Massive open online courses (MOOCs) generate learners’ performance data that can be used to understand learners’ proficiency and to improve their efficiency. However, the approaches currently used, such as assessing the proportion of correct responses in assessments, are oversimplified and may lead to poor conclusions and decisions because they do not account for additional information on learner, content, and context. There is a need for theoretically grounded data-driven explainable educational measurement approaches for MOOCs. In this conceptual paper, we try to establish a connection between psychometrics, a scientific discipline concerned with techniques for educational and psychological measurement, and MOOCs. First, we describe general principles of traditional measurement of learners’ proficiency in education. Second, we discuss qualities of MOOCs which hamper direct application of approaches based on these general principles. Third, we discuss recent developments in measuring proficiency that may be relevant for analyzing MOOC data. Finally, we draw directions in psychometric modeling that might be interesting for future MOOC research.

中文翻译：

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MOOC 的心理测量学：衡量学习者的熟练程度

大规模开放在线课程（MOOC）生成学习者的表现数据，可用于了解学习者的熟练程度并提高其效率。然而，目前使用的方法，例如评估评估中正确反应的比例，过于简单化，可能会导致糟糕的结论和决策，因为它们没有考虑有关学习者、内容和背景的额外信息。MOOC 需要有理论依据、数据驱动、可解释的教育测量方法。在这篇概念性论文中，我们试图在心理测量学（一门涉及教育和心理测量技术的科学学科）与 MOOC 之间建立联系。首先，我们描述传统衡量学习者教育熟练程度的一般原则。其次，我们讨论了 MOOC 的质量，这些质量阻碍了基于这些一般原则的方法的直接应用。第三，我们讨论了可能与分析 MOOC 数据相关的熟练程度测量的最新进展。最后，我们提出了未来 MOOC 研究可能感兴趣的心理测量模型方向。