Search over three decades of research on mindsets, including Mindset Scholars Network briefs and working papers, and other publications from Network studies and initiatives.
Achieving important goals is widely assumed to require confronting obstacles, failing repeatedly, and persisting in the face of frustration. Yet empirical evidence linking achievement and frustration tolerance is lacking. To facilitate work on this important topic, the authors developed and validated a novel behavioral measure of frustration tolerance: the Mirror Tracing Frustration Task (MTFT). In this 5-min task, participants allocate time between a difficult tracing task and entertaining games and videos. In two studies of young adults (Study 1: N = 148, Study 2: N = 283), the authors demonstrated that the MTFT increased frustration more than 18 other emotions, and that MTFT scores were related to self-reported frustration tolerance. Next, they assessed whether frustration tolerance correlated with similar constructs, including self-control and grit, as well as objective measures of real-world achievement. In a prospective longitudinal study of high-school seniors (N = 391), MTFT scores predicted grade-point average and standardized achievement test scores, and-more than 2 years after completing the MTFT-progress toward a college degree. Though small in size (i.e., rs ranging from .10 to .24), frustration tolerance predicted outcomes over and above a rich set of covariates, including IQ, sociodemographics, self-control, and grit. These findings demonstrate the validity of the MTFT and highlight the importance of frustration tolerance for achieving valued goals.
It is generally acknowledged that engagement plays a critical role in learning. Unfortunately, the study of engagement has been stymied by a lack of valid and efficient measures. The research team introduces the advanced, analytic, and automated (AAA) approach to measure engagement at fine-grained temporal resolutions. The AAA measurement approach is grounded in embodied theories of cognition and affect, which advocate a close coupling between thought and action. It uses machine-learned computational models to automatically infer mental states associated with engagement (e.g., interest, flow) from machine-readable behavioral and physiological signals (e.g., facial expressions, eye tracking, click-stream data) and from aspects of the environmental context.
The researchers present 15 case studies that illustrate the potential of the AAA approach for measuring engagement in digital learning environments. The paper discusses strengths and weaknesses of the AAA approach, concluding that it has significant promise to catalyze engagement research.
The first study revealed that students with more of a pro-social, self-transcendent purpose for learning persisted longer on a boring task and were less likely to drop out of college. A second study showed that a brief intervention promoting a self-transcendent purpose for learning improved high school GPA. Two other studies showed that promoting a self-transcendent purpose increased deeper learning behavior on tedious test review materials and sustained self-regulation over the course of an increasingly boring task. More self-oriented motives for learning—such as the desire to have an interesting or enjoyable career—did not, on their own, consistently produce these benefits.