mi><\/a:math><\/jats:inline-formula>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.<\/jats:p>","DOI":"10.1155\/2020\/6748430","type":"journal-article","created":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T23:20:40Z","timestamp":1608765640000},"page":"1-11","source":"Crossref","is-referenced-by-count":7,"title":["A Knowledge-Fusion Ranking System with an Attention Network for Making Assignment Recommendations"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9774-9688","authenticated-orcid":true,"given":"Canghong","family":"Jin","sequence":"first","affiliation":[{"name":"Zhejiang University City College, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuli","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang University City College, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengyu","family":"Ying","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weisong","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang Xuehai Education Technology Co., Ltd., Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8179-7119","authenticated-orcid":true,"given":"Minghui","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University City College, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","article-title":"The effectiveness of online learning: beyond No significant difference and future horizons","volume":"11","author":"T. Nguyen","year":"2015","journal-title":"Journal of Online Learning & Teaching"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1007\/s10734-009-9247-4"},{"first-page":"493","article-title":"Learning to rank for recommender systems","author":"A. Karatzoglou","key":"3"},{"first-page":"2239","article-title":"Learning to rank for hybrid recommendation","author":"J. Sun","key":"4"},{"first-page":"409","article-title":"SoRank: incorporating social information into learning to rank models for recommendation","author":"W. Yao","key":"5"},{"first-page":"945","article-title":"Reinforcement learning to rank with Markov decision process","author":"Z. Wei","key":"6"},{"author":"R. Wu","key":"7","article-title":"Cognitive modelling for predicting examinee performance"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1145\/3168361"},{"first-page":"64","article-title":"Discriminative models for information retrieval","author":"N. 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