Process Mining Insights From a Student-Generated Questions Tool: Lower Workload and Higher Perceived Usefulness Improve the Learning Process

Domínguez, César ; Jaime, Arturo ; Pérez, Beatriz ; Rubio, Ángel Luis ; Zapata, María Antonia (Universidad de Zaragoza)

Resumen: Student 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.

Idioma: Inglés

DOI: 10.1109/TLT.2025.3630658

Año: 2025

Publicado en: IEEE Transactions on Learning Technologies 18 (2025), 1083-1096
ISSN: 1939-1382

Financiación: info:eu-repo/grantAgreement/ES/MICIU/PID2024-155834NB-I00

Tipo y forma: Article (Published version)

Área (Departamento): Área Ciencia Comput.Intelig.Ar (Dpto. Informát.Ingenie.Sistms.)