167940 20260122172506.0 doi 10.1109/TLT.2021.3119224 sideral 127318 ART-2021-127318 eng Dominguez C. Using Process Mining to Analyze Time-Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool 2021 The study of the relationships between self-regulated learning and formative assessment is an active line of research in the educational community. A recent review of the literature highlights that the study of these connections has been mainly unidirectional, focusing on how formative assessment helps students to self-regulate their learning, being much less explored the effect of self-regulated learning strategies on formative assessment. In this context, analyzing automatically captured students activities within online learning tools can provide us further insights on the interactions between these two topics. More specifically, this article examines the activity traces of 382 students who used an online tool to learn a programming language. The tool incorporates review exercises for promoting self-assessment (an important self-regulated learning strategy). Furthermore, the tool is used in supervised laboratories where students receive formative assessment. This study uses process mining techniques to analyze the temporal component of student behavior in both types of activities, their interaction, and how self-assessment relates to formative assessment. Some key lessons are learned: activities promoting self-assessment significantly improved students involvement in formative assessment activities; increasing self-assessment cannot compensate for a lack of effort in formative assessment. We also underline that, to the best of our knowledge, to date no research has used process mining to consider the time component in the analysis of the relationships between formative assessment and self-assessment. Access copy available to the general public Unrestricted info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 4.433 2021 EDUCATION & EDUCATIONAL RESEARCH 38 / 270 = 0.141 2021 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 41 / 112 = 0.366 2021 Q2 T2 1.288 2021 Computer Science Applications 2021 Q1 Engineering (miscellaneous) 2021 Q1 Education 2021 Q1 7.4 2021 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Garcia-Izquierdo F.J. Jaime A. Perez B. Rubio A.L. Zapata Abad, M.A. Universidad de Zaragoza (orcid)0000-0002-9531-1586 5007 075 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ciencia Comput.Intelig.Ar 14, 5 (2021), 709-722 IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES IEEE Transactions on Learning Technologies 1939-1382 707230 http://zaguan.unizar.es/record/167940/files/texto_completo.pdf Postprint 3076379 http://zaguan.unizar.es/record/167940/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:167940 articulos driver 2026-01-22-16:07:25 ARTICLE