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In youth, students are acquiring and developing these skills, but for many students, effectively self-regulating their learning is challenging. To design support in this regard, an in depth understanding of how and why their self-regulated learning (SRL) is enacted is needed. This study focuses on secondary school students\u2019 enacted SRL strategies in a computer-based, multi-source writing task to detect and explain differences between high- and low-performing students. To address this aim, the students\u2019 SRL processes during the task were captured using digital trace data (N\u2009=\u200950, navigational log, mouse, and keyboard data) and supplemented with stimulated recall interviews (n\u2009=\u200917). Raw trace data were parsed by implementing an existing theory-based process library that automatically detects the SRL processes. The durations and network properties of and transitions between the SRL processes of students in the highest and lowest essay score tertiles were investigated, involving the novel application of network metrics, and a qualitative content analysis for the stimulated recall data was performed. The results show that successful students differed from less successful ones regarding the time they allotted for the SRL processes, the number of distinct transitions between them, as well as transition probabilities. The successful students expressed a larger proportion of and different cyclical patterns of SRL processes during the task. The student interviews contextualise these findings and complement them by revealing qualitative differences in students\u2019 monitoring of learning. This study provides novel insights into SRL among young students in computer-based writing task and suggests approaches for designing effective, personalised support for students\u2019 adaptive learning strategies, which can be useful in developing educational technology and teacher education.<\/jats:p>","DOI":"10.1007\/s10758-024-09789-4","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T12:02:33Z","timestamp":1730116953000},"page":"1727-1756","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Secondary School Students\u2019 Enacted Self-Regulated Learning Strategies in a Computer-Based Writing Task\u2013Insights from Digital Trace Data and Interviews"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6465-4631","authenticated-orcid":false,"given":"Olli","family":"Aksela","sequence":"first","affiliation":[]},{"given":"Joni","family":"L\u00e4ms\u00e4","sequence":"additional","affiliation":[]},{"given":"Sanna","family":"J\u00e4rvel\u00e4","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"9789_CR1","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2022.813632","author":"R Azevedo","year":"2022","unstructured":"Azevedo, R., Bouchet, F., Duffy, M., Harley, J., Taub, M., Trevors, G., Cloude, E., Dever, D., et al. 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