Literature DB >> 35528023

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

Nigel Bosch1.   

Abstract

Growth mindset interventions foster students' beliefs that their abilities can grow through effort and appropriate strategies. However, not every student benefits from such interventions - yet research identifying which student factors support growth mindset interventions is sparse. In this study, we utilized machine learning methods to predict growth mindset effectiveness in a nationwide experiment in the U.S. with over 10,000 students. These methods enable analysis of arbitrarily-complex interactions between combinations of student-level predictor variables and intervention outcome, defined as the improvement in grade point average (GPA) during the transition to high school. We utilized two separate machine learning models: one to control for complex relationships between 51 student-level predictors and GPA, and one to predict the change in GPA due to the intervention. We analyzed the trained models to discover which features influenced model predictions most, finding that prior academic achievement, blocked navigations (attempting to navigate through the intervention software too quickly), self-reported reasons for learning, and race/ethnicity were the most important predictors in the model for predicting intervention effectiveness. As in previous research, we found that the intervention was most effective for students with prior low academic achievement. Unique to this study, we found that blocked navigations predicted an intervention effect as low as 0.185 GPA points (on a 0-4 scale) less than the mean. This was a notable negative prediction given that the mean intervention effect in our sample was just 0.026 GPA points, though few students (4.4%) experienced a substantial number of blocked navigation events. We also found that some minoritized students were predicted to benefit less (or even not at all) from the intervention. Our findings have implications for the design of computer-administered growth mindset interventions, especially in relation to students who experience procedural difficulties completing the intervention.

Entities:  

Keywords:  21st century abilities; Data science applications in education; Secondary education

Year:  2021        PMID: 35528023      PMCID: PMC9075678          DOI: 10.1016/j.compedu.2021.104190

Source DB:  PubMed          Journal:  Comput Educ        ISSN: 0360-1315            Impact factor:   11.182


  18 in total

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Authors:  Yue Li; Timothy C Bates
Journal:  J Exp Psychol Gen       Date:  2019-09

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Journal:  J Clin Epidemiol       Date:  2019-02-11       Impact factor: 6.437

5.  Do rewards reinforce the growth mindset?: Joint effects of the growth mindset and incentive schemes in a field intervention.

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Journal:  J Exp Psychol Gen       Date:  2017-08-14

6.  Does Mindset Intervention Predict Students' Daily Experience in Classrooms? A Comparison of Seventh and Ninth Graders' Trajectories.

Authors:  Jennifer A Schmidt; Lee Shumow; Hayal Z Kackar-Cam
Journal:  J Youth Adolesc       Date:  2016-04-22

7.  Growth mindset tempers the effects of poverty on academic achievement.

Authors:  Susana Claro; David Paunesku; Carol S Dweck
Journal:  Proc Natl Acad Sci U S A       Date:  2016-07-18       Impact factor: 11.205

8.  To What Extent and Under Which Circumstances Are Growth Mind-Sets Important to Academic Achievement? Two Meta-Analyses.

Authors:  Victoria F Sisk; Alexander P Burgoyne; Jingze Sun; Jennifer L Butler; Brooke N Macnamara
Journal:  Psychol Sci       Date:  2018-03-05

9.  The Transition to High School: Current Knowledge, Future Directions.

Authors:  Aprile D Benner
Journal:  Educ Psychol Rev       Date:  2011-04-01

10.  An online growth mindset intervention in a sample of rural adolescent girls.

Authors:  Jeni L Burnette; Michelle V Russell; Crystal L Hoyt; Kasey Orvidas; Laura Widman
Journal:  Br J Educ Psychol       Date:  2017-09-27
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  1 in total

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Journal:  Front Psychol       Date:  2022-09-23
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