{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:41:48Z","timestamp":1760370108463,"version":"build-2065373602"},"reference-count":90,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T00:00:00Z","timestamp":1738713600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["bera-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brit J Educational Tech"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:sec><jats:label\/><jats:p>Smartphone addiction (SA) has become a pervasive issue among university students. Therefore, it is important to better understand the conditions under which SA develops. Previous studies indicate that fear of missing out (FoMO), a psychological barrier to behavioural self\u2010regulation, is often associated with SA risk. In the pedagogical context, poor self\u2010regulation may manifest as lack of self\u2010regulated learning skills (SRLSs), which may, in turn, be associated with the adoption of a superficial approach to learning tasks. Therefore, the aim of the present study was to examine and model the associations between deep and surface learning approaches, SRLSs, SA and FoMO among university students. The sample comprised 687 university students, and structural equation modelling (SEM) was used to analyse the data. The results indicated that SLRSs were positively associated with deep learning, and negatively associated with surface learning. It was also shown that higher SRLSs were associated with lower risk of FoMO and SA. However, while SRLSs may help reduce the level of SA among surface learners by helping them overcome FoMO, the same may not be said for students with a deep learning approach, whose reduced risk of SA due to higher SRLSs was not explained through FoMO. Based on the findings, interventions that aim to improve SRLSs appear warranted, as these may help reduce SA.<\/jats:p><\/jats:sec><jats:sec><jats:label\/><jats:p>\n<jats:boxed-text content-type=\"box\" position=\"anchor\"><jats:caption><jats:title>Practitioner Notes<\/jats:title><\/jats:caption><jats:p>What is already known about this topic\n<jats:list list-type=\"bullet\">\n<jats:list-item><jats:p>Fear of missing out (FoMO) is commonly associated with smartphone addiction (SA) risk.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Smartphone notifications disrupt the learning activities of surface learners and FoMO may be the reason.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>FoMO is considered to be a challenge for behavioural self\u2010regulation.<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p><jats:p>What this paper adds\n<jats:list list-type=\"bullet\">\n<jats:list-item><jats:p>Higher levels of self\u2010regulated learning skills (SRLSs) are associated with deeper approaches to learning.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Although a deeper approach to learning is associated with lower SA risk, a reduction in FoMO is irrelevant in explaining this effect.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>As far as surface learners are concerned, better SRLSs are associated with FoMO but are not associated with lower SA risk.<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p><jats:p>Implications for practice and\/or policy\n<jats:list list-type=\"bullet\">\n<jats:list-item><jats:p>For deep learners, interventions that support the development of SRLSs are advised because these are important not only for fostering a deep approach to learning but also for helping reduce the risk of SA.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Further research is necessary to identify the underlying mechanism by which improved SRLSs are associated with lower SA risk among deep learners.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Further research is necessary to identify factors other than FoMO that may be associated with SA risk among surface learners.<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p><\/jats:boxed-text>\n<\/jats:p><\/jats:sec>","DOI":"10.1111\/bjet.13572","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T05:05:16Z","timestamp":1738731916000},"page":"2296-2320","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The role of self\u2010regulated learning in modelling the relationships between learning approaches, <scp>FoMO<\/scp> and smartphone addiction among university students"],"prefix":"10.1111","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4688-043X","authenticated-orcid":false,"given":"Deniz Mertkan","family":"Gezgin","sequence":"first","affiliation":[{"name":"Department of Computer Education and 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