在法治背景下结合多目标优化算法的民事诉讼混合教学设计

Tingting Su
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引用次数: 0

摘要

当前传统的教学手段多采用灌输式教学模式。学生被动接受信息,学习积极性不高,传统教学模式亟待重构。本文构建了基于微信公众平台的民事诉讼混合式教学模式,并提供了混合式教学模式的实施流程。为了使学生获取的民事诉讼相关资源更符合微信平台的学习特点,提出了一种多目标学习路径优化算法,并采用离散二元粒子群算法对多目标学习路径进行优化求解。采用教学实验的方法,选择研究对象和方法,研究混合教学模式的教学效果,从而获得学生对混合教学模式的满意度。结果表明,通过BPSO算法,迭代次数达到150次后,其适应度值在10-5左右,20个测试函数的平均运行时间在500ms左右,学生对混合式教学模式的满意度均值为9.31分。法治背景下的民事诉讼混合式教学模式可以帮助学生更好地树立法治思维,促进学生学习能力的提高,多目标优化可以帮助学生更好地获得最优的学习路径。
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A Blended Instructional Design for Civil Litigation Combining Multi-Objective Optimization Algorithms in a Rule of Law Context
Abstract The current traditional means of teaching adopt the indoctrination teaching mode. Students passively accept information, learning enthusiasm is not high, and the traditional teaching mode needs to be reconstructed urgently. This paper constructs a blended teaching mode for civil litigation based on the WeChat public platform and provides the implementation process for the blended teaching mode. In order to enable students to obtain civil litigation-related resources more in line with the learning characteristics of the WeChat platform, a multi-objective learning path optimization algorithm is proposed, and the discrete binary particle swarm algorithm is used to optimize and solve the multi-objective learning path. The teaching experiment method is utilized to select the research object and method to study the teaching effect of the blended teaching mode and then obtain the students’ satisfaction with the blended teaching mode. The results show that through the BPSO algorithm, after the number of iterations reaches 150 times, its fitness value is around 10-5, the average running time of the 20 test functions is around 500ms, and the mean value of students’ satisfaction with the blended teaching mode is 9.31 points. The blended teaching mode of civil litigation in the context of the rule of law can help students better establish the thinking of the rule of law and promote the improvement of students’ learning ability, and multi-objective optimization can help students better obtain the optimal learning path.
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来源期刊
Applied Mathematics and Nonlinear Sciences
Applied Mathematics and Nonlinear Sciences Engineering-Engineering (miscellaneous)
CiteScore
2.90
自引率
25.80%
发文量
203
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