A hybrid knowledge and data-driven approach for comprehensive assessment of digital maturity in higher education

Effectively assessing the digital maturity of higher education is crucial for formulating digital transformation strategies. However, existing evaluation models often suffer from limitations such as unidimensionality, strong subjectivity, and weak generalization capabilities. To address these shortcomings, this study proposes a hybrid knowledge- and data-driven model for assessing higher education digital maturity (HADMA). Initially, influencing factors were identified through a comprehensive literature review and expert interviews. Subsequently, a combination of qualitative and quantitative methods was employed to determine the key influencing factors. Finally, a maturity discrimination model based on pattern recognition was constructed. Empirical results demonstrate that the proposed method achieves a macro precision of 0.9516, a macro recall of 0.9452, and a macro F1-score of 0.9465, indicating superior performance. These findings underscore the effectiveness of HADMA in evaluating higher education digital maturity, thereby providing a robust foundation for decision-making in digital transformation initiatives.