大数据支持下的网络学习行为采集模型设计

The Collection Model Design of Big Data-Supported Online Learning Behavior

网络学习行为采集是学习分析的重要步骤,是推进学习分析"落地"的重要抓手,对学习分析结果的有效性具有重要的意义。在网络学习行为采集过程中需要"明确分析目标、列举采集行为,结合研究目的、分类采集行为,分析行为特征、选择采集工具,利用采集工具、获取目标数据,整理采集数据、数据存储入库"等五个步骤对外显操作和内隐认知两个层面的数据进行采集。同时,针对采集过程中可能出现的问题做到以数据库作为采集基础、清晰定位显隐性行为、保持采集的针对性和规范性、合理应用采集数据等。

Online learning behavior collection,a main step of learning analysis,is important to the implementation of learning analysis, and is significant to the efficiency of the learning analysis results. There are five steps of collecting online learning behaviors on levels of externally explicit operation and implicit cognition. They are determining the target of analysis and listing the collection behaviors,classifying the collection behaviors according to research objectives, analyzing behavior features and selecting collection tools,getting the target data by using the collection tools,systemizing and storing the collected data. Meanwhile,for the possible problems in the process of collection, it is necessary to keep the database as basis for collection, clearly position explicit and implicit behaviors, maintain the target and normalization and rationally apply the collected data into practice.