RT Journal Article
T1 Profiling students’ self-regulation with learning analytics: a proof of concept
A1 Liz Domínguez, Martín
A1 Llamas Nistal, Martín
A1 Caeiro Rodríguez, Manuel
A1 Mikic Fonte, Fernando Ariel
K1 1203.04 Inteligencia Artificial
K1 1203.10 Enseñanza Con Ayuda de Ordenador
K1 5801.07 Métodos Pedagógicos
AB The ability to regulate one's own learning processes is a key factor in educational scenarios.Self-regulation skills notably affect students' ef cacy when studying and academic performance, for betterorworse. However, neither students or instructors generally have proper understanding of what self-regulatedlearning is, the impact that it has or how to assess it. This paper has the purpose of showing howlearning analytics can be used in order to generate simple metrics related to several areas of students' selfregulation,in the context of a  rst-year university course. These metrics are based on data obtained from alearning management system, complemented by more speci c assessment-related data and direct answers toself-regulated learning questionnaires. As the end result, simple self-regulation pro les are obtained for eachstudent, which can be used to identify strengths and weaknesses and, potentially, help struggling students toimprove their learning habits.
PB IEEE Access
SN 21693536
YR 2022
FD 2022
LK http://hdl.handle.net/11093/4554
UL http://hdl.handle.net/11093/4554
LA eng
NO IEEE Access, 10, 71899-71913 (2022)
NO Xunta de Galicia | Ref. ED431B 2020/33
DS Investigo
RD 14-sep-2024