Machine learning-based analysis of key factors and mediating mechanisms of career decision-making difficulties among higher vocational college students

Vollständiger Abstract

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This research explores the mechanisms through which multidimensional predictors shape career decision-making difficulties among vocational college students and identifies disparities in career decision-making difficulties across student subgroups with disparate developmental profiles. Survey participants were recruited from vocational institutions in Jiangsu, Anhui, and Guangxi provinces, with nine predictive constructs selected as research variables: sense of place, parental career-related behaviors, psychological resilience, core self-evaluations, vocational self-concept, meaning in life, career adaptability, future time perspective, and career exploration. The study first employs k-means cluster analysis to categorize student subgroups, followed by random forest modelling to pinpoint pivotal variables contributing to subgroup segmentation. Empirical findings unfold in three core aspects. First, sampled vocational students fall into three distinct clusters defined by developmental resources: high-resource, moderate-resource, and low-resource cohorts based on the nine measured variables. Second, measurable graded discrepancies in career decision-making difficulties emerge across the three subgroups. Third, future time perspective, core self-evaluations, career exploration and psychological resilience stand as dominant predictors differentiating student clusters; specifically, future time perspective negatively forecasts career decision-making difficulties via a serial mediating path sequentially through core self-evaluations, psychological resilience and career exploration. By unpacking subgroup heterogeneity of vocational students’ career decision-making difficulties from an integrated multi-factor framework, this study furnishes empirical evidence and practical implications for vocational colleges to design targeted career counselling interventions and mitigate students’ struggles in career decision-making.