RT conferenceObject
T1 Monitoring students’ self-regulation as a basis for an early warning system
A1 Liz Domínguez, Martín
A1 Caeiro Rodríguez, Manuel
A1 Llamas Nistal, Martín
A1 Mikic Fonte, Fernando Ariel
K1 1209.03 Análisis de Datos
K1 1203.04 Inteligencia Artificial
AB Among the elements that determine a student’s academic success, their ability to regulate their own learning processes is an   important, yet typically underrated factor. It is possible for students to improve their self-regulated learning skills, even at university levels. However, they are often unaware of their own behavior. Moreover, instructors are usually not prepared to assess students’ self-regulation. This paper presents a learning analytics solution which focuses on rating selfregulation skills, separated in several different categories, using activity and performance data from a LMS, as well as self-reported student data via questionnaires. It is implemented as an early warning system, offering the possibility of detecting students whose poor SRL profile puts them at risk of academic underperformance. As of the date of this writing, this is still a work in progress, and is being tested in the context of a first year college engineering course.
SN 16130073
YR 2021
FD 2021
LK http://hdl.handle.net/11093/4627
UL http://hdl.handle.net/11093/4627
LA eng
DS Investigo
RD 13-oct-2024