In the context of Education Informatization 3.0, this article proposes an online learning optimization method that integrates neurocognitive diagnosis and time aware recommendation to address the issues of insufficient accuracy in traditional cognitive diagnostic models and lack of personalization in recommendation systems. The study constructed an empirical Q-matrix neurocognitive diagnostic model NCD-EQ, which fitted the student exercise interaction relationship through deep neural networks and optimized the Q-matrix definition through exploratory factor analysis, significantly improving the relevance of knowledge points and diagnostic interpretability. Further design the BiasTSVD collaborative filtering recommendation algorithm, introducing time preference weight factors to dynamically adjust user preferences, effectively alleviating data sparsity and enhancing recommendation timeliness. The experiment on the ASSIST dataset shows that the BiasTSVD algorithm reduces RMSE and MAE to their minimum values at K=80, reducing errors by 4.51% compared to traditional algorithms, improving accuracy by 11.68%, and increasing recall stability by 18.69%. The study verified the advantages of the NCD-EQ model in knowledge diagnosis accuracy and the comprehensive performance improvement of the BiasTSVD algorithm in personalized recommendation. In the future, knowledge path map construction and natural language processing technology can be explored to deepen the accuracy of cognitive diagnosis and expand recommendation scenarios.

Jianheng Lu, Lu Zhang, Jiecong Huang. Research on Neurocognitive Diagnosis and Online Learning Recommendation System Based on Computer Intelligence. International Journal of Multimedia Computing (2025), Vol. 6, Issue 1: 71-80. https://doi.org/10.38007/IJMC.2025.060107.

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