The rapid expansion of online entrepreneurship education has underscored the importance of accurately assessing students’ adaptability, a key factor influencing learning effectiveness and entrepreneurial competency. Yet, predicting adaptability remains a challenge due to the heterogeneous and complex nature of educational data. To address this issue, we introduce TabNSA, a hybrid deep learning framework for tabular data that integrates Native Sparse Attention (NSA) to capture long-range feature dependencies and a TabMixer module to model non-linear interactions among diverse attributes. Experimental evaluations demonstrate that TabNSA consistently outperforms both classical machine learning baselines and recent state-of-the-art tabular models, achieving an overall accuracy of 93.58%. These results highlight the potential of TabNSA as a robust predictive tool to inform adaptive instructional design and learner support in online entrepreneurship education, ultimately enhancing employability and reducing unemployment through more effective entrepreneurial training.
Yin et al. (Mon,) studied this question.