Special feature: advanced technologies in educational assessment
Special feature: advanced technologies in educational assessment
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特色：教育评估先进技术
DOI：
10.1007/s41237-018-0071-y
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Wiberg
中科院分区：
文献类型：
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作者：
Ronny Scherer;M. Wiberg
我们认为，本期特刊所选的论文说明了先进技术在评估教育相关变量和结构方面的巨大潜力。具体来说，在学习者完成某些任务时，不仅有机会捕捉到准确性指标，而且有机会捕捉到任务行为和知识掌握的指标，这使得研究人员能够在技术的帮助下更详细地描绘任务表现。与此同时，新类型数据（即过程数据）的可用性与作者在这一特殊问题中引起注意的几个挑战相关：首先，过程数据需要透明和可复制的框架来概念化、表示和分析它们。其次，除了项目反应的准确性之外，描述任务表现的心理测量模型必须进一步发展，以便过程数据（例如，反应时间、动作序列、动作频率）可以告知和改进对学习者熟练程度的描述（另见von Davier 2017）。第三，先进的技术，因为它们允许交互式任务设计（例如，模拟，智能辅导，适应性测试），可以改善传统结构（例如，识字和算术）的评估，并使评估需要复杂任务设计的新结构（例如，协作技能，适应性问题解决）成为可能。然而，与过程数据的可用性一起，我们认为这种潜力需要仔细制作有效性论证，以促进对基于先进技术的评估所产生的分数和指标的解释（另见Katz等人2017；Mislevy 2016）。总的来说，我们鼓励该领域的研究人员在现代心理测量学和先进技术的帮助下，不要回避在教育中建模和评估复杂结构的新方法。
3 ConclusionWe believe that the selected papers for this special issue illustrate the enormous potential of advanced technologies for the assessment of educationally relevant variables and constructs. Specifically, the opportunities to capture not only indicators of accuracy while learners work on certain tasks but also indicators of task behavior and knowledge mastery allow researchers to draw a more detailed picture of task performance with the help of technology. At the same time, the availability of new types of data (ie, process data) is associated with several challenges that the authors bring to attention in this special issue: First, process data require transparent and replicable frameworks for conceptualizing, representing, and analyzing them. Second, the psychometric models to describe task performance beyond the accuracy of item responses must be developed further so that process data (eg, response times, sequences of actions, frequency of actions) can inform and improve the description of learners’ proficiency (see also von Davier 2017). Third, advanced technologies, as they allow for interactive task designs (eg, simulations, intelligent tutoring, adaptive testing), may improve the assessment of traditional constructs (eg, literacy and numeracy) and enable the assessment novel constructs that require complex task designs (eg, collaborative skills, adaptive problem solving). Together with the availability of process data, however, we believe that this potential requires the careful crafting of a validity argument to facilitate the interpretation of the scores and indicators resulting from advanced technology-based assessments (see also Katz et al. 2017; Mislevy 2016). Overall, we encourage researchers in the field to not shy away from novel approaches to modeling and assessing complex constructs in education with the help of modern psychometrics and advanced technologies.