TY - CHAP
T1 - Relationship Between Variables of Instruments Measuring Self-Regulated Learning to Improve Student Monitoring
AU - Simón-Grábalos, David
AU - Fonseca, David
AU - Aláez, Marian
AU - Martínez-Felipe, María
AU - Necchi, Silvia
AU - Romero-Yesa, Susana
AU - Amo-Filvà, Daniel
AU - Portillo, Carlos Fresneda
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Previous studies have shown that early dropout rates in universities range from 17% to 25%, depending on the institution and the program, generating a current problem for universities, and society. Assessing the degree of self-regulated learning among first-year students is essential for enhancing their motivation, integration, and outcomes, which correlates with reducing the risk of dropout. This study presents the results of two instruments that evaluate the degree of student self-regulation and discusses the relationships between variables from both instruments to determine possible relations. The aim is to streamline the monitoring process of self-regulation by potentially using only one instrument in future iterations. The results indicate an apparent disconnection between the instruments despite measuring similar aspects and highlight the need for further research to uncover hidden relationships between variables, including the use of generative artificial intelligence systems.
AB - Previous studies have shown that early dropout rates in universities range from 17% to 25%, depending on the institution and the program, generating a current problem for universities, and society. Assessing the degree of self-regulated learning among first-year students is essential for enhancing their motivation, integration, and outcomes, which correlates with reducing the risk of dropout. This study presents the results of two instruments that evaluate the degree of student self-regulation and discusses the relationships between variables from both instruments to determine possible relations. The aim is to streamline the monitoring process of self-regulation by potentially using only one instrument in future iterations. The results indicate an apparent disconnection between the instruments despite measuring similar aspects and highlight the need for further research to uncover hidden relationships between variables, including the use of generative artificial intelligence systems.
KW - academic analytics
KW - drop-out reduction
KW - educational variables correlation
KW - first-year undergraduate students
KW - Self-regulation assessment
UR - https://www.scopus.com/pages/publications/105011540327
U2 - 10.1007/978-981-96-5658-5_99
DO - 10.1007/978-981-96-5658-5_99
M3 - Chapter
AN - SCOPUS:105011540327
T3 - Lecture Notes in Educational Technology
SP - 1009
EP - 1019
BT - Lecture Notes in Educational Technology
PB - Springer Science and Business Media Deutschland GmbH
ER -