Stathakarou Natalia, Zary Nabil, Kononowicz Andrzej A
Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden.
Stud Health Technol Inform. 2014;205:793-7.
Massive Open Online Courses (MOOCs) are raising extensive attention across disciplines, while it becomes evident that rethinking of learning designs that work well in these environments is needed. In the field of medical education, where the technology of MOOCs is not widely adopted yet, we wish to investigate the potential offered by virtual patients for the purpose of clinical reasoning skills training. In this paper we describe three use case scenarios employing virtual patients' features in MOOCs: (1) collective evaluation of decision making in the context of uncertainty; (2) collective repurposing of cases and division of discussion into subgroups focusing on local variances in healthcare; (3) division of content in short cases for flexible selection and adaptive learning with virtual patients. We also present technical requirements for implementing the use case scenarios and future work plans.
大规模在线开放课程(MOOCs)正在跨学科领域引发广泛关注,同时,重新思考在这些环境中有效的学习设计变得很有必要。在医学教育领域,MOOCs技术尚未得到广泛应用,我们希望研究虚拟患者在临床推理技能培训方面的潜力。在本文中,我们描述了在MOOCs中利用虚拟患者特征的三个用例场景:(1)在不确定性背景下对决策进行集体评估;(2)对病例进行集体重新利用,并将讨论分成关注医疗保健中局部差异的子组;(3)将内容划分为简短病例,以便灵活选择并与虚拟患者进行适应性学习。我们还提出了实施用例场景的技术要求和未来工作计划。