Abbakumov Dmitry, Desmet Piet, Van den Noortgate Wim
ITEC, IMEC research group at KU Leuven, Kortrijk, BE.
Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, BE.
Psychol Belg. 2020 May 22;60(1):115-131. doi: 10.5334/pb.515.
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.
大规模在线开放课程(MOOC)会生成学习者的表现数据,这些数据可用于了解学习者的水平并提高他们的学习效率。然而,目前所采用的方法,比如评估测试中正确答案的比例,过于简单化,可能会导致得出糟糕的结论和决策,因为这些方法没有考虑到关于学习者、内容和情境的其他信息。对于MOOC而言,需要有基于理论的数据驱动的可解释教育测量方法。在这篇概念性论文中,我们试图在心理测量学(一门关注教育和心理测量技术的科学学科)与MOOC之间建立联系。首先,我们描述教育中传统测量学习者水平的一般原则。其次,我们讨论MOOC的一些特性,这些特性阻碍了基于这些一般原则的方法的直接应用。第三,我们讨论测量水平方面的最新进展,这些进展可能与分析MOOC数据相关。最后,我们给出心理测量建模的方向,这些方向可能会引起未来MOOC研究的兴趣。