【作 者】：
张婧婧， 杨业宏， 王烨宇， 陈 丽
【关 键 词 】：
在线学习； MOOC； 网络分析； 交互
【栏 目】：
网络教育
【中文摘要】：
在线学习呈现大规模性、开放性、灵活性、动态性等特征，使得学习交互的本质发生了变化，亟待新的研究理论与方法来发现交互与演化的新规律。学习与交互不仅仅局限于小组之中，个体之间的交互正通过某种形式不断延展开来，形成一个交互的网络，使得从网络分析的视角对学习与交互展开研究成为可能。研究选取23篇英文论文作为文本数据，从网络分析的重新词化、基本特征、常用网络指标、数据筛选方法、研究问题、理论框架、模型与方法等多个方面，对使用网络分析方法探究在线学习交互的研究进行了系统分析。研究发现，网络分析是研究在线学习大规模交互的重要方法，但传统的静态网络分析指标不足以支撑对在线学习交互展开深入研究，网络的动态演化机制将成为新的趋势和研究重点，相关实证研究亟待开展。基于多学科理论，将如何剔除交互数据中的“噪音”作为研究设计的一部分，综合运用网络分析、内容与文本分析、统计分析等方法，开展实证研究将帮助人们重新认识社会化学习与开放灵活的在线学习，使得新理论的产生成为可能。
【英文摘要】：
Online learning is characterized by large-scale, openness, flexibility, dynamics and other characteristics, which have changed the nature of learning interaction. New research theories and methods are urgently needed to discover new laws of interaction and evolution. Learning and interaction are not constricted to groups. The interaction between individuals is being extended in a way to form an interactive network, making it possible to conduct research on learning and interaction from the perspective of network analysis. In this study, 23 English papers are selected as text data to illustrate online learning interaction using network analysis from multiple aspects such as re-lexicalization, basic features, network indicators, data screening methods, research questions, theoretical framework, models and methods. It is found that network analysis is an important method to study large-scale interaction in online learning. However, traditional static network indicators are incapable of supporting the in-depth study of online learning interaction. The dynamic evolution mechanism of network will become a new trend and research focus. Based on the multi-disciplinary theory, how to eliminate the "noise" in interactive data is taken as the research design and method, and the empirical research will help people to re-understand social learning, open and flexible online learning, making the emergence of new theories possible.