An Enhancement-Focused Framework For Developing High Quality Single Best Answer Multiple Choice Questions

EDUCATION FOR HEALTH(2015)

引用 5|浏览1
暂无评分
摘要
Background: The primary goal of any assessment of students is to provide valid and reliable evaluations of students' knowledge and skills as well as provision of accurate feedback to students about their performance. Contrary to best practice guidelines for development of multiple choice questions (MCQs), however, items used within medical schools are often flawed. This disappoints students and discourages examiners from using in-house MCQ databases. Vetting and reviewing items can improve the quality of MCQs. In this paper, we describe our approach to standardize the format used for MCQ assessment and provide recommendations for quality enhancement of high-stakes assessment. Methods: A collaborative enhancement-focused vetting and review approach to development of high quality single best answer MCQs has been described. Results: Implementation of a collaborative strategy to blueprint, vet, review and standard set MCQ items for high stakes examinations can effectively contribute to assessment quality assurance. Similarly, shared responsibility for post examination analyses of items may reveal the psychometric properties of items in need of improvement and contribute to closure of the assessment outcomes feedback loop. Discussion: Devolving responsibility for implementation of assessment processes as an integral part of educational practices and values can maximize reliability and standards of assessment processes. We contend that while logistics and time constraints are of concern to busy faculty members, judicious utilization of resources to develop well-written MCQ items arc well worth the effort to produce reliable and valid examinee scores. An enhancement-focused approach can be institutionally rewarding and lead to improved quality of high stakes assessments.
更多
查看译文
关键词
Multiple Choice Questions, quality assurance, enhancement-focused, vetting, standard setting, blueprinting
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要