Over the past few decades there have been multiple studies showing the effectiveness of the self-regulated learning strategies approach using a variety of methodologies (e.g., think-aloud protocols, diaries, observation). In one recent large review, John Dunlosky and colleagues evaluated the relative utility of ten learning strategies. While some of the learning strategies (e.g., highlighting, rereading) were found to have low utility in benefitting learning outcomes, the following strategies were assessed as having moderate to high utility: practice testing (high), distributed practice (high), elaborative interrogation (medium), self-explanation (medium), and interleaved practice (medium). Practice testing had the most evidence supporting its benefits for learning across context and over time.

Researchers have also recently begun to integrate the learning strategies approach with the expert performance approach. A plethora of research shows that a very deliberate type of practice involving the active use of strategies to maximize performance and overcome limitations is essential to greatness across many domains, including the arts, sciences, and sports. Excitedly, recent research suggests that the expert performance approach can also be applied to increase our understanding of the acquisition of school-based knowledge.

In one study, Kiruthiga Nandagopal and K. Anders Ericsson investigated the use of self-regulated learning strategies among advanced undergraduate bioscience majors. Because these students “made active decisions to embark on the road to acquiring expertise in the biological sciences,” they met the expert performance approach criteria. Adopting one of the key methodologies of the expert performance approach, they analyzed student diaries over the course of three weeks, estimating the presence, frequency, and duration (in terms of total number of hours) of self-regulated learning strategies. They grouped fourteen self-regulated learning strategies into six main categories: self-regulating (self-assessing, goal-setting, planning, and so on), organizing, seeking information, mnemonic usage, seeking social assistance(for instance, seeking assistance from peers, tutors, and professors), andreviewing (reviewing prior problems, notes, textbook, and such). Then they compared the diary responses among the following three groups of achievers based on their GPA before entering the course: high-achieving students (GPA > 3.7), average-achieving students (GPA ≥ 3) and low-achieving students (GPA < 3).

Comparing the diary responses of the different groups of achievers, they found that the high-achieving students reported employing a larger number of different strategies. The high-achieving students were particularly more likely to engage in organizing and transforming, seeking information, and reviewing strategies compared to the low-achieving students. Timing was also critical. While students engaged in organizing, transforming, and reviewing notes more frequently and for longer stretches of time during the midterm week than other weeks, high-achieving students sought more assistance from their peers and spent more time studying during midterm weeks compared to low-achieving students. In contrast, low-achieving students engaged in these strategies more than average-achieving students toward the end of the semester. High-achieving students also spent more time overall in study-related activities earlier in the semester compared to average and low-achieving students, whereas there was no such difference between the groups later on in the semester.

The most important learning strategies for predicting end-of-semester GPA were (1) seeking information, (2) reviewing the textbook, and (3) seeking assistance from peers during the midterm week. While the correlation between prior SAT scores and semester GPA was significant, once the most predictive learning strategies were considered, prior SAT scores didn’t explain any additional variation in end of semester GPA. Considering  IQ scores (which are highly correlated with SAT scores) are known to be excellent predictors of academic achievement, this finding is actually quite striking! While these findings certainly don't invalidate the predictive value of IQ tests, they do suggest that one of the crucial reasons why those with higher general cognitive ability tend to do so well across so many learning situations is due, in large part, to their use of efficient learning strategies that maximize learning outcomes.