Research Library

Search over three decades of research on mindsets, including Mindset Scholars Network briefs and working papers, and other publications from Network studies and initiatives.


Developmental Stage

Publication Type

This study answered novel questions about the connection between high school extracurricular dosage (number of activities and participation duration) and the attainment of a bachelor’s degree. Using data from the Common Application and the National Student Clearinghouse (N = 311,308), we found that greater extracurricular participation positively predicted bachelor’s degree attainment. However, among students who ultimately earned a bachelor’s degree, participating in more than a moderate number of high school activities (3 or 4) predicted decreasing odds of earning a bachelor’s degree on time (within 4 years). This effect intensified as participation duration increased, such that students who participated in the greatest number of high school activities for the most years were the most likely to delay college graduation.

It is widely acknowledged that the language we use reflects numerous psychological constructs, including our thoughts, feelings, and desires. Can the so called "noncognitive" traits with known links to success, such as growth mindset, leadership ability, and intrinsic motivation, be similarly revealed through language? We investigated this question by analyzing students' 150-word open-ended descriptions of their own extracurricular activities or work experiences included in their college applications. We used the Common Application-National Student Clearinghouse data set, a six-year longitudinal dataset that includes college application data and graduation outcomes for 278,201 U.S. high-school students. We first developed a coding scheme from a stratified sample of 4,000 essays and used it to code seven traits: growth mindset, perseverance, goal orientation, leadership, psychological connection (intrinsic motivation), self-transcendent (prosocial) purpose, and team orientation, along with earned accolades. Then, we used standard classifiers with bag-of-n-grams as features and deep learning techniques (recurrent neural networks) with word embeddings to automate the coding. The models demonstrated convergent validity with the human coding with AUCs ranging from .770 to .925 and correlations ranging from .418 to .734. There was also evidence of discriminant validity in the pattern of inter-correlations (rs between -.206 to .306) for both human- and model-coded traits. Finally, the models demonstrated incremental predictive validity in predicting six-year graduation outcomes net of sociodemographics, intelligence, academic achievement, and institutional graduation rates. We conclude that language provides a lens into noncognitive traits important for college success, which can be captured with automated methods.

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.