The growth mindset or the belief that intelligence is malleable has garnered significant attention for its positive association with academic success. Several recent randomized trials, including the National Study of Learning Mindsets (NSLM), have been conducted to understand why, for whom, and under what contexts a growth mindset intervention can promote beneficial achievement outcomes during critical educational transitions. Prior research suggests that the NSLM intervention was particularly effective in improving low-achieving 9th graders’ GPA, while the impact varied across schools. In this study, we investigated the underlying causal mediation mechanism that might explain this impact and how the mechanism varied across different types of schools. By extending a recently developed weighting method for multisite causal mediation analysis, the analysis enhances the external and internal validity of the results. We found that challenge-seeking behavior played a significant mediating role, only in medium-achieving schools, which may partly explain the reason why the intervention worked differently across schools. We conclude by discussing implications for designing interventions that not only promote students’ growth mindsets but also foster supportive learning environments under different school contexts.
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How Do Students Talk About Intelligence?: An Investigation of Motivation, Self-Efficacy, and Mindsets in Computer Science
Undergraduate programs in computer science (CS) face high dropout rates, and many students struggle while learning to program. Studies show that perceived programming ability is a significant factor in students' decision to major in CS. Fortunately, psychology research shows that promoting the growth mindset, or the belief that intelligence grows with effort, can improve student persistence and performance. However, mindset interventions have been less successful in CS than in other domains. We conducted a small-scale interview study to explore how CS students talk about their intelligence, mindsets, and programming behaviors. We found that students' mindsets rarely aligned with definitions in the literature; some present mindsets that combine fixed and growth attributes, while others behave in ways that do not align with their mindsets. We also found that students frequently evaluate their self-efficacy by appraising their programming intelligence, using surprising criteria like typing speed and ease of debugging to measure ability. We conducted a survey study with 103 students to explore these self-assessment criteria further, and found that students use varying and conflicting criteria to evaluate intelligence in CS. We believe the criteria that students choose may interact with mindsets and impact their motivation and approach to programming, which could help explain the limited success of mindset interventions in CS.
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- Award ID(s):
- 1755628
- NSF-PAR ID:
- 10144185
- Date Published:
- Journal Name:
- Proceedings of the 2019 ACM Conference on International Computing Education Research
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Teacher professional development (PD) is a key factor in enabling teachers to develop mindsets and skills that positively impact students. It is also a key step in building capacity for computer science (CS) education in K-12 schools. Successful CS PD meets primary learning goals and enable teachers to grow their self-efficacy, asset and equity mindset, and interest in teaching CS. As part of a larger study, we conducted a secondary analysis of CS PD evaluation instruments (). We found that instruments across providers were highly dissimilar with limited data collected for measures related to teacher learning, which has implications for future K-12 CS education. Likewise, the instruments were limited in being connected to student learning and academic growth. As a way to enable PD providers to construct measures that align with known impacting factors, we offer recommendations for collecting demographic data and measuring program satisfaction, content knowledge, pedagogical content knowledge, growth and equity mindset, and self-efficacy. We also highlight questions for PD providers to consider when constructing their evaluation, including reflecting community values, the goals of the PD, and how the data collected will be used to continually improve CS programs.more » « less
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Abstract Background Motivational factors are one active area of research that aims to increase the inclusion of women in physics. One of these factors that has only recently gained traction in physics is intelligence mindset (i.e., the belief that intelligence is either innate and unchangeable or can be developed). We studied 781 students in calculus-based Physics 1 to investigate if their mindset views were separable into more nuanced dimensions, if they varied by gender/sex and over time, and if they predicted course grade. Confirmatory factor analysis was used to divide mindset survey questions along two dimensions: myself versus others and growth versus ability aspects of mindset. Paired and unpaired
t -tests were used to compare mindset factors over time and between genders, respectively. Multiple regression analysis was used to find which mindset factors were the best predictors of course grade.Results This study shows that intelligence mindset can be divided into four factors: My Ability, My Growth, Others’ Ability, and Others’ Growth. Further, it reveals that gender differences are more pronounced in the “My” categories than the “Others’” categories. At the start of the course, there are no gender differences in any mindset component, except for My Ability. However, gender differences develop in each component from the start to the end of the course, and in the My Ability category, the gender differences increase over time. Finally, we find that My Ability is the only mindset factor that predicts course grade.
Conclusion These results allow for a more nuanced view of intelligence mindset than has been suggested in previous interview and survey-based work. By looking at the differences in mindset factors over time, we see that learning environments affect women’s and men’s intelligence mindsets differently. The largest gender difference is in My Ability, the factor that best predicts course grade. This finding has implications for developing future mindset interventions and opens new opportunities to eliminate classroom inequities.
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Abstract Background Students may exhibit growth mindsets, where intelligence is seen as malleable and failures prompt more effort and new approaches, or fixed mindsets, where intelligence is seen as immutable and failures indicate lack of intelligence. One's mindset in general may be different from that for a particular domain such as engineering. Having a growth mindset predicts more positive learning outcomes.
Purpose We described the general and engineering mindsets of students in fifth‐grade U.S. classrooms (ages 10 and 11) who received engineering instruction. We explored how general mindsets may predict engineering learning outcomes and how engineering mindsets may be predicted by general mindset and other variables.
Design/Method We collected General Mindset (GM) and Engineering Mindset (EM) surveys from 2,086 fifth graders. We analyzed and correlated their GM and EM scores and survey item responses. Using hierarchical linear modeling, we measured the contribution of GM to models predicting EM and engineering learning outcomes and identified factors that predicted EM.
Results Almost three quarters of students who submitted EM surveys agreed with a growth mindset. Students with higher GM scores had higher engineering postassessment scores. Those who had a higher socioeconomic status did not receive special education services, experienced more whole‐class discussion and activity, or had a higher GM score were more likely to have a more growth‐minded EM score.
Conclusions Engineering education should help students see that they can grow in their capacities to engineer. Instructional interventions may positively impact mindset, which could be particularly important for some students. Engineering design may encourage an especially growth‐minded approach to failure experiences.
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The goal of this project is to better understand the beliefs that undergraduate students hold about their own intelligence and how these beliefs change during their undergraduate engineering education. The research team has used the theoretical framework established by Carol Dweck on Mindset and how different fixed and growth mindsets affect success. Fixed mindset individuals believe that their intelligence is an unchanging trait, while people with a growth mindset believe that through effort they can grow and develop greater intelligence. Prior researchers have shown that individuals with a growth mindset respond to challenges with higher levels of persistence, are more interested in improving upon past failures, and value criticism and effort more than those with a fixed mindset. The team developed an interview protocol from the theoretical framework. Then the team piloted the protocol and subsequently modified the protocol multiple times to ensure that the interviews provided rich qualitative data. Analytic memos were used to analyze and modify the piloted interview protocols. Once the final protocol was established, first-year and senior students were recruited to provide cross-sectional insight. The team also recruited using purposeful sampling to ensure that women and underrepresented minorities were included. To date, 19 interviews have been conducted with the final protocol. Of these interviews, four have been coded in detail using the “Attitudes, Values, and Beliefs” coding system. A codebook has also been started to categorize and interconnect the themes in the interview transcripts. This paper provides details of the protocol and coding process as well as preliminary findings on the themes extracted from the student interviews.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3291279.3339413
