Machine learning algorithms drive research on college entrance examination question assessment

By scientifically assessing the structure, difficulty, knowledge coverage and other factors of the high school examination questions, it can not only provide theoretical support for the proposition, but also promote the fairness and impartiality of the college entrance examination.This paper analyzes and evaluates the questions of the college entrance examination based on the principal component analysis method, and discusses the various factors affecting the quality of the questions of the college entrance examination and their interrelationships.First, data were collected through questionnaire surveys to analyze the 10 factors affecting the questions of the college entrance examination.Subsequently, principal component analysis was applied to downscale these factors and refine three main components, which were the result presentation factor, the test question context factor and the solution process factor.The KMO test and Bartlett's sphericity test on the questionnaire data verified that the data were suitable for principal component analysis, and a principal component with a cumulative contribution rate of 72.437% was finally obtained.The results of the study showed that the difficulty of the test questions, the degree of knowledge synthesis, and the way of presenting information had a significant effect on the overall assessment of the high school test questions.