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基于数据挖掘的任务驱动式教学评估研究

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针对任务驱动式教学质量评估准确性不足、计算时间长的问题,文中提出一种基于数据挖掘的任务驱动式教学评估方法。为避免"饱和现象",首先将数据进行归一化处理,并通过线性组合获得一个新的综合指标,随后,根据相关性,选择合适的质量评价形式,利用粒子群优化技术对数据挖掘设置进行优化,从而建立任务驱动式教学质量评价模型,并基于Scala构建教学评估在线系统。将该系统应用于任务驱动式教学的质量评估中以验证其可行性,结果表明,该算法能够有效提高教学质量评估的准确性,节省计算时间。
Research on task-driven teaching evaluation based on data mining
To solve the problems of insufficient accuracy and long calculation time of task-driven teaching quality evaluation,this paper proposes a task-driven teaching evaluation method based on data mining.In order to avoid the'saturation phenomenon',the data is firstly normalized and a new comprehensive index is obtained through linear combination.Then,according to the correlation,the appropriate quality evalua-tion form is selected,and the particle swarm optimization technology is used to optimize the data mining set-ting.Thus,a task-driven teaching quality evaluation model is established,and an online teaching evalua-tion system is constructed based on Scala.The system is applied to the quality evaluation of task-driven teaching to verify the feasibility.The results show that the algorithm can effectively improve the accuracy of teaching quality evaluation and save computing time.

data miningtask-driven teachingindicator systemquality evaluationScala

李燕

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上海开放大学闵行分校,上海 200433

数据挖掘 任务驱动式教学 指标体系 质量评估 Scala

2024

信息技术
黑龙江省信息技术学会 中国电子信息产业发展研究院 中国信息产业部电子信息中心

信息技术

CSTPCD
影响因子:0.413
ISSN:1009-2552
年,卷(期):2024.(12)