Corona Gaps: Vulnerable students’ mathematics competencies before and after the pandemic and practices of resilience-supporting teachers

在德国学校系统中，数学成绩的社会差距大于经合组织的平均水平。许多专家假设，在大流行期间部分学校关闭期间，这些差距进一步扩大，但迄今为止，经验支持有限。此外，人们对那些在大流行病期间表现出复原力的脆弱学生以及支持这种复原力的教学做法知之甚少。在Corona Gaps项目中，将通过比较流行病前后背景因素对数学成绩的影响（RQ 1），确定背景因素以及家庭教育条件是否增加或减少与流行病相关的脆弱性（RQ 2），确定具有许多弹性学生的最佳实践班级（RQ 3），并确定教师在这些最佳实践课程（RQ4）中使用的支持服从的做法。该项目重新分析了已经或将要从流行病前后的两个五年级学生样本（n = 2 x 500）和两个七年级学生样本（n = 2 x 1 200）中收集的测试数据，其中许多是城市地区学校的弱势学生。对于RQ 1和RQ 2，两个双样本进行了比较，在协调的多层次回归模型中，就预测能力的背景因素的社会经济地位，移民身份，性别，语言能力，和家庭教育条件的数学成绩。对于RQ3，一个样本的纵向设计将揭示对大流行期间学习收益或损失以及促进或阻碍他们的学习机会的更多见解。采用重复测量的协方差分析方法，在95名教师中确定最佳实践班级，以便在RQ4中，通过问卷和访谈询问最佳实践教师的依从性支持教学实践。由于主要的数据收集工作已经或将要在其他项目中进行，因此本补充项目只需要增加关于学生家庭教育条件和教师自我报告做法的调查问卷，然后进行密集的二级比较分析。提供独特的实证，不仅包括关于学生在不同数学能力和潜在脆弱性因素中与流行病相关的脆弱性的细粒度特定主题知识，而且还包括对复原力高度相关方面的见解：有助于复原力的背景因素和家庭教育条件将被确定沿着可以支持学生复原力的教学实践。这种对复原力的关注不仅可以描述与流行病有关的挑战，还可以为今后克服挑战的教学方法提供信息。

In the German school system, social gaps in mathematics achievement are larger than the OECD average. Many experts have hypothesized that these gaps have further increased during the partial school closures during the pandemic, but so far there has been limited empirical backing. Furthermore, little is known about those vulnerable students who have shown resilience and the teaching practices that support that resilience during the pandemic. In the Corona Gaps project, the hypothesis of increased social gaps will be tested and substantiated by comparing the impact of background factors on mathematics achievement before and after the pandemic (RQ1), identifying background factors and whether home-schooling conditions increase or reduce pandemic-related vulnerability (RQ2), identifying best-practice classes with many resilient students (RQ3), and determining resilience-supporting practices used by teachers in these best-practice classes (RQ4). The project re-analyzes test data that have been or will be gathered from two samples of fifth graders (n = 2 x 500) and two samples of seventh graders (n = 2 x 1 200) before and after the pandemic, many of them vulnerable students in schools in urban areas. For RQ1 and RQ2, the two double samples are compared in coordinated multilevel regression models, with respect to the predictive power of the background factors of socioeconomic status, immigrant status, gender, language proficiency, and the home-schooling conditions for the mathematics achievement. For RQ3, a longitudinal design in one sample will reveal additional insights into the learning gains or losses during the pandemic and the learning opportunities that fostered or hindered them. Covariance analysis with repeated measures will be used to identify the best-practice classes among 95 teachers, so that for RQ4, best-practice teachers can be asked for their resilience-supporting teaching practices in questionnaires and interviews. As the main data gathering was or will be administered within other projects, only additional questionnaire items on students’ home-schooling conditions and teachers’ self-reported practices will be necessary for this supplemental project, which will be followed by an intensive secondary comparative analysis.Expected outcomes of the supplemental project are empirical insights into the increased social gaps hypothesis, providing a unique substantiation that comprises not only fine-grained topic-specific knowledge about students’ pandemic-related vulnerability in different mathematical competencies and potential vulnerability factors, but also insights into highly relevant aspects of resilience: Background factors and home-schooling conditions contributing to resilience will be identified along with teaching practices that can support students’ resilience. Rather than only describing pandemic-related challenges, this focus on resilience can also inform instructional approaches for overcoming challenges in the future.