{"title":"Dynamic answer-dependent multiple-choice questions and holistic assessment analysis in high-enrollment courses.","authors":"Harnejan K Atwal, Kenjiro W Quides","doi":"10.1128/jmbe.00047-24","DOIUrl":null,"url":null,"abstract":"<p><p>Many 4-year public institutions face significant pedagogical challenges due to the high ratio of students to teaching team members. To address the issue, we developed a workflow using the programming language R as a method to rapidly grade multiple-choice questions, adjust for errors, and grade answer-dependent style multiple-choice questions, thus shifting the teaching teams' time commitment back to student interaction. We provide an example of answer-dependent style multiple-choice questions and demonstrate how the output allows for discrete analysis of questions based on various categories such as Fundamental Statements or Bloom's Taxonomy Levels. Additionally, we show how student demographics can be easily integrated to yield a holistic perspective on student performance in a course. The workflow offers dynamic grading opportunities for multiple-choice questions and versatility through its adaptability to assessment analyses. This approach to multiple-choice questions allows instructors to pinpoint factors affecting student performance and respond to changes to foster a healthy learning environment.</p>","PeriodicalId":46416,"journal":{"name":"Journal of Microbiology & Biology Education","volume":" ","pages":"e0004724"},"PeriodicalIF":1.6000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11360413/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Microbiology & Biology Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1128/jmbe.00047-24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/13 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
引用次数: 0
Abstract
Many 4-year public institutions face significant pedagogical challenges due to the high ratio of students to teaching team members. To address the issue, we developed a workflow using the programming language R as a method to rapidly grade multiple-choice questions, adjust for errors, and grade answer-dependent style multiple-choice questions, thus shifting the teaching teams' time commitment back to student interaction. We provide an example of answer-dependent style multiple-choice questions and demonstrate how the output allows for discrete analysis of questions based on various categories such as Fundamental Statements or Bloom's Taxonomy Levels. Additionally, we show how student demographics can be easily integrated to yield a holistic perspective on student performance in a course. The workflow offers dynamic grading opportunities for multiple-choice questions and versatility through its adaptability to assessment analyses. This approach to multiple-choice questions allows instructors to pinpoint factors affecting student performance and respond to changes to foster a healthy learning environment.
由于学生与教学团队成员的比例较高,许多四年制公立院校面临着巨大的教学挑战。为了解决这个问题,我们开发了一种使用编程语言 R 的工作流程,作为快速批改选择题、调整错误和批改答案依赖型选择题的方法,从而将教学团队的时间投入转回学生互动上。我们提供了一个答案依赖型选择题的示例,并演示了输出如何根据基本陈述或布卢姆分类学等级等不同类别对问题进行离散分析。此外,我们还展示了如何轻松整合学生人口统计数据,以全面了解学生在课程中的表现。该工作流程为多项选择题提供了动态评分机会,并通过其对评估分析的适应性提供了多功能性。这种处理选择题的方法使教师能够准确定位影响学生成绩的因素,并对变化做出反应,从而营造健康的学习环境。