Understanding factors that contribute to differences in learning performance is important for educational practice. Learning involves cognitive factors, such as working memory capacity, as well as, non-cognitive factors, such as motivation. Therefore, it is not only important to understand how cognitive and non-cognitive factors independently influence learning performance, but also to understand how interactions between cognitive and non-cognitive factors have an impact on learning performance.
Musso, Boekaerts, Segers, and Cascallar (2019) analysed the relationship between working memory capacity, executive attention, and self-regulated learning on math performance of undergraduates. Results showed that students working memory capacity and perceived competence directly influenced math performance. In addition, perceived competence mediates the effect of working memory capacity on math performance. In other words, perceived competence appeared to increase students’ efficiency in solving the math problems, especially for tasks that were high in complexity but low in difficulty, through strategic performance. Since the effect of non-cognitive factors, such as motivational beliefs in terms of perceived competence, depend on cognitive factors, such as the availability of the working memory capacity and executive attention resources, the authors recommended that targeted interventions to train specific strategies would be needed to enhance the efficient use of working memory capacity. Also, teachers should take students’ individual differences in cognitive resources into account when reinforcing positive motivational beliefs, such as students’ perceived competence in the subject area, to enhance learning performance. There is no doubt that supporting learning requires the understanding of the intricacies of the interaction between learners’ cognition and motivation.
Musso, M. F., Boekaerts, M., Segers, M., & Cascallar, E. C. (2019). Individual differences in basic cognitive processes and self-regulated learning: Their interaction effects on math performance. Learning and Individual Differences, 71, 58-70.