The mediating role of psychological resilience in the relationship between deep learning approach and mathematical creativity: integrating structural equation model and network analysis

Background Previous scholars have conducted a series of explorations on the relationship between learning approaches and mathematical creativity, but it remains unclear how deep learning approach predicts mathematical creativity. According to the componential theory of creativity, psychological resilience may be one of the mediating processes, but the network relationships among psychological resilience, learning approaches, and mathematical creativity are currently unclear. Methods To clarify these relationships, we recruited 986 Chinese university students to complete questionnaire surveys, employing an integrated approach combining structural equation modeling and network analysis for the first time to reveal the mediating relationship and network relationships among learning approaches, psychological resilience, and mathematical creativity. Results The study found that deep learning approach positively predicted mathematical creativity through the mediation of psychological resilience. In the network, node centrality demonstrates the strongest characteristics across three dimensions: strength, closeness, and expected influences, followed by psychological resilience, while deep learning methods exhibit the weakest characteristics. Therefore, compared to stimulating mathematical creativity at the motivational level, directly stimulating it from the volitional level like psychological resilience would be a more effective approach. Conclusion The findings of this study contribute to helping educational practitioners understand the internal relationships among learning approaches, psychological resilience, and mathematical creativity.