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.