基于云计算技术的职业教育个性化学习效果评价模型

IF 3.6
Xiangyu Wang , Kang Cao
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引用次数: 0

摘要

云计算技术的出现加速了在线学习方法的发展,并在一定程度上弥补了传统教学方法固有的局限性。然而,CCT下的在线教学仍然存在教学质量不稳定的问题,因此本研究针对CCT下的职业教育个性化学习平台建立了相关的学习效果评价模型。为了更高效、准确地评估学习效果,提出了一种可调变遗传算法-反向传播神经网络(AGA-BP)。该模型引入了可调突变方法,根据遗传算法在搜索过程中的进展实时调整突变概率,避免进入局部最优状态,保证多样性的保持。该策略显著提高了算法的收敛速度和整体搜索能力。同时,利用神经网络良好的拟合特性,AGA-BP模型能够准确地学习和模拟不同学生的学习行为和学习效果。实验结果表明,该模型的均方误差为3.3883e* 10-12,适应度值为1.36,平均准确率为98.35%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Personalized learning effect evaluation model for vocational education with cloud computing technology
The advent of cloud computing technology (CCT) has expedited the advancement of online learning methodologies and, to a certain extent, compensated for the limitations inherent in traditional teaching approaches. However, online teaching under CCT still has the problem of unstable teaching quality, so the study establishes a relevant learning effect evaluation model for the personalized learning platform of vocational education under CCT. To achieve more efficient and accurate evaluation of learning effect, an adjustable variation genetic algorithm-backpropagation neural network (AGA-BP) is proposed. The model introduces an adjustable mutation approach, which adapts the mutation probability in real-time in accordance with the progress of the genetic algorithm in the search process, so as to prevent entering into local optimization and ensure the maintenance of diversity. This strategy significantly enhances the convergence speed and overall search capability of the algorithm. Meanwhile, using the excellent fitting characteristics of neural network, AGA-BP model can accurately learn and simulate different students' learning behavior and effectiveness. The experiment outcomes indicate that the model's mean square error is 3.3883e*10–12, its fitness value is 1.36, and its average accuracy is 98.35 %.
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CiteScore
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