Design of Intelligent Political Test Paper Generation Method Based on Improved Intelligent Optimization Algorithm

Qing Wan
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Abstract

With the development of artificial intelligence, computer intelligent grouping, as a research hotspot of political ideology examination paper proposition, can greatly shorten the time of generating examination papers, reduce the human cost, reduce the human factor, and improve the quality of political ideology teaching evaluation. Aiming at the problem that the current political ideology examination paper-grouping strategy method easily falls into the local optimum, a kind of intelligent paper-grouping method for political ideology examination based on the improved stock market trading optimisation algorithm is proposed. Firstly, by analyzing the traditional steps of political thought grouping, according to the index genus of the grouping problem and the condition constraints, we construct the grouping model of political thought test questions; then, combining the segmented real number coding method and the fitness function, we use the securities market trading optimization algorithm based on the Circle chaotic mapping initialization strategy and adaptive t-distribution variability strategy to solve the grouping problem of the political thought test. The experimental results show that the method can effectively find the optimal strategy of political thought exam grouping, and the test questions have higher knowledge point coverage, moderate difficulty, and more stable performance.
基于改进的智能优化算法的智能政治试卷生成方法设计
随着人工智能的发展,计算机智能组卷作为政治思想品德试卷命题的研究热点,可以大大缩短试卷生成时间,降低人力成本,减少人为因素,提高政治思想品德教学评价质量。针对目前政治思想考试组卷策略方法容易陷入局部最优的问题,提出了一种基于改进股市交易优化算法的政治思想考试智能组卷方法。首先,通过分析传统的政治思想组卷步骤,根据组卷问题的指标属和条件约束,构建政治思想试题的组卷模型;然后,结合分段实数编码方法和拟合函数,利用基于Circle混沌映射初始化策略和自适应t分布变异策略的证券市场交易优化算法解决政治思想试题的组卷问题。实验结果表明,该方法能有效找到政治思想考试分组的最优策略,试题的知识点覆盖率较高,难度适中,成绩较为稳定。
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