SMILI (sic): a Framework for Interfaces to Learning Data in Open Learner Models, Learning Analytics and Related Fields
SMILI (sic): a Framework for Interfaces to Learning Data in Open Learner Models, Learning Analytics and Related Fields
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DOI：
10.1007/s40593-015-0090-8
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发表时间：
2016-03-01
影响因子：
4.9
通讯作者：
Kay, Judy
中科院分区：
文献类型：
--
作者：
Bull, Susan;Kay, Judy
SMILI（Student Models that Invite the Learner In）开放式学习者模型框架旨在为开放式学习者模型（OLM）的多种多样形式提供一幅连贯的图景。SMILI（原文如此）的目的是为研究人员提供一种系统的方法来描述，比较和批评OLM。我们希望它能突出那些有大量OLM工作的领域，以及那些被忽视的领域。然而，我们观察到SMILI（原文如此）并没有以这些方式使用。我们现在反思其原因，并得出结论，它实际上在定义OLM的概念和通知OLM设计方面发挥了更广泛的作用。自SMILI（原文如此）论文发表以来，技术强化学习发生了很大变化。值得注意的是，学习技术在正式学习和终身学习中已变得更加普遍。这提供了巨大的，并且仍在增长的学习数据量。学习分析（LA），规模学习（L@S），教育数据挖掘（EDM）和量化自我（QS）领域已经出现。本文认为，即使在人工智能教育和智能辅导系统研究中，学习者模型的性质和作用也发生了重要转变。鉴于这些趋势，并反映了SMILI（原文如此）的使用，本文提出了SMILI（原文如此）的修订和简化版本。在这两种情况下，都有额外的类别来涵盖新的趋势，可以根据需要应用，省略或替换。我们现在提供这作为OLM，学习分析和相关领域的界面设计师的指南，我们强调了需要更多研究的领域。
The SMILI (sic) (Student Models that Invite the Learner In) Open Learner Model Framework was created to provide a coherent picture of the many and diverse forms of Open Learner Models (OLMs). The aim was for SMILI (sic) to provide researchers with a systematic way to describe, compare and critique OLMs. We expected it to highlight those areas where there had been considerable OLM work, as well as those that had been neglected. However, we observed that SMILI (sic) was not used in these ways. We now reflect on the reasons for this, and conclude that it has actually served a broader role in defining the notion of OLM and informing OLM design. Since the initial SMILI (sic) paper, much has changed in technology-enhanced learning. Notably, learning technology has become far more pervasive, both in formal and lifelong learning. This provides huge, and still growing amounts of learning data. The fields of Learning Analytics (LA), Learning at Scale (L@S), Educational Data Mining (EDM) and Quantified Self (QS) have emerged. This paper argues that there has also been an important shift in the nature and role of learner models even within Artificial Intelligence in Education and Intelligent Tutoring Systems research. In light of these trends, and reflecting on the use of SMILI (sic), this paper presents a revised and simpler version of SMILI (sic) alongside the original version. In both cases there are additional categories to encompass new trends, which can be applied, omitted or substituted as required. We now offer this as a guide for designers of interfaces for OLMs, learning analytics and related fields, and we highlight the areas where there is need for more research.