000167940 001__ 167940
000167940 005__ 20260122172506.0
000167940 0247_ $$2doi$$a10.1109/TLT.2021.3119224
000167940 0248_ $$2sideral$$a127318
000167940 037__ $$aART-2021-127318
000167940 041__ $$aeng
000167940 100__ $$aDominguez C.
000167940 245__ $$aUsing Process Mining to Analyze Time-Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool
000167940 260__ $$c2021
000167940 5060_ $$aAccess copy available to the general public$$fUnrestricted
000167940 5203_ $$aThe 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.
000167940 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000167940 590__ $$a4.433$$b2021
000167940 591__ $$aEDUCATION & EDUCATIONAL RESEARCH$$b38 / 270 = 0.141$$c2021$$dQ1$$eT1
000167940 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b41 / 112 = 0.366$$c2021$$dQ2$$eT2
000167940 592__ $$a1.288$$b2021
000167940 593__ $$aComputer Science Applications$$c2021$$dQ1
000167940 593__ $$aEngineering (miscellaneous)$$c2021$$dQ1
000167940 593__ $$aEducation$$c2021$$dQ1
000167940 594__ $$a7.4$$b2021
000167940 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000167940 700__ $$aGarcia-Izquierdo F.J.
000167940 700__ $$aJaime A.
000167940 700__ $$aPerez B.
000167940 700__ $$aRubio A.L.
000167940 700__ $$0(orcid)0000-0002-9531-1586$$aZapata Abad, M.A.$$uUniversidad de Zaragoza
000167940 7102_ $$15007$$2075$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ciencia Comput.Intelig.Ar
000167940 773__ $$g14, 5 (2021), 709-722$$pIEEE TRANSACTIONS ON LEARNING TECHNOLOGIES$$tIEEE Transactions on Learning Technologies$$x1939-1382
000167940 8564_ $$s707230$$uhttps://zaguan.unizar.es/record/167940/files/texto_completo.pdf$$yPostprint
000167940 8564_ $$s3076379$$uhttps://zaguan.unizar.es/record/167940/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000167940 909CO $$ooai:zaguan.unizar.es:167940$$particulos$$pdriver
000167940 951__ $$a2026-01-22-16:07:25
000167940 980__ $$aARTICLE