Construction of Knowledge Mapping for Vocational and Technical Education Programs and Its Application in Students' Academic Assessment

Abstract

Knowledge mapping for vocational and technical education programs are the academic assessment represent a significant advancement in educational methodologies. The process of constructing knowledge maps tailored to vocational and technical education, emphasizing their utility in enhancing students' learning experiences and academic evaluations. In this manuscript proposes a Construction of Knowledge Mapping for Vocational and Technical Education Programs and Its Application in Students Academic Assessment (CKMV-TEP-ASAS). Initially, the data is collected from the Holland Code (RIASEC) Test Responses data set.  Then, the collected data is fed to Pre-processing segment. In pre-processing stage, Disentangled Contrastive Collaborative Filtering (DCCF) is used to clean the data. The pre-processed data are fed to feature selection. Clouded Leopard Optimization Algorithm (CLOA) is used for selecting 6 features. Then features selection output is given to spiking Early-Exit Neural Networks (SEENN) used to predict vocational personality type. The weight parameters of SEENN are optimized utilizing Horse Herd Optimization Algorithm. The CKMV-TEP-ASAS-SEENN method is implemented and the metrics such as Accuracy, Computation time, Error rate, F1-score, precision, recall, Sensitivity specificity. By then, the proficiency of the proposed approach is executed in the python platform. Proposed CKMV-TEP-ASAS-SEENN method attains 23.5%, 22.5% and 24% higher accuracy, 24.06%, 23.33% and 22.98% higher Precision, 24.12%, 22.33% and 23.98% higher sensitivity compared to other  existing methods, such as Chinese Traditional Culture Teaching Application in Higher Vocational Education (RAC-TCT-HVE-RNN), Transformative Technologies in the Evaluation of a Vocational Education System (TTE-VES-CNN), and BP Neural Network Application in Teaching Quality Evaluation of Higher Vocational Education  (ABPNN-TQE-HVE-BPNN) correspondingly.