This full research paper reports findings from a multitiered intervention focused on developing growth mindset among talented, low-income undergraduate students attending a College of Computing in the northeastern United States. Rooted in theories of intelligence, a growth mindset views intelligence and skills as being developed through persistent practice and learning from mistakes, while a fixed mindset sees skills as set at birth, never evolving, with mistakes becoming insurmountable barriers to success. The program in this study was designed to develop a community of learners with a shared framework for responding to academic challenges, to combat imposter syndrome, and to support persistence in their major and enter the workforce. During their first two years as college students, three undergraduate cohorts (totaling 32 participants) experienced four semesters of growth-mindset faculty mentoring concurrent with a community-building, growth mindset-focused seminar, and in their first year experienced two growth-mindset infused introductory programming courses. To address the research question, “How do talented, financially disadvantaged computing students understand growth and fixed mindsets?”, we report on qualitative data collected each semester, for each cohort. Focus group transcripts and individual written responses were thematically analyzed, drawing from a priori frameworks (social constructivism and self-efficacy in the context of mindset theory) and emergent codes to develop categories. Discussion is presented using frames of self-determination theory and positioning theory. We discuss the impact of these findings on students, implications for growth mindset interventions and provide guidance for using educational and developmental theories in the context of studies of growth mindset.
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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
- 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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