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    基于多主体交互的混合学习环境 感知与投入分析

    Analysis on Environmental Perception and Engagement in Blended Learning Based on Multi-agent Interaction

    [浏览次数:30931]

【作      者】:

卢国庆, 刘清堂, 张臣文, 贺相春


【关 键 词 】:

混合学习; 多主体交互; 经验取样法; 学习环境感知; 学习投入; 情境投入


【栏      目】:

学习环境与资源


【中文摘要】:

混合学习逐渐成为技术与教学融合的新常态,其学习体验呈现独特的跨情境变化规律。然而,传统自我报告方法很难捕获这种波动。据此,研究依托多主体交互的混合学习场景,采用经验取样法、传统自我报告法,以H高校两个班144名职前教师为研究样本,对混合学习中个体的学习环境感知、投入的状态(情境投入)与特质(整体投入)进行纵向数据采集,并采用描述性统计、时序分析、单因素重复测量方差分析等统计方法,探索学习环境感知及投入的波动规律。研究发现:(1)关于自由度及负担的感知,与后测(整体感知)相比,课前线上学习自由度偏低、课业负担偏重;(2)关于同伴交互的感知,与课前线上感知相比,课中线下组内交互感知偏低、组间交互偏高;(3)关于行为及认知投入,与课前线上投入相比,课中线下行为投入偏低、认知投入偏高。最后,研究在理论和实践层面,提出了优化个体混合学习体验、促进个体的深层次投入的若干建议。


【英文摘要】:

Blended learning is emerging as the new normal in the integration of technology and instruction, and its learning experience exhibits a unique pattern of cross-situational change. However, traditional self-reporting methods are difficult to capture such fluctuations. Accordingly, this study adopts the experience sampling method and the traditional self-reporting method to collect longitudinal data on individual's environmental perception, the state (situational engagement) and the trait (overall engagement) of the engagement in the blended learning environment of multi-agent interaction. 144 pre-service teachers of two classes in H university are taken as research samples. And descriptive statistics, time series analysis, and one-way repeated measures ANOVA are used to explore the fluctuation pattern of learning environmental perception and engagement. The results show that (1) regarding the perception of freedom and burden, compared with the post-test (overall perception), the freedom of online learning before class is lower and the study load is heavier; (2) Regarding the perception of peer interactions, compared to the online perception before class, the perception of intra-group interaction is lower while the perception of inter-group interaction is higher during class; (3) Regarding behavioral and cognitive engagement, compared to the online engagement before class, offline behavioral engagement during class is lower and cognitive input is higher. Finally, some suggestions are proposed to optimize individual blended learning experience and promote individual's deep engagement in theory and practice

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