{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:02:29Z","timestamp":1787065349786,"version":"build-2736575974"},"reference-count":57,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["iaap-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Applied Psych Health &amp;amp; Well"],"published-print":{"date-parts":[[2025,2,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Children with special educational needs (SEN) are a diverse group facing numerous challenges related to well\u2010being and mental health. Understanding the predictors of well\u2010being in this population requires the incorporation of diverse factors along with approaches that can uncover complexity in how these factors work together to influence well\u2010being. We longitudinally predicted subjective well\u2010being in a group of children with diverse special educational needs (N\u2009=\u2009499; M\u2009=\u20098.4\u2009\u00b1\u20090.9\u00a0years). Thirty\u2010two variables \u2010 ranging from demographics to various categories of life experiences \u2010 were used as predictors for both nonlinear machine learning and classical linear classifiers. Nonlinear machine learning classifiers exhibited much performance in predicting subjective well\u2010being (F1 score\u2009=\u20090.72 to 0.84) compared to traditional linear classifiers. Overall, across all children, prior subjective well\u2010being, numeracy, literacy skills, and interpersonal dimensions played important roles. However, clustering further identified four distinct clusters sharing important predictors: a \u2018socializer\u2019 cluster dominated by interpersonal functioning predictors, an \u2018analyzer\u2019 cluster emphasizing academic skills predictors, and two clusters with more diverse sets of important predictors. Our research highlights the multiple pathways toward well\u2010being in children with SEN as uncovered by machine learning, with implications for understanding and supporting their well\u2010being.<\/jats:p>","DOI":"10.1111\/aphw.12625","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T01:09:17Z","timestamp":1731373757000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Subjective well\u2010being of children with special educational needs: Longitudinal predictors using machine learning"],"prefix":"10.1111","volume":"17","author":[{"given":"Amanda Swee\u2010Ching","family":"Tan","sequence":"first","affiliation":[{"name":"Learning Sciences and Assessment, National Institute of Education Nanyang Technological University  Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6773-893X","authenticated-orcid":false,"given":"Farhan","family":"Ali","sequence":"additional","affiliation":[{"name":"Learning Sciences and Assessment, National Institute of Education Nanyang Technological University  Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8809-902X","authenticated-orcid":false,"given":"Kenneth K.","family":"Poon","sequence":"additional","affiliation":[{"name":"Psychology and Child &amp; Human Development, National Institute of Education Nanyang Technological University  Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10964-022-01605-5"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/educsci13111103"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10964-016-0450-1"},{"key":"e_1_2_11_5_1","unstructured":"Barnes M. &Harrison E.(2017).The wellbeing of secondary school pupils with special educational needs (social science in government p. 112). 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