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    大数据背景下的精准个性化学习路径挖掘研究——基于AprioriAll的群体行为分析

    Research on the Mining of Precise Personalized Learning Path in Age of Big Data: Analysis of Group Learning Behaviors Based on AprioriAll

    [浏览次数:10113]

【作      者】:

姜强,赵蔚,李松,王朋娇


【关 键 词 】:

个性化学习;精准学习路径;AprioriAll算法;大数据;群体行为


【栏      目】:

学习环境与资源


【中文摘要】:

在数字化环境中,学习是对信息进行收集、汇聚、存储、共享和创造的过程,不仅涉及个体学习行为,也涉及群体行为,影响着个体知识建构过程。大数据背景下,基于AprioriAll算法,挖掘分析相同或相近学习偏好、知识水平的同一簇群体学习行为轨迹,并以学习者特征与学习对象媒体类型、理解等级、难度级别的匹配计算为基础,能够生成精准个性化学习路径,可为差异化教学提供新思路。最后,采用实验研究法,通过散点图与无回路有向图及学习效率与满意度调查,表明研究成果满足学习需求,能为学习者提供有效指引,有助于激发学习兴趣,提高学习动机,促进个性化发展。


【英文摘要】:

Under the digital environment, learning means collecting, converging, storing, sharing and creating information, involves both individual learning behavior and group behaviors, and as a result, affects individual knowledge building. In age of big data, based on AprioriAll algorithm, this study explores the learning behavior trajectory of the same group with the same or similar learning preference and knowledge level, and generates precise personalized learning path according to learners' characteristics and learning media type, the level of understanding, the matching calculation of difficulty level, which can provide new ideas for differentiating teaching. Finally, the experimental study is adopted and the learning efficiency and satisfaction are investigated through the scatter diagram and directed acyclic graph. The results indicate that the accurate personalized learning path can meet learners' learning needs, provide them effective guidance, stimulate their interest in learning, enhance their learning motivations and promote their personalized development.

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