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Complexity, theory and praxis: Researching collaborative learning and tutoring processes in a networked learning community

机译:复杂性,理论和实践:在网络学习社区中研究协作式学习和辅导过程

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This paper explores the complexity ofresearching networked learning and tutoring ontwo levels. Firstly, on the theoretical level,we argue that the nature of praxisin networked environments (that is, learning and tutoring)is so complex that no single theoretical model,among those currently available, is asufficiently powerful, descriptively,rhetorically, inferentially or in itsapplication to real contexts, to provide aframework for a research agenda that takes intoaccount the key aspects of human agency. Furthermore, we argue that this complexity ofpraxis requires a multi-method approach toempirical investigation, in order that theoryand praxis may converse, with both beingenriched by these investigations. Secondly, onan empirical level, and as an example thatdraws upon our theoretical argument aboutcomplexity, we present the findings of amulti-method analysis of the learning andtutoring processes occurring in an on-linecommunity of professionals engaged in aMaster's Programme in E-Learning. Thisinvestigation is informed by two mainstreamtheoretical perspectives on learning, andemploys computer-assisted content analysis andcritical event recall as complementarymethodologies. This study reveals thedifferentiated nature of participants'learning, even within a highly structuredcollaborative learning environment, identifiessome of the key functions and roles ofparticipants, and provides an indication of thevalue of such multi-method studies. Futureprospects for this approach to research in thefield are considered.
机译:本文探讨了在两个层次上研究网络学习和辅导的复杂性。首先,在理论层面上,我们认为网络环境(即学习和辅导)中的实践的本质是如此复杂,以至于在当前可用的理论模型中,没有任何一个理论模型足够强大,在描述上,在修辞上,在推论上或在其应用方面都足够强大。结合实际情况,为研究议程提供框架,并考虑到人类代理的关键方面。此外,我们认为这种实践的复杂性需要一种多方法的实证研究方法,以便理论和实践可以相互转化,而这两者都被这些研究所丰富。其次,在经验水平上,并以借鉴我们关于复杂性的理论论证为例,我们介绍了对从事在线学习硕士课程的专业人士的在线社区中的学习和辅导过程进行的多方法分析结果。这项研究是基于对学习的两种主流理论观点进行的,并采用计算机辅助内容分析和关键事件召回作为补充方法。这项研究揭示了参与者学习的差异性,即使是在高度结构化的协作学习环境中,也可以识别参与者的一些关键功能和角色,并为此类多方法研究的价值提供了指示。考虑了该领域研究方法的未来前景。

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