Mapping the self in self-regulation using complex dynamic systems approach

Complex dynamic systems offer a rich platform for understanding the individual or the person-specific mechanisms. Yet, in learning analytics research and education at large, a complex dynamic system has rarely been framed, developed, or used to understand the individual student where the learning process takes place. Individual (or person-specific) methods can accurately and precisely model the individual person, create person-specific models, and devise unique parameters for each individual. Our study used the latest advances in complex systems dynamics to study the differences between group-based and individual self-regulated learning (SRL) dynamics. The findings show that SRL is a complex, dynamic system where different sub-processes influence each other resulting in the emergence of non-trivial patterns that vary across individuals and time scales, and as such far from the uniform picture commonly theorized. We found that the average SRL process does not reflect the individual SRL processes of different people. Therefore, interventions derived from the group-based SRL insights are unlikely to be effective in personalization. We posit that, if personalized interventions are needed, modelling the person with person-specific methods should be the guiding principle. Our study offered a reliable solution to model the person-specific self-regulation processes which can serve as a ground for understanding and improving individual learning and open the door for precision education.

Practitioner notes

What is already known about this topic

• Self-regulation is a catalyst for effective learning and achievement.
• Our understanding of SRL personalization comes from insights based on aggregate group-based data.

What this paper adds

• Every student has their own unique SRL process that varies from the average in non-trivial ways.
• We offer a credible method for mapping the individualized SRL process.
• SRL dynamics vary across time scales. That is, the trait dynamics are different from the state dynamics, and they should be supported differently.

Implications for practice and/or policy

• Personalization can best be achieved if based on unique person-specific idiographic methods.
• Supporting learning and SRL in particular can be more efficient when we understand the differences across time scales and persons and apply insights accordingly.
• The general SRL average should not be expected to work for everyone.