大数据驱动的学习分析技术研究进展

Research process of big⁃data⁃driven learning analysis technology

为了促进学习分析在高等院校的实施,该文对大数据驱动的学习分析技术及其应用进行了综述。首先,对分类、回归、聚类、潜在知识评估、文本和语音挖掘、社会网络分析、序列模式挖掘等学习分析技术进行分析总结;然后,根据它所解决的问题进行分类,从分析学习行为、评估学习结果和支持学生的个性化学习三方面详细阐述了学习分析技术的应用职能;最后,总结了学习分析的挑战和展望。在多学科协同下,学习分析融入了多方面的新技术来研究学习发生的内在机制和深层次的原因,揭示学习规律,从而为学习者提供更优化的学习环境。

The big⁃data⁃driven learning analysis technology and its application are reviewed to promote the implementation of learning analytics in higher education institutions.The learning analysis technologies such as classification and regression,clustering,potential knowledge assessment,text and speech mining,social network analytics and sequence pattern mining are analyzed and summarized.The learning analytics technologies are classified according to what they can solve,and then the application functions of the learning analytics technology is elaborated in the aspects of analyzing learning behavior,evaluating learning results and supporting students′personalized learning.The challenges and prospects of learning analytics are summarized.In the multidisciplinary collaboration,the learning analytics integrates various new technologies to study the internal mechanism and deep⁃seated reasons of learning occurrence,and reveal the learning pattern,so as to provide learners with a more optimized learning environment.