Multimode Teaching Quality Evaluation Model of Higher Education Course Based on Improved Particle Swarm Optimization

Q. Liu, Zhiqiang Wang, Nan Wang, Di Tian
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Abstract

The current classroom evaluation in colleges and universities is mostly qualitative analysis, lacking of necessary quantitative means. Although the traditional analytic hierarchy process can deal with this problem quantitatively, it is difficult to test whether the judgment matrix has consistency. A multi-mode teaching quality evaluation model design technique based on an upgraded particle swarm optimization algorithm is suggested as a solution to these issues. An appropriate assessment system index is created by studying the evaluation system of teaching quality in colleges and universities using enhanced particle swarm optimization algorithm. To deal with the qualitative problem quantitatively, we use the enhanced particle swarm optimization approach, the multi-mode teaching model of education course is established. The experimental results show that the multi-mode teaching quality evaluation model based on improved particle swarm optimization algorithm is easy to operate, and can solve the subjectivity and randomness of teaching quality evaluation.
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基于改进粒子群优化的高等教育课程多模式教学质量评价模型
目前高校课堂评价多以定性分析为主,缺乏必要的定量手段。传统的层次分析法虽然可以定量地处理这一问题,但很难检验判断矩阵是否具有一致性。针对这些问题,提出了一种基于改进粒子群优化算法的多模式教学质量评价模型设计技术。通过对高校教学质量评价体系的研究,采用改进的粒子群优化算法,建立了合适的评价体系指标。为了定量处理定性问题,我们采用增强粒子群优化方法,建立了教育课程多模式教学模型。实验结果表明,基于改进粒子群优化算法的多模式教学质量评价模型易于操作,解决了教学质量评价的主观性和随机性问题。
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