{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:51:02Z","timestamp":1747216262348,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643683164"},{"type":"electronic","value":"9781643683171"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,14]]},"abstract":"<jats:p>Online judge systems are widely used in programming courses and competitions, but they lack personalized resource recommendations. In this paper, the deep knowledge tracing (DKT) model with transformer network serving as the RNN block is used to model the learning state of programming learners, and the topic recommendation is modeled as Markov decision process, and the problem set recommendation strategy is optimized by maximum expectation algorithm. Compared with other traditional knowledge tracing models and DKT with LSTM network through experiments and data analysis, the algorithm adopted in this paper shows better effect.<\/jats:p>","DOI":"10.3233\/faia220283","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:08:24Z","timestamp":1663319304000},"source":"Crossref","is-referenced-by-count":0,"title":["Learning Exercises Recommendation for Online Judge System Based on Deep Knowledge Tracing"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9147-6669","authenticated-orcid":false,"given":"Kaiyu","family":"Dai","sequence":"first","affiliation":[{"name":"National Demonstration Center for Experimental Computer Education, School of Computer Science, Fudan University, Shanghai, China"}]},{"given":"Xiaorui","family":"Zuo","sequence":"additional","affiliation":[{"name":"School of Economics, Fudan University, Shanghai, China"}]},{"given":"Fan","family":"Liu","sequence":"additional","affiliation":[{"name":"Software School of Fudan University, Shanghai, China"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Demonstration Center for Experimental Computer Education, School of Computer Science, Fudan University, Shanghai, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","New Trends in Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220283","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:08:25Z","timestamp":1663319305000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220283"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,14]]},"ISBN":["9781643683164","9781643683171"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220283","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"type":"print","value":"0922-6389"},{"type":"electronic","value":"1879-8314"}],"subject":[],"published":{"date-parts":[[2022,9,14]]}}}