CRII: CHS: Automatically Praising Learning Process to Promote the Growth Mindset in Computer Science

Project: Research project

Project Details

Description

There is a pressing need to train large numbers of computer scientists to meet the demands of the 21st century economy. However, many students struggle to learn programming, and introductory courses often have low retention rates. This problem is particularly pronounced for women and underrepresented minorities, who make up only a small percentage of computer science students. Recent studies indicate that student mindsets about programming aptitude may be one factor contributing to the retention problem; introductory computer science courses have been shown to promote the fixed mindset, or the belief that intelligence is an inborn trait. Psychology research has demonstrated that students with the fixed mindset react poorly to challenge, and view mistakes as indications of low ability. In contrast, students with the growth mindset believe that intelligence is malleable. These students relish challenge, and view mistakes a natural part of the learning process.
Students’ mindsets about their intelligence have a strong impact on their academic performance. Furthermore, studies show that women and minorities are more likely to develop fixed mindsets in STEM fields due to stereotype threat, contributing to their lack of representation. As a result, growth mindset interventions have great potential to improve student retention and diversity in computer science. One of the most effective methods of teaching the growth mindset is to praise the learning process, encouraging students to attribute their successes to their process rather than innate ability. However, it is difficult to incorporate praise-based interventions into computer science courses. Enrollments are skyrocketing, making it impossible for teachers to monitor and praise each individual student’s learning process.
The proposed research addresses these challenges through the development of a new growth mindset intervention that leverages the programming environment to automatically detect and praise good learning processes in real time. Programming environments provide a unique opportunity to track and understand student learning behaviors, and offer a scalable environment for praising good practices. This research will be conducted in two phases. First, I will develop heuristics that detect good learning processes using behavioral log data, leveraging the computer science education literature and studying the behavior of fixed and growth mindset students to identify good processes. Second, I will design a programming environment extension that uses these heuristics to automatically praise good learning processes, which I will evaluate the through a quarter-long controlled study with Northwestern students.
Intellectual Merit:
The central intellectual contributions of this work include (1) the development of behavioral heuristics that identify good learning processes in the domain of computer science, (2) an in-depth study of the differences in programming behaviors between growth and fixed mindset students, and (3) the design, implementation, and evaluation of a growth mindset programming environment that automatically praises good learning processes in real time. This research will produce a deep understanding of how the fixed and growth mindsets are exhibited through programming behavior, and expand our knowledge of how to promote the growth mindset through automated, praise-based interventions at scale.
Broader Impacts:
The proposed research will produce a new growth mindset development environment that encourages students to focus on their learning process through autom
StatusActive
Effective start/end date3/15/182/28/21

Funding

  • National Science Foundation (IIS-1755628)

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