000166122 001__ 166122
000166122 005__ 20260120151004.0
000166122 0247_ $$2doi$$a10.1109/TLT.2025.3630658
000166122 0248_ $$2sideral$$a147503
000166122 037__ $$aART-2025-147503
000166122 041__ $$aeng
000166122 100__ $$aDomínguez, César
000166122 245__ $$aProcess Mining Insights From a Student-Generated Questions Tool: Lower Workload and Higher Perceived Usefulness Improve the Learning Process
000166122 260__ $$c2025
000166122 5060_ $$aAccess copy available to the general public$$fUnrestricted
000166122 5203_ $$aStudent generated questions (SGQ) is a constructive educational strategy in which students elaborate their own questions about the contents being learned. Research on this learning method has been focused on academic results, but other important aspects have been overlooked. In this work, we present an innovative, online, and collaborative software application to specifically support the SGQ strategy. The traces left on the tool by 221 students organized in teams are analyzed using process mining, in order to obtain insights from the learning process and the collaboration among students. Using a new feature model to identify the key characteristics of the SGQ strategy, we focus on the quality of the generated questions, the collaborative processes among students during question generation, and the alignment of students’ behavior with the instructors’ plan. In addition, the study is enriched by the influence of some cross-cutting parameters: type of subject, academic level of students, number of questions developed by each student, and availability of the questions–answers for self-study. The results obtained suggest that students were able to formulate good-quality questions and were well-suited to the planned task; however a competitive effect between teams was detected. Furthermore, we found that neither the type of subject nor the academic level of the undergraduates significantly influenced the process. In contrast, the volume and perceived usefulness of the questions did influence the studied characteristics, with lower workload and higher usefulness positively impacting the process. The results obtained thanks to the use of educational process mining on an SGQ learning tool offer valuable guidance for future proposals of this successful learning strategy.
000166122 536__ $$9info:eu-repo/grantAgreement/ES/MICIU/PID2024-155834NB-I00
000166122 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000166122 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000166122 700__ $$aJaime, Arturo
000166122 700__ $$aPérez, Beatriz
000166122 700__ $$aRubio, Ángel Luis
000166122 700__ $$0(orcid)0000-0002-9531-1586$$aZapata, María Antonia$$uUniversidad de Zaragoza
000166122 7102_ $$15007$$2075$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ciencia Comput.Intelig.Ar
000166122 773__ $$g18 (2025), 1083-1096$$pIEEE TRANSACTIONS ON LEARNING TECHNOLOGIES$$tIEEE Transactions on Learning Technologies$$x1939-1382
000166122 8564_ $$s2208032$$uhttps://zaguan.unizar.es/record/166122/files/texto_completo.pdf$$yVersión publicada
000166122 8564_ $$s3440551$$uhttps://zaguan.unizar.es/record/166122/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000166122 909CO $$ooai:zaguan.unizar.es:166122$$particulos$$pdriver
000166122 951__ $$a2026-01-20-14:18:06
000166122 980__ $$aARTICLE