Construction of a CS-ELM-based assessment model for civic education within a multidimensional analysis framework

Sophisticated technology is required to evaluate college and university ideological and political education since it shapes student values, morality, and societal obligations. This paper suggests a cuckoo search-based extreme learning machine (ELM) model optimisation approach to handle this challenge. Using CS helps to maximise ELM model input weights and biases, hence enhancing stability and assessment accuracy. The work consists in three key investigations: In evaluation accuracy, CS-ELM performs better than other optimisation techniques and the benchmark ELM model. Second, ablation studies for every model component revealed the effects on the final assessment results of the CS optimisation algorithm, input weight optimisation, bias optimisation, and other factors. At last, time consumption comparison experiments reveal that in practice the CS-ELM model has low computational demand and great assessment accuracy.