Predicting adaptability of students in online entrepreneurship education using deep learning

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.