Frontiers in psychology · 2026
Internship engagement as a predictive pathway between an academic self-concept proxy and employability outcomes: evidence from a public undergraduate employability dataset.
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Zhong Xiaoyu, An Kai, Zhao Xin
Аннотация
BACKGROUND: Employability competencies in higher education reflect both academic indicators and experiential learning. This study examines whether a behavioral internship-engagement proxy provides a predictive indirect pathway between an academic self-concept proxy and employability outcomes using the Hierarchical Attention Mediated Employability Network (HAMEN). Because the source data are cross-sectional and observational, mediation is interpreted strictly as an associational predictive decomposition rather than a causal mechanism.
METHODS: A secondary analysis was conducted on a publicly available Kaggle dataset containing 1,200 undergraduate records of unspecified institutional and national origin. Academic Self-Concept Proxy (ASC-P) was constructed from grade point average, core-subject score, and academic consistency, whereas Behavioral Internship Engagement Proxy (IE-P) was constructed from internship and learning-exposure indicators. Data were divided into training, validation, and test sets (70%/15%/15%); preprocessing was fitted only on the training set, and HAMEN hyperparameters were selected using five-fold cross-validation. Performance was assessed using R2, RMSE, MAE, bootstrap confidence intervals, and prespecified hypothesis tests.
RESULTS: HAMEN achieved R 2 = 0.89, RMSE = 3.84, and MAE = 2.97, exceeding Linear Regression (R 2 = 0.71) and Structural Equation Modeling (R 2 = 0.78). The model-based indirect predictive component associated with ASC-P through IE-P was 0.61 (68% of the total component of 0.90; 95% CI [0.55, 0.67]; Holm-adjusted p < 0.001), whereas the direct predictive component was 0.29 (32%; Holm-adjusted p < 0.001).
CONCLUSION: HAMEN improved employability-score prediction and assigned the largest engagement attention weights to internship duration and project experience. The results indicate predictive associations only; they do not establish that academic self-beliefs or internship engagement causally produce employability outcomes because the constructs are proxy measures and the dataset provides no temporal ordering.
METHODS: A secondary analysis was conducted on a publicly available Kaggle dataset containing 1,200 undergraduate records of unspecified institutional and national origin. Academic Self-Concept Proxy (ASC-P) was constructed from grade point average, core-subject score, and academic consistency, whereas Behavioral Internship Engagement Proxy (IE-P) was constructed from internship and learning-exposure indicators. Data were divided into training, validation, and test sets (70%/15%/15%); preprocessing was fitted only on the training set, and HAMEN hyperparameters were selected using five-fold cross-validation. Performance was assessed using R2, RMSE, MAE, bootstrap confidence intervals, and prespecified hypothesis tests.
RESULTS: HAMEN achieved R 2 = 0.89, RMSE = 3.84, and MAE = 2.97, exceeding Linear Regression (R 2 = 0.71) and Structural Equation Modeling (R 2 = 0.78). The model-based indirect predictive component associated with ASC-P through IE-P was 0.61 (68% of the total component of 0.90; 95% CI [0.55, 0.67]; Holm-adjusted p < 0.001), whereas the direct predictive component was 0.29 (32%; Holm-adjusted p < 0.001).
CONCLUSION: HAMEN improved employability-score prediction and assigned the largest engagement attention weights to internship duration and project experience. The results indicate predictive associations only; they do not establish that academic self-beliefs or internship engagement causally produce employability outcomes because the constructs are proxy measures and the dataset provides no temporal ordering.