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.

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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.

This article reports findings from the largest-ever randomized controlled trial of a growth mindset program in the United States in K-12 settings. The study combined a test for cause-and-effect (a randomized experiment) with a sample that enables claims about an entire population (a nationally representative probability sample). The study found that a short (less than one hour), online growth mindset intervention—which teaches that intellectual abilities can be developed—improved grades among lower-achieving students and increased enrollment in advanced mathematics courses among both higher- and lower-achieving students in a nationally representative sample of regular public high schools in the United States. Notably, the study identified school contexts that moderated the effects of the growth mindset intervention: the intervention had a stronger effect on grades when peer norms aligned with the messages of the intervention. In addition to its rigorous design, the study also featured independent data collection and processing, pre-registration of analyses, and corroboration of results by a blinded Bayesian analysis.

In this article, Angela Duckworth describes what led her to co-found Character Lab and the lessons learned by the organization. Character Lab is a nonprofit organization dedicated to helping children thrive using psychological science. Character Lab pursues three specific initiatives. First, the organization makes it easier for scientists to carry out applied research with school-age children. Second, Character Lab conducts interdisciplinary research, partnering with teachers, athletic coaches, artists, and other outside-the-academic-box thinkers to create interventions that build character strengths. Third, Character Lab translates insights from research into actionable advice for teachers and parents.

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.

Previous experiments have shown that college students benefit when they understand that challenges in the transition to college are common and improvable and, thus, that early struggles need not portend a permanent lack of belonging or potential. Could such an approach—called a lay theory intervention—be effective before college matriculation? The lay theory interventions raised first-year full-time college enrollment among students from socially and economically disadvantaged backgrounds exiting a high-performing charter high school network or entering a public flagship university (experiments 1 and 2) and, at a selective private university, raised disadvantaged students’ cumulative first-year grade point average (experiment 3). These gains correspond to 31–40% reductions of the raw (unadjusted) institutional achievement gaps between students from disadvantaged and nondisadvantaged backgrounds at those institutions. Further, follow-up surveys suggest that the interventions improved disadvantaged students’ overall college experiences, promoting use of student support services and the development of friendship networks and mentor relationships.

Countless studies have addressed why some individuals achieve more than others. Nevertheless, the psychology of achievement lacks a unifying conceptual framework for synthesizing these empirical insights.We propose organizing achievement-related traits by two possible mechanisms of action: Traits that determine the rate at which an individual learns a skill are talent variables and can be distinguished conceptually from traits that determine the effort an individual puts forth. This approach takes inspiration from Newtonian mechanics: achievement is akin to distance traveled, effort to time, skill to speed, and talent to acceleration. A novel prediction from this model is that individual differences in effort (but not talent) influence achievement (but not skill) more substantially over longer (rather than shorter) time intervals. Conceptualizing skill as the multiplicative product of talent and effort, and achievement as the multiplicative product of skill and effort, advances similar, but less formal, propositions by several important earlier thinkers.

We used self-report surveys to gather information on a broad set of non-cognitive skills from 1,368 8th-grade students attending Boston public schools and linked this information to administrative data on their demographics and test scores. At the student level, scales measuring conscientiousness, self-control, grit, and growth mindset are positively correlated with attendance, behavior, and test-score gains between 4th- and 8th-grade. Our results therefore highlight the importance of improved measurement of non-cognitive skills in order to capitalize on their promise as a tool to inform education practice and policy.

Attempts to measure so-called "non-cognitive" factors for the purposes of educational policy and practice are relatively recent. The authors identify serious challenges to doing so. They discuss advantages and limitations of different measures, in particular: self-report questionnaires, teacher-report questionnaires, and performance tasks. The authors discuss how each measure’s imperfections can affect its suitability for program evaluation, accountability, individual diagnosis, and practice improvement. In addition to urging caution among policymakers and practitioners, they highlight medium-term innovations that may make these measures more suitable for educational purposes.

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.

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