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Quantifying pandemic-driven educational assessment modifications: grade predictions, student and school characteristics, and university outcomes

量化流行病驱动的教育评估修改：成绩预测、学生和学校特征以及大学成果

基本信息

批准号：
ES/W012405/1
负责人：
Konstantina Maragkou
金额：
$ 11.04万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FW012405%2F1
关键词：
Quantifying pandemic driven educational assessment

项目摘要

In response to the Covid-19 pandemic, A-level and equivalent examinations were cancelled in 2020. Instead, students completing qualifications in 2020 were awarded grades based on teacher determinations, initially standardized by Ofqual via an algorithm, and eventually returning to so-called centre-assessed grades. Prior to the pandemic, teachers had predicted grades for students, including those applying to university. This is established practice, part of the normal procedure of applying to university. Evidence suggests though that teacher predictions can be inaccurate, often overestimating a student's true achievement, and can vary between subgroups (e.g. by gender, ethnic, socio-economic background; or by school characteristics, incl. rate of progression to university). The aim of this research is to quantify the change generated by the 2020 assessment procedure that replaced examination-based awarded grades with what amounts to another set of predictions by teachers, after initial attempts to apply a standardization approach. The research will evaluate how it may be associated with student and school characteristics; and assess implications on progression to university for students and schools.The research will use a new data resource, linking information about students, schools, assessment results, and university applications, for 2020 and three years before the pandemic. These linked data, held jointly by the Department for Education, qualifications regulator Ofqual, and university application service UCAS, offer a unique opportunity to understand how any assessment modification affected students and schools in different circumstances.Despite the importance of predicted grades for university admissions, the evidence on their determinants and impact is limited. This research will address this gap in three ways.First, using the new data resource, the research will explore for the first time the relationship between UCAS predicted grades and awarded grades at the individual level. This will measure the prediction accuracy and explore how it varies by individual, subject, and school characteristics.Second, using innovative analysis, the research will contrast the prediction accuracy in the 2020 and the pre-2020 cohorts, quantifying the modification introduced by the 2020 assessment method, by subject and school. As long as no substantial variation in the prediction accuracy is observed across the pre-2020 cohorts, any change in this relationship in the 2020 cohort, over and above regular variation between years, can be attributed to the 2020 assessment procedure. The research will generate evidence around factors associated with the modification, including school type, size, composition, location, and other features. The modification will be also quantified using the 2020 Ofqual-provided calculated grades. This will create a further counterfactual for what could have happened were grades not awarded by the 2020 procedure, also accounting for student and school features.Third, the research will explore how the above-quantified modification has affected the university application outcomes of students and schools in the 2020 cohort compared to pre-2020, linking this work to existing policy efforts to widen access to university. The research will offer an insight into the impact of the 2020 assessment procedure on the types of educational outcomes that are associated with later employment and social mobility opportunities.Overall, the research will provide a comprehensive investigation of the relationship between predicted and awarded grades and the impact of the Covid-19 driven changes on this relationship, broadening the understanding of the determinants of inequalities in university access during the pandemic, helping inform policies to mitigate the negative impact of the pandemic on students' life chances, to inform policy that may improve outcomes contributing to social mobility.

为应对2019冠状病毒病大流行，2020年取消了A-level和同等考试。相反，在2020年完成资格认证的学生将根据教师的决定获得分数，最初由Ofqual通过算法进行标准化，最终返回到所谓的中心评估分数。在大流行之前，老师们预测学生的成绩，包括那些申请大学的学生。这是惯例，是申请大学正常程序的一部分。有证据表明，尽管教师的预测可能是不准确的，往往高估了学生的真实成就，并且在子群体之间可能会有所不同（例如，根据性别、种族、社会经济背景；或根据学校特点，包括升入大学的速度）。本研究的目的是量化2020年评估程序所产生的变化，该程序在最初尝试应用标准化方法后，用相当于另一套教师预测的方式取代了基于考试的奖励分数。这项研究将评估它与学生和学校特征之间的关系；并评估对学生和学校升入大学的影响。这项研究将使用一种新的数据资源，将2020年和大流行前三年的学生、学校、评估结果和大学申请信息联系起来。这些关联数据由英国教育部、资格监管机构英国教育部和大学申请服务中心联合持有，提供了一个独特的机会，可以了解任何评估修改在不同情况下对学生和学校的影响。尽管预测成绩对大学录取很重要，但关于其决定因素和影响的证据有限。这项研究将从三个方面解决这一差距。首先，利用新的数据资源，本研究将首次在个人层面上探索大学入学申请办公室预测成绩与授予成绩之间的关系。这将衡量预测的准确性，并探讨它如何因个人、学科和学校的特点而变化。其次，采用创新的分析方法，对比2020年和2020年前的预测准确性，量化2020年评估方法引入的修正，按学科和学校进行量化。只要在2020年之前的队列中没有观察到预测准确性的实质性变化，那么2020年队列中这种关系的任何变化，超过年份之间的常规变化，都可以归因于2020年的评估程序。这项研究将围绕与修改相关的因素，包括学校类型、规模、组成、位置和其他特征，得出证据。修改也将使用2020年ofquality提供的计算分数进行量化。这将进一步创造一个反事实，因为如果在2020年的程序中没有授予分数，可能会发生什么，也考虑到学生和学校的特点。第三，研究将探讨与2020年之前相比，上述量化修改如何影响2020年队列学生和学校的大学申请结果，并将这项工作与现有的扩大大学入学机会的政策努力联系起来。该研究将深入了解2020年评估程序对与以后就业和社会流动机会相关的教育成果类型的影响。总体而言，该研究将全面调查预测成绩和授予成绩之间的关系以及Covid-19驱动的变化对这种关系的影响，扩大对大流行期间大学入学不平等的决定因素的理解，帮助为减轻大流行对学生生活机会的负面影响的政策提供信息，为可能改善有助于社会流动的结果的政策提供信息。