Buildup of speaking skills in an online learning community: a network-analytic exploration
Buildup of speaking skills in an online learning community: a network-analytic exploration
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在线学习社区中口语技能的培养：网络分析探索
DOI：
10.1057/s41599-018-0116-6
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发表时间：
2018
影响因子：
4
通讯作者：
Hoque, Mohammed Ehsan
中科院分区：
文献类型：
--
作者：
Shafipour, Rasoul;Baten, Raiyan Abdul;Hasan, Md Kamrul;Ghoshal, Gourab;Mateos, Gonzalo;Hoque, Mohammed Ehsan
在学习社区的研究一直发现的证据表明，同伴互动有助于学生的表现成果。在现代环境中，一个特别重要的能力是有效地沟通思想的能力。其中一个衡量标准是说话，这是专业和休闲环境中的一项重要技能。在这项研究中，我们探讨了在线网络中的同伴互动对口语技能发展的影响。特别是，我们提出了一个证据，逐步积累的技能，在一个小团体的设置，还没有在文献中报道。评估这些技能的发展需要研究客观证据，为此，我们引入了一个由158名参与者组成的六个在线社区的新数据集，专注于提高他们的口语技能。他们在10天内录制5个提示的视频演讲，并与同行交换评论和表现评级。我们问（i）参与者的评级是否受到他们与同龄人的互动模式的影响，以及（ii）是否有任何逐步建设的口语技能在社区走向同质化。为了分析数据，我们采用了图形信号处理（GSP）新兴领域的工具。GSP与社会网络分析的区别在于后者主要关注图的连接结构，而前者研究图上的信号。我们研究的性能评级的参与者作为图形信号的底层交互拓扑。图形信号的总变差分析显示，参与者的评分差异随时间推移而减少（斜率=-0.04，p< 0.01），而平均评分增加（斜率= 0.07，p< 0.05）-从而逐渐建立起社区范围内的同质性评分。我们通过预测公式提供了同伴影响的证据。我们基于共识的预测模型在预测性能评级方面比基线网络不可知回归模型高出约23%。这反过来又表明，参与者的评级受到同行的评级和相关的互动模式的影响，证实了以前的研究结果。然后，我们制定了一个基于共识的扩散模型，从我们的分析中捕获这些同行影响的观察结果。我们预计，这项研究将开辟未来的途径，更广泛地探索同行影响的技能发展机制，并可能有助于设计创新的干预措施，在小团体，以最大限度地提高同行的影响。
Studies in learning communities have consistently found evidence that peer-interactions contribute to students’ performance outcomes. A particularly important competence in the modern context is the ability to communicate ideas effectively. One metric of this is speaking, which is an important skill in professional and casual settings. In this study, we explore peer-interaction effects in online networks on speaking skill development. In particular, we present an evidence for gradual buildup of skills in a small-group setting that has not been reported in the literature. Evaluating the development of such skills requires studying objective evidence, for which purpose, we introduce a novel dataset of six online communities consisting of 158 participants focusing on improving their speaking skills. They video-record speeches for 5 prompts in 10 days and exchange comments and performance-ratings with their peers. We ask (i) whether the participants’ ratings are affected by their interaction patterns with peers, and (ii) whether there is any gradual buildup of speaking skills in the communities towards homogeneity. To analyze the data, we employ tools from the emerging field of Graph Signal Processing (GSP). GSP enjoys a distinction from Social Network Analysis in that the latter is concerned primarily with the connection structures of graphs, while the former studies signals on top of graphs. We study the performance ratings of the participants as graph signals atop underlying interaction topologies. Total variation analysis of the graph signals show that the participants’ rating differences decrease with time (slope=− 0.04, p< 0.01), while average ratings increase (slope= 0.07, p< 0.05)—thereby gradually building up the ratings towards community-wide homogeneity. We provide evidence for peer-influence through a prediction formulation. Our consensus-based prediction model outperforms baseline network-agnostic regression models by about 23% in predicting performance ratings. This in turn shows that participants’ ratings are affected by their peers’ ratings and the associated interaction patterns, corroborating previous findings. Then, we formulate a consensus-based diffusion model that captures these observations of peer-influence from our analyses. We anticipate that this study will open up future avenues for a broader exploration of peer-influenced skill development mechanisms, and potentially help design innovative interventions in small-groups to maximize peer-effects.
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DOI：
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DOI：
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期刊：
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