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Crowdsourcing for assessment items to support adaptive learning

机译：用于评估项目的众群，以支持自适应学习

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Purpose: Adaptive learning requires frequent and valid assessments for learners to track progress against their goals. This study determined if multiple-choice questions (MCQs) crowdsourced from medical learners could meet the standards of many large-scale testing programs.Methods: Users of a medical education app (Osmosis.org, Baltimore, MD) volunteered to submit case-based MCQs. Eleven volunteers were selected to submit MCQs targeted to second year medical students. Two hundred MCQs were subjected to duplicate review by a panel of internal medicine faculty who rated each item for relevance, content accuracy, and quality of response option explanations. A sample of 121 items was pretested on clinical subject exams completed by a national sample of U.S. medical students.Results: Seventy-eight percent of the 200 MCQs met faculty reviewer standards based on relevance, accuracy, and quality of explanations. Of the 121 pretested MCQs, 50% met acceptable statistical criteria. The most common reasons for exclusion were that the item was too easy or had a low discrimination index.Conclusions: Crowdsourcing can efficiently yield high-quality assessment items that meet rigorous judgmental and statistical criteria. Similar models may be adopted by students and educators to augment item pools that support adaptive learning.
机译：目的：自适应学习需要频繁和有效的学习者评估，以跟踪他们的目标进度。本研究确定了来自医学学习者的多项选择题（MCQ）可以满足许多大规模测试计划的标准。方法：医学教育应用程序的用户（Osmosis.org，Baltimore，MD）自愿提交案例MCQ。选择11名志愿者提交针对二年医学生的MCQ。由一小组内科医学教师进行两百MCQ的审查，他们评估每个项目的相关性，内容准确性和响应选项的质量解释。在由美国医学院的国家样本完成的临床主题考试中预先测试121项样品。结果：200 MCQ的78％的百分之七十八个基于相关性，准确性和解释质量达到教职员工标准。在预测的MCQ中，50％符合可接受的统计标准。排除的最常见原因是该项目太容易或具有低歧视指数。结论：众包可以有效地产生符合严格的评判和统计标准的高质量评估项目。学生和教育工作者可以采用类似的模型来增加支持自适应学习的项目池。

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