The effect of shadow education or private supplementary education (PSE) on school achievement has been prolifically studied, but its impact on well-being remains understudied. This study examines the heterogeneous effect of PSE participation on school achievement and depression symptoms among high schoolers in Taiwan. The study uses panel data of the Taiwan Upper Secondary Database (TUSD) in the 2014 and 2015 academic years. We join the inverse-probability-of-treatment weighting (IPTW) approach and the seemingly unrelated regression (SUR) model to estimate the effects of PSE participation patterns on two correlated outcomes, comprehensive assessment of high school entrance examination and self-reported depression symptoms in the 11th grade. The latent class analysis identifies five PSE participation patterns: always-taker, early-adopter, dropout, late-adopter, and explorer, to predict the effect of PSE on the scores of entrance examination and later depression symptoms in high school (n = 7708, mean age = 15.33). The findings suggest that PSE participation in junior high is positively associated with academic achievement. However, PSE participation also increases depression symptoms, particularly in the case of always-takers. In other words, while always-takers increase their school achievement in transition into high school, their risks of suffering from depression are also higher than their peers.

As AI and machine learning permeates every area of life, its use to ameliorate educational inequities becomes of great interest. One important application of machine learning within education is to help students increase their alignment of career choice, educational attainment, and projected salary. Alignment theory has shown that having alignment yields higher educational attainment for students. Using the app, Init2Winit, which has students play a game which gives them points for correct alignment, this chapter explores how machine learning, in particular using a decision tree, can give insights into game use and its relation to educational expectations. This model builds a basis for the improvement of Init2Winit to increase student educational expectations through counselor interventions and how other educational applications could use machine learning for insights to improve educational outcomes. The model can decrease educational inequities by increasing educational attainment for those in underrepresented minorities.

Purpose: Recently, the institutional performance model has been used to explain the increased distrust of health care system by arguing that distrust is a function of individuals’ perceptions on the quality of life in neighborhood and social institutions. We examined (1) whether individuals assess two dimensions of distrust consistently, (2) if the multilevel institutional performance model explains the variation of distrust across neighborhoods, and (3) how distrust patterns affect preventive health care behaviors. Methodology: Using data from 9,497 respondents in 914 census tracts (neighborhoods) in Philadelphia, we examined the patterns of how individuals evaluate the competence and values distrust using the Multilevel Latent Class Analysis (MLCA), and then investigated how neighborhood environment factors are associated with distrust patterns. Finally, we used regression to examine the relationships between distrust patterns and preventive health care. Findings: The MLCA identified four distrust patterns: Believers, Doubters, Competence Skeptics, and Values Skeptics. We found that 55 percent of the individuals evaluated competence and values distrust coherently, with Believers reporting low levels and Doubters having high levels of distrust. Competence and Values Skeptics assessed distrust inconsistently. Believers were the least likely to reside in socioeconomically disadvantaged and racially segregated neighborhoods among these patterns. In contrast to Doubters, Believers were more likely to use preventive health care, even after controlling for other socioeconomic factors including insurance coverage. Practical implications: Our findings suggest that distrust patterns are a function of neighborhood conditions and distrust patterns are associated with preventive health care. This study provides important policy implications for health care and future interventions.