Motivation is the psychological process that propels learning. When people’s basic physiological needs are satisfied, motivation is a critical driver of how much, and how deeply people learn. But how do environments affect students’ motivation? And how can we design schools and classrooms that nurture the natural curiosity and drive to learn with which people are born?

In Fall 2016, the Mindset Scholars Network launched an interdisciplinary initiative to explore how learning environments shape the mindsets students develop about learning and school. During Fall 2017, the network launched a second round of this initiative. This portfolio of research investments supports novel projects that rapidly generate scientific evidence about how institutions and educators in K-12 and higher education can convey messages to students that they belong and are valued at school, that they can grow their ability, and that what they are doing in school matters.

This second round of the initiative is being funded with generous support from the Bill & Melinda Gates Foundation. The network is pleased to announce that six projects have been selected based on an external peer review process. These projects include seven network scholars and sixteen external collaborators.

Building on the eight projects funded in Fall 2016, the following six projects were awarded in this second round. More information about the projects and teams is below:

A Neurobehavioral Investigation of the Relationship between Early Adverse Experiences and Learning Mindsets in Children
Andrei Cimpian & Nim Tottenham
This project will focus on children who have experienced a range of early caregiving adversities (e.g., neglect, abuse) and investigate which aspects of these children’s environments predict whether they adopt a growth mindset. The team will also investigate the way early adversity changes the structure and function of the developing brain, and which of these changes correlate with the adoption of a resilient, growth-oriented mindset about ability. As part of this work, the project will make a significant contribution to the measurement of mindsets for research, developing and testing a new measure of growth mindset in young children (ages six to eight)—a population for which current measures are not designed to be used.

The Effects of the African American Male Achievement Program
Thomas Dee & Emily Penner
The Oakland Unified School District recently established the first department within a public-school district that specifically targets its most vulnerable students: the Office of African American Male Achievement (AAMA). This project will be the first independent, quantitative assessment of the program. The team will use longitudinal student-level administrative data and quasi-experimental research designs to provide credibly causal evidence on the effects of AAMA participation with regard to key learning and engagement outcomes (e.g., attendance, grades, test scores, and disciplinary actions) and look into how social-psychological mechanisms (e.g., values affirmation and stereotype forewarning) mediate the effects of district initiatives like the AAMA.

Learning Mindsets, Teacher Practice, and School Organizations: Becoming Effective Learners Survey and 5Essentials
Camille Farrington & Shanette Porter
This project will link three survey datasets (Becoming Effective Learners Student Survey and Teacher Survey and the 5Essentials school survey) with student demographic data to explore how elements of school organization (e.g., leadership, professional collaboration and supports, family involvement, instructional quality, supports for student learning) are related to students’ learning mindsets and performance. The team will look into how teachers’ mindsets, beliefs about students, and teaching-related beliefs/practices are related to students’ learning mindsets, learning strategies, and course performance. They will also examine whether any of these relationships vary depending on students’ background.

Developing Spartan Persistence: Connecting Students to Resources in a Public University
Barbara Schneider, John Yun, & Soobin Kim
This project will explore whether the impact of light touch mindset interventions delivered to incoming college students can be enhanced by university programs (in this case, mentoring programs for first-year students at risk of leaving college without a degree) that also focus on supporting the development of learning mindsets and associated behaviors. The team will look into whether there is value added for students assigned to both a mindset intervention and first-year mentoring program, and whether any of these effects differ by student subgroups (e.g., race, gender, first generation college students).

Social and Environmental Influences on Motivation for Learning: The Role of Childhood Adversity in Affecting Motivation Among Older Children and Adolescents
Katie McLaughlin & Robert Crosnoe
Exposure to childhood adversity has been consistently linked to a variety of academic difficulties. Although motivational factors likely play a role in explaining these relationships, scant research has investigated this possibility. This project will examine how three specific types of adversity (trauma, deprivation, and socioeconomic disadvantage) differentially influence motivation for learning. The team will leverage two ongoing longitudinal studies of childhood adversity and developmental outcomes to determine whether diverse forms of adversity are related to mindsets about intelligence (i.e., growth/fixed mindset), sense of belonging at school, and perceived utility of school among young people 10 to 18 years old.

Language As Thought: Using Natural Language Processing to Investigate Mindsets, Learning Environments, and College Success
Sidney D’Mello & Angela Duckworth
This project will utilize a one-of-a-kind, nationwide six-year longitudinal dataset to explore how mindsets and motivation predict college success and mediate the relationship between extracurricular activities / work experiences and college success. The research team will use natural language processing and machine learning techniques to explore these relationships and to understand how they are influenced by students’ learning environments.

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