Analysis on the Particularity of Higher Education Subject Development under the Background of Artificial Intelligence

Abstract

Subject development plays a crucial role in higher education (HE), improving student academic performance. The HE continuously requires conceptual and empirical development to deliver valuable content to the students. The subject reforms offer quality, accessibility, affordability, accountability, and equity to accomplish continual learning. The changes in higher education subjects require a continuous assessment to understand the relationship between the reform and student performance. The subject development quality is evaluated using machine learning (ML) and artificial intelligence (AI) techniques. The existing researchers use intelligent techniques to identify student academic performance. However, the exact relationship between student performance and subject changes fails to address. Therefore, higher education learning (HEL) requires improvement to manage the Higher Education Subject Development (HESD). To achieve the research goal, AdaBoost Adaptive-Bidirectional Associative Memory (AA-BAM) network is introduced in this work. The network model uses the Hebbian supervised learning (HSL) process to create the training model. The learning process has a network parameter updating procedure that reduces the total error and deviation between the academic details. In addition, the neural model uses the memory cell that stores every processing information that recalls the output patterns with maximum accuracy. The output pattern identifies the student's academic performance, which helps to analyze the quality of the subject development in institutions. The created system ensures 98.78% accuracy, showing that subject development correlates highly with student academic performance.