异步在线学习中的“准”同步视频交互实验研究  

An Experimental Study of Reconstructing Asynchronized Online Learning into“Quasi”Synchronized Video Learning

作　　者：张婧婧[1] 牛晓杰 姚自明 郑勤华[1] Zhang Jingjing;Niu Xiaojie;Yao Ziming;Zheng Qinhua(Center for Distance Education,Faculty of Education,Beijing Normal University,Beijing 100875)

机构地区：[1]北京师范大学教育学部远程教育研究中心,北京100875

出　　处：《远程教育杂志》2021年第3期52-64,共13页Journal of Distance Education

基　　金：2019年度国家自然科学基金委员会管理学部重点课题“‘互联网+’时代的教育改革与创新管理研究”(项目编号:71834002)的资助。

摘　　要：在在线学习中,视频作为传递知识的媒介发挥了重要的作用,其异步交互是保障在线学习大规模性、灵活性与可持续发展的前提。然而,异步交互降低了学习者的在线临场感,难以在学习者之间达成认知共识。要解决这样的异步交互给认知交互带来的挑战,可利用视频播放时间作为学习者异步学习中可遵循的同一时间,将原本异步的交互重构为基于播放时间的“准”同步交互。基于此,通过设计超视频学习平台,实现异步视频学习中的“准”同步交互,开展基于组内设计的准实验研究,发现超视频能够显著增强学习者的社会临场感,并能够显著降低学习者的内在认知负荷;进而通过滞后行为序列和认知网络分析,发现超视频对学习者行为变化与认知发展具有影响。具体来说,按照人格特质,使用HAC与K-means聚类,将学习者划分为优质均衡型、冲动活跃型、低迷懒散型、开放内向型和焦虑社交型学习者,其中优质均衡型和焦虑社交型为具有高度参与讨论特征的学习者。然而尽管这两类学习者具有相似的学习行为,但焦虑社交型学习者并没有取得较好的学习绩效,可见学习者的学习绩效受到人格特质等的影响。因此,在进行超视频学习设计和应用时,应将人格特质作为学习者特征,进一步探究其学习机制,以优化大规模教学中的个性化学习支持服务。Video plays an important role as a medium for transferring knowledge.Using video to facilitate asynchronous interaction is a prerequisite for allowing the large-scale,flexible and sustainable development of online learning.However,asynchronous interactions reduce the online presence of learners and make it difficult to reach cognitive agreement while interacting.To address these challenges,the timestamp associated with videos can be used as a new reference time for the online space,bringing learners into a same time zone in asynchronous online learning.The study designed a hypervideo learning platform to implement pseudo-synchronous interaction in asynchronous video learning and conducted a quasi-experimental study using a within-group design.The study found that hypervideo significantly enhance learners’online presence and reduce learners’internal cognitive load.Further,this study used Lag Sequence Analysis(LSA)and Epistemic Network Analysis(ENA)to identify the effects of hypevideo on learners’behavioural change and cognitive development.Learners were classified by personality traits using Hierarchical Agglomerative Clustering(HAC)and K-means as“high quality balanced”,“impulsive active”,“depressed lazy”,“open introverted”and“anxious social”learners.Learners with a high level of engagement in interactions are“high quality balanced”and“anxious social”,however,although these two types of learners have similar learning behaviour sequences,“anxious social”learners didn’t gain high performance,indicating that learning performance is influenced by personality traits,etc.The classification of learners based on personality traits,as well as further exploration of their learning behaviour sequences could further assisted adaptive learner support in online learning at scale.

关 键 词：MOOCs 超视频 社会临场感 认知负荷 大五人格 

分 类 号：G420[文化科学—课程与教学论]