Automated Optimization Deep Learning Model for Assessment and Guidance System Through Natural Language Processing with Reduction of Anxiety Among Students
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Abstract
The Assisted Assessment and Guidance System serves as a valuable tool in supporting individuals' learning, growth, and development. The Assisted Assessment and Guidance System with Natural Language Processing (NLP) is an innovative software application designed to provide personalized and intelligent support for assessment and guidance processes in various domains. NLP techniques are employed to analyze and understand human language, allowing the system to extract valuable insights from text-based data and provide tailored feedback and guidance. This paper proposed an Integrated Optimization Directional Clustering Classification (IODCc) for assessment of the foreign language anxiety. Additionally, the paper introduces an Integrated Optimization Directional Clustering Classification (IODCc) approach for assessing foreign language anxiety. This approach incorporates two optimization models, namely Black Widow Optimization (BWO) and Seahorse Optimization (SHO). BWO and SHO are metaheuristic optimization algorithms that simulate the behaviors of black widow spiders and seahorses, respectively, to improve the accuracy of the assessment process. The integration of these optimization models within the IODCc approach aims to enhance the accuracy and effectiveness of the foreign language anxiety assessment. Simulation analysis is performed for the data collected from the 1000 foreign language students. The experimental analysis expressed that the proposed IODCc model achieves an accuracy of 99% for the classification. The findings suggested that through pre-training of languages, the anxiety of the students will be reduced.
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