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Predicting How Well Adolescents Get Along with Peers and Teachers: A Machine Learning Approach

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Abstract

How well adolescents get along with others such as peers and teachers is an important aspect of adolescent development. Current research on adolescent relationship with peers and teachers is limited by classical methods that lack explicit test of predictive performance and cannot efficiently discover complex associations with potential non-linearity and higher-order interactions among a large set of predictors. Here, a transparently reported machine learning approach is utilized to overcome these limitations in concurrently predicting how well adolescents perceive themselves to get along with peers and teachers. The predictors were 99 items from four instruments examining internalizing and externalizing psychopathology, sensation-seeking, peer pressure, and parent-child conflict. The sample consisted of 3232 adolescents (M = 14.0 years, SD = 1.0 year, 49% female). Nonlinear machine learning classifiers predicted with high performance adolescent relationship with peers and teachers unlike classical methods. Using model explainability analyses at the item level, results identified influential predictors related to somatic complaints and attention problems that interacted in nonlinear ways with internalizing behaviors. In many cases, these intrapersonal predictors outcompeted in predictive power many interpersonal predictors. Overall, the results suggest the need to cast a much wider net of variables for understanding and predicting adolescent relationships, and highlight the power of a data-driven machine learning approach with implications on a predictive science of adolescence research.

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Funding

This study was partially funded by Singapore Ministry of Education (MOE) under the Education Research Funding Programme (PG 05/21 FA) and administered by National Institute of Education, Nanyang Technological University, Singapore. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Singapore MOE and NIE.

Data Sharing and Declaration

Processed data, analysis code and machine learning models are available in National Institute of Education Data Repository, https://doi.org/10.25340/R4/SA2FV8.

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Authors and Affiliations

  1. Learning Sciences and Assessment Academic Group, National Institute of Education, Nanyang Technological University, Singapore, Singapore

    Farhan Ali

  2. Psychology and Child & Human Development Academic Group, National Institute of Education, Nanyang Technological University, Singapore, Singapore

    Rebecca P. Ang

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  1. Farhan Ali
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  2. Rebecca P. Ang
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Contributions

F.A. conceived of the study, implemented analyses, and wrote the manuscript; R.P.A. conceived of the study, collected the original data, provided input to analyses, and wrote the manuscript. All authors read and approved the final manuscript.

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Correspondence to Farhan Ali.

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All study procedures received approval from Nanyang Technological University IRB prior to data collection and analyses.

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Written informed consent was obtained from each participant and their parents for data collection.

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Ali, F., Ang, R.P. Predicting How Well Adolescents Get Along with Peers and Teachers: A Machine Learning Approach. J Youth Adolescence 51, 1241–1256 (2022). https://doi.org/10.1007/s10964-022-01605-5

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