Autorregúlate, a Methodology to support self-regulated learning in MOOC courses

Abstract

Global education is facing major challenges, including the lack of strategies and methods to ensure that students are truly engaged in the learning process, self-regulating their learning, and promoting successful completion of the educational process. In this research, we introduce Autorregúlate, a methodology to support self-regulated learning in Massive Open Online Courses (MOOCs), which from the design of the MOOC contributes to maintaining motivation and self-regulation throughout the course. Autorregúlate provides concrete and easy-to-implement guidelines for diverse teams in the creation of MOOCs, based on Zimmerman's self-regulation model. The methodology was validated in real MOOC courses using two evaluation instruments, the Questionnaire OSLQ and the Keller's Motivation Survey. Results show high levels of motivations (M=3,83; SD=0,7). On the other hand, results revel self-regulation strategies adoption by the participants, in particular, Goal setting (M=4,0; SD=0,7), Environmental structuring (M=4,1; SD=0,7), Task strategies (M=3,7; SD=0,7), Time management (M=3,84; SD=0,8), Seeking help (M=4,1; SD=0,8) and Self-evaluation (M=4,0; SD=0,7). Therefore, Autorregúlate support students on self-regulating their learning while achieving a sustained motivation.