NIGEL BOSCH

We are seeking a motivated researcher to join our project titled Identifying When Mindset Interventions are Effective for Students: A Two-Model Machine Learning Approach. This exploratory study aims to determine which student- and school-level characteristics (e.g., math anxiety, sense of belonging) influence the efficacy of mindset interventions in improving learning outcomes.

About the role

The researcher will train two machine learning models using data from the National Study of Learning Mindsets. The first model will predict longitudinal grade improvement from student characteristics within the control condition, identifying key predictive factors. The second model will analyze experimental condition data to predict the difference between expected and actual grade improvement, revealing characteristics that influence intervention efficacy.

Responsibilities

• Develop and train machine learning models to analyze student and school-level data.
• Identify key characteristics predictive of grade improvement and intervention efficacy.
• Collaborate with the research team to interpret model results and their implications for mindset interventions.
• Contribute to the preparation of research publications and presentations.

Qualifications

• Ph.D. in Computer Science, Education, Psychology, Statistics, or a related field.
• Experience with machine learning, particularly in predictive modeling and data analysis.
• Familiarity with educational data, mindset research, or related social science domains is a plus.
• Strong programming skills (e.g., Python, R) and experience with machine learning libraries (e.g., scikit-learn, TensorFlow).

About the researcher

The project is associated with Nigel Bosch, Ph.D. in Computer Science and former Postdoctoral Researcher at the National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign. The work builds on his research related to learning mindsets, with contributions from a team of fellows including Guillaume Basse, Michael Broda, Alexander Browman, Jazmin Brown-Iannuzzi, Nicholas Buttrick, Maithreyi Gopalan, Soobin Kim, Alison Koenka, Manyu Li, Xu Qin, Eunjin Seo, and Nicole Sorhagen.

For related publications, see Identifying supportive contexts for mindset interventions: A two-model machine learning approach and other works in our library.