Understanding Chinese Students' Well-Being: A Machine Learning Study

Wang, Yi; King, Ronnel B.; Leung, Shing On

doi:10.1007/s12187-022-09997-3

Cited by 9 publications

(2 citation statements)

References 141 publications

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“…In an effort to extract meaningful features, permutation importance and pseudo partial effects were examined; finding that poor health, frequency of worry, and age, were significant factors across the three datasets evaluated. Importantly, the nonlinearity of these effects was noted, with tree-based machine learning approaches outperforming conventional linear models [55].…”

Section: Machine Learning To Understand Wellbeing Factorsmentioning

confidence: 99%

Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data

Milligan,

Harvey,

Dowthwaite

et al. 2023

2023 IEEE International Conference on Big Data (BigData)

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Life satisfaction significantly contributes to wellbeing and is linked to positive outcomes for individual people and society more broadly. However, previous research demonstrates that many factors contribute to the life satisfaction of an individual person, including: demography, socioeconomic status, health, deprivation, family life, friendships, social networks, living environment, and the broad range of behaviours enacted by the person, such as helping or volunteering. Consequently, it is challenging to disentangle the factors that contribute most significantly to life satisfaction, and thus more importantly, inform public policies designed to help foster positive wellbeing. We analyse primary survey data (n=2849) on self-reported life satisfaction in relation to a range of self-reported and observed variables associated with wellbeing. Specifically, we draw on a massive paired dataset related to use of a food sharing application in London, to augment the analysis using additional socioeconomic, environmental, and behavioural variables. Through a random forest machine learning approach and variable importance measures, we evaluate how a range of factors, that are often only evaluated individually, provide relative contributions towards life satisfaction. Result reveal that factors such as employment and social reliance contribute most significantly towards the experience of life satisfaction.

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Section: Machine Learning To Understand Wellbeing Factorsmentioning

confidence: 99%

Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data

Milligan,

Harvey,

Dowthwaite

et al. 2023

2023 IEEE International Conference on Big Data (BigData)

View full text Add to dashboard Cite

show abstract

“…ML has been used in well-being studies to improve the identification of features that predict well-being in small-area estimation in the Netherlands (Viljanen et al, 2022). Further ML approach (random forest regression) was used to better understand different factors that predict well-being among Chinese students in terms of positive/negative affect, life satisfaction, and eudaimonic well-being (Wang et al, 2023). A handful of ML approaches were used to discover factors of work-life integration that impact physician well-being (Nishi et al, 2021).…”

Section: Another Possible Approach For Well-being Studies As a Utilit...mentioning

confidence: 99%

Ethical AGI Action Selection Module

Gothard

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Hybridizing Motivational Strains: How Integrative Models Are Crucial for Advancing Motivation Science

King,

Fryer

2024

Educ Psychol Rev

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This special issue was motivated by the realization that student motivation is inherently complex and no single framework can capture it in its full richness. However, the current zeitgeist in educational psychology seems to explicitly discourage attempts at integration as researchers are incentivized to stay within their own theoretical camps. In this special issue, we asked seven research teams to revisit their theoretical assumptions and cross-fertilize their own theories with other frameworks. We also invited three distinguished luminaries to critique and comment on this undertaking. We highlighted key issues that prevent cross-fertilization of ideas across theoretical borders, surfaced potential dangers associated with naïve integration, and proffered future directions that could nudge motivation science towards a more cumulative and integrative approach.

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Understanding Chinese Students' Well-Being: A Machine Learning Study

Cited by 9 publications

References 141 publications

Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data

Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data

Ethical AGI Action Selection Module

Hybridizing Motivational Strains: How Integrative Models Are Crucial for Advancing Motivation Science

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