【作 者】：
马志强， 岳芸竹
【关 键 词 】：
学习投入； 学习分析； 经验取样法； 交叉滞后分析； 纵向研究
【栏 目】：
课程与教学
【中文摘要】：
在在线与混合式学习投入研究中，如何采集学习过程中产生的即时投入数据，刻画不同群体的投入特征，探索认知、行为与情感投入之间的复杂关系是研究者面临的重要挑战。研究提出面向即时过程数据的学习投入动态分析框架，其包含即时性、持续性、多维性，并据此提出学习投入纵向研究设计思路，综合采用经验取样法、交叉滞后分析与聚类分析法对混合式学习投入进行分析。研究发现，基于认知、情感与行为子投入水平可将学习者聚类成四类群体：浅层投入、中等投入、深层投入与愉悦投入，学习者在认知、行为与情感维度的投入是不均衡的。认知、情感与行为子投入之间的预测关系很可能受到时间、学习环境等因素的影响。研究结果也进一步表明：面向即时数据采集与分析的纵向研究设计为精准刻画学习者投入特征提供有效路径，也为深入揭示学习投入子维度之间的预测关系及中介因素提供了可能。
【英文摘要】：
In the study of online and blended learning engagement, it is an important challenge for researchers to collect the data of real-time engagement generated in the process of learning, analyze the characteristics of engagement of different groups, and explores the complex relationship between cognition, emotion and affective engagement. This study proposes a dynamic analysis framework of learning input for real-time process data, including immediacy, sustainability and multi-dimension. Based on this, a design idea of longitudinal study on learning engagement is put forward. This study adopts empirical sampling method, cross-lag analysis and cluster analysis to analyze the blended learning engagement. It is found that learners can be grouped into four groups based on their cognitive, emotional and behavioral levels, namely shallow engagement, medium engagement, deep engagement and cheerful engagement, and learners' cognitive, behavioral and emotional engagement is not balanced. The predictive relationship between cognition, emotion and behavioral engagement is likely to be influenced by time, learning environment and other factors. The research results further indicate that the longitudinal study for real-time data acquisition and analysis provides an effective way to accurately describe the characteristics of learners' engagement, and also provides a possibility to reveal the predictive relationship between sub-dimensions of learning engagement and the mediating factors.