学习分析的系统化综述:回顾、辨析及前瞻  

Systematic Analysis of Literature on Learning Analytics:Current Situation and Future Challenge

作　　者：韩锡斌[1] 黄月[1] 马婧[1] 程建钢[1] 

机构地区：[1]清华大学教育研究院,北京100084

出　　处：《清华大学教育研究》2017年第3期41-51,124,共12页Tsinghua Journal of Education

基　　金：北京市教育科学规划重点课题"基于网络教学平台的学习分析研究"(AJA13146)

摘　　要：学习分析是近几年来教育技术领域的热点研究方向之一。本文对九年来与学习分析相关的研究文献进行了系统梳理,提出了学习分析研究的综述框架,包括概念与综述、构成与模型、技术系统、组织实施和效果评价,并对这五个方面的研究进行了分析,结果表明:学习分析领域发展迅猛,研究的主要内容和核心问题都已浮现,对学习分析的内涵尚未达成一致,但已有共识,学习分析需要跨学科拓展;学习分析方法模型的研究是该领域的主体内容,包括数据选择与采集、数据分析与处理、结果呈现与反馈三个方面;技术系统和工具是支持学习分析的基础,涉及数据收集与撷取、存储、清洗、集成、分析、可视化呈现和预警等环节;学习分析组织实施和效果评价方面的研究需要考虑利益相关者、目标、教学实践、内部限制、外部约束和政策等方面的因素。然而进一步发展还面临诸多挑战,如很多学习分析的研究与实践偏离真实的教育问题;通过多种途径收集的数据难以整合应用;学习分析的结果没能及时反馈给目标群体;基于学习分析的自适应学习系统研究进展缓慢;学习分析在院校机构组织实施方面的研究还很少;对学习分析应用效果评估方面的研究欠缺等。Study of learning analytics draws attentions from many research staffs with different professional backgrounds. This paper conducts a systemic review of literature from 2008 to 2016 on learning analytics, and proposes a review framework, including concepts, models, technical tools, implementation and evaluation. The results show that the research literature of learning analytics grows rapidly, and main issues has been well researched. Moreover, the models for learning analytics is found to be the core issue, including three aspects. They are data collection and selection, data analysis and processing, and findings presentation and feedback. The technical tools support the analysis process, such as data collection and acquisition, storage, cleaning, integration, analysis, representation and visualiza- tion, and feedback. The implementation and evaluation of learning analytics need to consider some other variables, namely stakeholders, objectives, internal limitations, external constraints and so on. The challenges for learning analytics in future development are also discussed in the current study.

关 键 词：学习分析 教育数据挖掘 教育大数据 

分 类 号：G434[文化科学—教育学]