Design of Instructional Assessment System in Higher Vocational Schools Based on Improved Genetic Algorithm

As educational informatization experiences rapid expansion, its widespread integration in various facets of tertiary education has yielded commendable outcomes, but the research and practice of measuring instructional level in instructional level assessment system and assessment model are relatively backward. Scientific and effective instructional level assessment methods play an important role in giving full play to the guiding and inspiring role in the instructional stage and urging teachers to improve their instructional level. Data analysis, achieved through the utilization of data mining tools, involves the identification of meaningful, prospective, and comprehensible patterns within datasets. Ensuring and enhancing the quality of hands-on instruction, as well as conducting judicious evaluations of practical teaching, has emerged as a crucial focal point in teaching management. Therefore, using artificial intelligence (AI) algorithm to analyze the data of instructional assessment can effectively improve the instructional level of vocational schools. In this article, an improved algorithm of instructional level assessment in vocational schools is proposed by combining the individual optimal selection of genetic algorithm (GA) and the nonlinear fitting characteristics of back propagation neural network (BPNN).