The use of self-regulating learning skills and strategies within the online learning environment has been shown to play a significant role in a student’s level of academic achievement. In a recent study published by Barnard-Brak, Lan, and Paton, they discuss their research into the classification of five distinct profiles of self-regulated learning. They found that academic achievement varied significantly across the spectrum containing these five profiles.
Self-regulated learning is basically the active behaviors employed by students during the learning process in order to achieve a desired outcome. These behaviors include learning tactics such as goal setting, time management, task strategies, environment structuring, and help-seeking. In the study by Barnard-Brak et al., the authors approached the correlation between academic achievement and self-regulated learning skills via the social theoretical framework. This framework indicates that development of self-regulated learning skills comes from the interaction between personal, behavioral, and environmental factors. Using this framework allows for the use of the three-phase development model, proposed by Zimmerman in 1998, which outlines the development of self-reflection skillsets. In short, Zimmerman’s three-phase model consists of the following cyclical phases: forethought, performance control or volitional, and self-reflection. The social theoretical framework suggests that students cycle through these three phases continuously while developing a unique set of skills and strategies.
This study found five distinct profiles of self-regulated learning within online learning environments. Please note that learners can straddle two profiles.
- Super self-regulators: Students in this profile use a large variety of self-regulating strategies on a regular basis and reach the highest levels of academic achievement ( 20% of learners)
- Competent self-regulators: Students in this profile make up the bulk of online learners. They typically invoke self-regulating strategies as much as needed, yet are hesitant to do much more (39% of learners)
- Performance/reflection self-regulators: Students in this profile typically use task strategies, time management, help seeking, and self-evaluation instead of other self-regulating strategies. These students are more concerned with self-regulation in a reactive/post hoc sense, as opposed to being proactive (12% of learners).
- Fore-thinking self-regulators: Students in this profile tend to use goal setting and environment structuring strategies over the other self-regulating strategies (16% of learners).
- Non, or minimal self-regulators: Students in this profile do not typically have any organized self-regulated learning tendencies and show the lowest levels of academic achievement (22% of learners).
Learners who fit into the performance/reflection and fore-thinking profiles would seem to benefit greatly from assistance in incorporating alternative or additional strategies during their coursework.
An interesting exercise to try would be to have students self-identify with one or two of the self-regulated profiles. This would reinforce the notion that online learning does require a good amount of self-regulation.
Online instructors can gain a greater sense of which students need assistance by using performance monitoring tools integrated into the LMS.
Profiles in Self-Regulated Learning in the Online Learning Environment. Lucy Barnard-Bark, William Y. Lan, and Valerie Osland Paton