{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T11:15:31Z","timestamp":1758107731854,"version":"3.44.0"},"reference-count":39,"publisher":"Emerald","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,18]]},"abstract":"<jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>This research aims to introduce a knowledge tracing (KT) method that evaluates students\u2019 knowledge mastery state dynamically and precisely by analyzing their historical interaction data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Design\/methodology\/approach<\/jats:title>\n                  <jats:p>The proposed KT method is called Auxiliary Boosted Knowledge Tracing (AuBoKT). First, this paper presents a novel difficulty evaluation approach that takes into account individual abilities and the number of problem solvers, providing a more accurate estimation of exercise difficulty. In addition, this paper extracts various auxiliary features to mimic the learning process, enriching the information available for modeling students\u2019 knowledge states. Moreover, this paper proposes a sequential neural network-based performance prediction model, which not only predicts students\u2019 performance on given exercises but also implicitly models their knowledge state.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>Extensive experiments on three public real-world data sets are conducted. The experimental results highlight the significance and effectiveness of each component in our approach.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Originality\/value<\/jats:title>\n                  <jats:p>This research addresses classical test theory\u2019s limitations in exercise difficulty assessment by introducing a multi-concept fusion mechanism for comprehensive KT. This paper proposes AuBoKT, a deep learning framework leveraging auxiliary features to model fine-grained student interactions while dynamically integrating educational forgetting\/learning theories for improved knowledge state tracking accuracy.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1108\/ijwis-03-2025-0077","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T06:32:27Z","timestamp":1751351547000},"page":"519-544","source":"Crossref","is-referenced-by-count":0,"title":["AuBoKT: an auxiliary boosted knowledge tracing model"],"prefix":"10.1108","volume":"21","author":[{"given":"Kai","family":"Jiang","sequence":"first","affiliation":[{"name":"Zhejiang Development and Planning Institute , Hangzhou, , and Zhejiang University, Hangzhou, China","place":["China"]}]},{"given":"Chenyu","family":"Hou","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology , Hangzhou,","place":["China"]}]},{"given":"Zhiran","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang Development and Planning Institute , Hangzhou,","place":["China"]}]},{"given":"Yi","family":"Ma","sequence":"additional","affiliation":[{"name":"Zhuji Media Convergence Center , Shaoxing,","place":["China"]}]},{"given":"Ting","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology , Hangzhou,","place":["China"]}]}],"member":"140","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"2025091702480179700_ref001","first-page":"32","article-title":"Pytorch: an imperative style, high-performance deep learning library","author":"Adam","year":"2019","journal-title":"Conference on Neural Information Processing Systems"},{"key":"2025091702480179700_ref029","first-page":"1","volume-title":"in Applied Rasch Measurement: A Book of Exemplars","author":"Alagumalai","year":"2005"},{"key":"2025091702480179700_ref002","first-page":"253","article-title":"Knowledge tracing: modeling the acquisition of procedural knowledge","volume":"4","author":"Albert","year":"1994","journal-title":"User Modeling and User-Adapted Interaction"},{"key":"2025091702480179700_ref003","first-page":"2330","article-title":"Context-aware attentive knowledge tracing","author":"Aritra","year":"2020","journal-title":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining"},{"key":"2025091702480179700_ref004","first-page":"181","article-title":"Question difficulty and respondents\u2019 cognitive ability: the effect on data quality","volume":"13","author":"Barbel","year":"2001","journal-title":"Journal of Official Statistics-Stockholm"},{"issue":"1","key":"2025091702480179700_ref005","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1146\/annurev-statistics-041715-033702","article-title":"Item response theory","volume":"3","author":"Cai","year":"2016","journal-title":"Annual Review of Statistics and Its Application"},{"key":"2025091702480179700_ref006","first-page":"69","article-title":"Ednet: a large-scale hierarchical dataset in education","author":"Choi","year":"2020"},{"key":"2025091702480179700_ref007","first-page":"505","article-title":"Deep knowledge tracing","author":"Chris","year":"2015","journal-title":"Conference on Neural Information Processing Systems"},{"key":"2025091702480179700_ref008","first-page":"1","article-title":"Addressing two problems in deep knowledge tracing via prediction-consistent regularization","author":"Chun-Kit","year":"2018"},{"key":"2025091702480179700_ref009","first-page":"89","article-title":"Optimizing challenge in an educational game using large-scale design experiments","author":"Derek","year":"2013"},{"issue":"3","key":"2025091702480179700_ref010","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s11257-009-9063-7","article-title":"Addressing the assessment challenge with an online system that tutors as it assesses","volume":"19","author":"Feng","year":"2009","journal-title":"User Modeling and User-Adapted Interaction"},{"article-title":"A constructive error climate as an element of effective learning environments","year":"2015","author":"Gabriele","key":"2025091702480179700_ref011"},{"issue":"2","key":"2025091702480179700_ref012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3379507","article-title":"Learning or forgetting? 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