{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T02:06:52Z","timestamp":1769566012981,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686448","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,27]]},"abstract":"<jats:p>This study employs clustering algorithms to analyze multi-dimensional learning behavior data from a blended public course at Hanshan Normal University. Our analysis reveals a moderate positive correlation between process-oriented learning behaviors \u2014 such as video viewing, task completion, and check-in rates \u2014 and final exam scores. Through cluster analysis, we identify three distinct student profiles: Stable High-Achievers, To-Be-Improved, and Marginally-Stable, which exhibit significant differences in exam behavior, assignment submission patterns, and overall performance. We further determine that chapter task points, check-in rate, assignments, and chapter quizzes are the core factors influencing students\u2019 comprehensive scores, whereas exams and course points contribute less. Notably, extreme assignment submission times coupled with very low scores (20-40 points) strongly associate procrastination and time management issues with academic underachievement. These findings offer data-driven insights for refining instructional design and implementing targeted student support.<\/jats:p>","DOI":"10.3233\/faia251692","type":"book-chapter","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:19:58Z","timestamp":1769519998000},"source":"Crossref","is-referenced-by-count":0,"title":["Data-Driven Insights for Teaching Intervention: Profiling Student Types Through Clustering in a University Public Course"],"prefix":"10.3233","author":[{"given":"Si","family":"Shen","sequence":"first","affiliation":[{"name":"School of Computer Information Engineering, Hanshan Normal University, China"}]},{"given":"Zhongxing","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer Information Engineering, Hanshan Normal University, China"}]},{"given":"Wei","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Information Engineering, Hanshan Normal University, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining XI"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251692","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:19:59Z","timestamp":1769519999000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251692"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,27]]},"ISBN":["9781643686448"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251692","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,27]]}}}