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Identifying Supportive Student Factors for Mindset Interventions: A Two-model Machine Learning Approach.

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Summary
This summary is machine-generated.

Machine learning identified factors influencing growth mindset intervention effectiveness. Prior achievement, navigation issues, learning motivation, and race/ethnicity predicted outcomes, with some students benefiting less.

Keywords:
21st century abilitiesData science applications in educationSecondary education

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Area of Science:

  • Educational Psychology
  • Machine Learning in Education
  • Intervention Science

Background:

  • Growth mindset interventions aim to improve students' belief in ability development through effort.
  • Student factors influencing the effectiveness of these interventions are not well understood.
  • Personalized learning approaches are needed to maximize intervention benefits for all students.

Purpose of the Study:

  • To predict the effectiveness of growth mindset interventions using machine learning.
  • To identify student-level predictors of intervention success in a large-scale experiment.
  • To analyze complex interactions between student characteristics and intervention outcomes.

Main Methods:

  • Utilized machine learning models on data from over 10,000 students in a nationwide U.S. experiment.
  • Developed one model to control for 51 student predictors and Grade Point Average (GPA).
  • Developed a second model to predict GPA change specifically due to the intervention.

Main Results:

  • Prior academic achievement, blocked navigations, self-reported reasons for learning, and race/ethnicity were key predictors.
  • Intervention effectiveness was highest for students with lower prior academic achievement.
  • Procedural difficulties (blocked navigations) and certain minoritized racial/ethnic groups predicted reduced intervention benefits.

Conclusions:

  • Machine learning can uncover complex factors affecting growth mindset intervention effectiveness.
  • Intervention design should consider potential procedural barriers and diverse student needs.
  • Findings inform the development of more equitable and effective computer-administered educational interventions.