{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T16:24:14Z","timestamp":1726763054011},"reference-count":89,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T00:00:00Z","timestamp":1664928000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["asistdl.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Asso for Info Science & Tech"],"published-print":{"date-parts":[[2023,1]]},"abstract":"Abstract<\/jats:title>The transition from secondary education to higher education could be challenging for most freshmen. For students who fail to adjust to university life smoothly, their status may worsen if the university cannot offer timely and proper guidance. Helping students adapt to university life is a long\u2010term goal for any academic institution. Therefore, understanding the nature of the maladaptation phenomenon and the early prediction of \u201cat\u2010risk\u201d students are crucial tasks that urgently need to be tackled effectively. This article aims to analyze the relevant factors that affect the maladaptation phenomenon and predict this phenomenon in advance. We develop a prediction framework (MAladaptive STudEnt pRediction, MASTER) for the early prediction of students with maladaptation. First, our framework uses the SMOTE (Synthetic Minority Oversampling Technique) algorithm to solve the data label imbalance issue. Moreover, a novel ensemble algorithm, priority forest, is proposed for outputting ranks instead of binary results, which enables us to perform proactive interventions in a prioritized manner where limited education resources are available. Experimental results on real\u2010world education datasets demonstrate that the MASTER framework outperforms other state\u2010of\u2010art methods.<\/jats:p>","DOI":"10.1002\/asi.24718","type":"journal-article","created":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T08:53:49Z","timestamp":1664960029000},"page":"17-32","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lost at starting line: Predicting maladaptation of university\u00a0freshmen based on educational big data"],"prefix":"10.1002","volume":"74","author":[{"given":"Teng","family":"Guo","sequence":"first","affiliation":[{"name":"School of Software Dalian University of Technology Dalian Liaoning China"}]},{"given":"Xiaomei","family":"Bai","sequence":"additional","affiliation":[{"name":"Computing Center Anshan Normal University Anshan Liaoning China"}]},{"given":"Shihao","family":"Zhen","sequence":"additional","affiliation":[{"name":"School of Software Dalian University of Technology Dalian Liaoning China"}]},{"given":"Shagufta","family":"Abid","sequence":"additional","affiliation":[{"name":"School of Software Dalian University of Technology Dalian Liaoning China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8324-1859","authenticated-orcid":false,"given":"Feng","family":"Xia","sequence":"additional","affiliation":[{"name":"Institute of Innovation, Science and Sustainability Federation University Australia Ballarat Victoria Australia"}]}],"member":"311","published-online":{"date-parts":[[2022,10,5]]},"reference":[{"key":"e_1_2_9_2_1","first-page":"496","article-title":"Adjustment amongst first year students in Malaysian university","volume":"3","author":"Abdullah M. 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