基于多源数据融合的共享教育数据模型研究

Research on Shared Education Data Model Based on Multi-source Data Fusion

人工智能教育时代,传统的教育数据共享方法无法满足海量教育数据共享的时效性,进而影响智能教育系统响应的即时性与智能性,文章提出了一种基于多源数据融合的共享教育数据模型的建模方法。该建模方法首先对多源数据融合的概念、融合方法等内容进行分析,并对多种异构数据源的数据共享特性进行剖析,提取出"学习者、时间、空间、设备、事件"五维数据共享特性来对多源异构的教育数据进行数据融合分析;然后再结合国际通用的xAPI(Experience API)数据规范,对融合后的数据进行规范化分析,生成通用的教育数据交换格式;最后,基于该数据交换格式,探讨了共享教育数据模型的总体架构及实现路径,并构建一个可重用、可共享的教育数据模型,以期为今后开展基于大数据的数据共享的研究提供一套切实可行的实践指导框架。

In the era of artificial intelligence education, the traditional education data sharing method cannot meet the timeliness of the sharing of massive education data, which in turn affects the immediacy and intelligence of the response of intelligent education systems. This paper presents a modeling approach for a shared education data model based on multi-source data fusion. Firstly, this paper analyzes the concept and fusion method of multi-source data fusion, as well as the data sharing characteristics of multiple heterogeneous data sources, and extracts the five-dimensional data sharing characteristics of "learner, time, space, device, event" to perform data fusion analysis of multi-source and heterogeneous education data. Secondly, combined with internationally accepted xAPI(Experience API)data specification, the standardized analysis of the fused data is conducted to generate a common exchange format of education data. Finally, based on the data exchange format, this paper discusses the overall architecture and implementation path of the shared education data model, so as to provide a practical guidance framework for future research on big data-based data sharing.