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Identifying and predicting key industry–education integration projects via XAI and SHAP-based machine learning models

algorithms-and-analysis-of-algorithms
artificial-intelligence
computer-education
data-mining-and-machine-learning
emerging-technologies

Abstract

Background. Identifying priority projects in industry–education integration is critical for optimizing policy support and resource allocation. While prior studies have primarily relied on qualitative assessments, there remains a lack of interpretable, data-driven approaches for large-scale priority project evaluation. This study focuses on projects related to the Management Information Systems (MIS) discipline, enabling a discipline-specific analysis.

Methods. We developed a predictive framework using a Random Forest classifier on structured project-level data, integrating SHAP (SHapley Additive exPlanations) to quantify feature importance at both global and local levels. The dataset included multiple project- and enterprise-related indicators such as equipment investment, firm age, and prior project experience. Model robustness was evaluated through five-fold cross-validation and bootstrap confidence intervals.

Results. The model achieved an average ROC-AUC of 0.957 (±0.015), with bootstrap intervals showing performance fluctuation within 0.02, indicating high stability. SHAP analysis revealed that equipment investment, firm age, and project count were the top three predictors. Higher equipment investment strongly contributed to positive classifications, while extremely low or high firm age often had negative impacts. Local SHAP force plots identified distinct positive-extreme, negative-extreme, and mixed-impact groups, reflecting heterogeneous project profiles influencing prediction outcomes.

Conclusions. The proposed framework not only delivers high predictive accuracy but also provides interpretable insights for policy formulation and project application strategy optimization. These findings demonstrate that combining ensemble learning with explainable AI can enhance the transparency and effectiveness of priority project identification, with strong potential for adaptation to other resource allocation and project selection tasks.

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