摘要
智能算法应用到教学领域来实现自动分组具有重要意义。针对网络学习环境下任务驱动教学中如何按最优分组方案进行小组划分的问题,综合考虑了分组问题中学习者之间的特征差异和任务难易程度等影响因素,构建了基于任务驱动分组优化问题的数学模型,提出了基于混合遗传算法的任务驱动分组优化策略。在MATLAB7.0平台上,运用混合遗传算法对任务驱动的分组优化进行了仿真实验。实验结果表明,基于混合遗传算法的任务驱动分组优化是可行且有效的。
Intelligent algorithm that applies to the education field to realize automatic grouping has great significance.In the task-driven teaching under the network learning environment for how to group divided according to the optimal grouping scheme,the factors of the characteristic differences between learners and the degree of task difficulty were considered,a mathematical model based on task-driven grouping optimization problem was built,the strategy of taskdriven grouping optimization based on hybrid genetic algorithm was proposed.We had done an simulation experiment by using hybrid genetic algorithm on MATLAB7.0platform.Experimental results show that the optimization of task-driving grouping based on hybrid genetic algorithm is feasible and effective.
出处
《计算机科学》
CSCD
北大核心
2017年第S1期105-108,共4页
Computer Science
基金
2016年国家社科基金年度项目:移动设备知识传播的情景感知服务机制及运行实证研究(16BTQ084)资助
关键词
协作学习分组
任务驱动
分组优化模型
混合遗传算法
Collaborative learning group
Task-driven
Grouping optimization model
Hybrid genetic algorithm
