Practitioner Perspectives on COVID-19’s Impact on Computer Science Education Among High Schools Serving Students from Lower and Higher Income Families
Practitioner Perspectives on COVID-19’s Impact on Computer Science Education Among High Schools Serving Students from Lower and Higher Income Families
复制标题
从业者对 COVID-19 对为低收入和高收入家庭学生提供服务的高中计算机科学教育影响的看法
DOI：
10.1145/3557047
复制
发表时间：
2022
影响因子：
2.4
通讯作者：
Thompson, Angelica
中科院分区：
文献类型：
--
作者：
McGill, Monica M.;Snow, Eric;Vaval, Luronne;DeLyser, Leigh Ann;Wortel-London, Stephanie;Thompson, Angelica
研究问题。计算机科学（CS）教育研究人员针对高中生进行研究，他们可能会看到他们的研究受到COVID-19的影响。解释受COVID-19影响的研究结果提出了独特的挑战，需要更深入地了解大流行如何影响了学习或无法学习计算的服务不足和代表性不足的学生。研究问题我们对这项研究的研究问题是：高中计算机科学教育生态系统以何种方式受到COVID-19的影响，特别是在根据大多数学生的相对社会经济地位比较学校时？方法论。我们使用探索性顺序混合方法研究来了解高中CS教育工作者在过去一年中使用CAPE理论分解框架来衡量学校提供CS的能力，学生对CS教育的兴趣，学生对CS的兴趣，以及学生参加CS的经历的影响类型。数据收集程序。我们开发了一种工具来从开放式问题中收集定性数据，然后从CS高中教育工作者（n= 21）收集数据，并在CAPE中对其进行编码。我们用这些代码创造了一个定量仪器。我们从更广泛的CS高中教育工作者（n= 185）那里收集数据，分析数据，并考虑这些发现如何影响过去一年进行的研究。结果：总体而言，从业者的观点表明，在疫情期间，两种类型学校的CS资金，政策和课程的能力都有所增长，而提供物质和人力资源的能力下降。虽然参加课外活动的机会减少了，但提供的社会服务课程数量仍有显著增加。参加CS课程的女生较少，出勤率下降。学生在CS课程中的学习和参与受到了显著影响，而其他非认知因素，如对CS的兴趣和技术相关性则有所增加。从业者的观点还表明，为低收入家庭学生服务的学校（1）获得CS/CTE途径信息的学生人数减少较多;（2）注册CS课程的女孩人数减少较多;（3）学生人数减少较多。（3）修读双学分计算机科学课程而获得大学学分的学生人数有较大跌幅;（4）学生出席率有较大跌幅;及（5）有意修读额外计算机科学课程的学生人数有较大跌幅。另一方面，为来自高收入家庭的学生提供服务的学校，有兴趣学习额外CS课程的学生数量增幅显着更高。
Research Problem.Computer science (CS)education researchers conducting studies that target high school students have likely seen their studies impacted by COVID-19. Interpreting research findings impacted by COVID-19 presents unique challenges that will require a deeper understanding as to how the pandemic has affected underserved and underrepresented students studying or unable to study computing.Research Question.Our research question for this study was:In what ways has the high school computer science educational ecosystem for students been impacted by COVID-19, particularly when comparing schools based on relative socioeconomic status of a majority of students?Methodology.We used an exploratory sequential mixed methods study to understand the types of impacts high school CS educators have seen in their practice over the past year using theCAPEtheoretical dissaggregation framework to measure schools’Capacityto offer CS, studentAccessto CS education, studentParticipationin CS, andExperiencesof students taking CS.Data Collection Procedure.We developed an instrument to collect qualitative data from open-ended questions, then collected data from CS high school educators (n= 21) and coded them across CAPE. We used the codes to create a quantitative instrument. We collected data from a wider set of CS high school educators (n= 185), analyzed the data, and considered how these findings shape research conducted over the last year.Findings.Overall, practitioner perspectives revealed that capacity for CS Funding, Policy & Curriculum in both types of schools grew during the pandemic, while the capacity to offer physical and human resources decreased. While access to extracurricular activities decreased, there was still a significant increase in the number of CS courses offered. Fewer girls took CS courses and attendance decreased. Student learning and engagement in CS courses were significantly impacted, while other noncognitive factors like interest in CS and relevance of technology saw increases.Practitioner perspectives also indicated that schools serving students from lower-income families had (1) a greater decrease in the number of students who received information about CS/CTE pathways; (2) a greater decrease in the number of girls enrolled in CS classes; (3) a greater decrease in the number of students receiving college credit for dual-credit CS courses; (4) a greater decrease in student attendance; and (5) a greater decrease in the number of students interested in taking additional CS courses. On the flip-side, schools serving students from higher income families had significantly higher increases in the number of students interested in taking additional CS courses.
登录
查看更多内容
重新审视 COVID-19 时代的数字鸿沟
影响因子：
5.8
作者：
John Lai;N. Widmar
通讯作者：
N. Widmar
DOI：
10.5860/choice.51-5366
发表时间：
1994-12
期刊：
--
影响因子：
--
作者：
Sally Dillow;Thomas D. Snyder;Thomas D. Snyder;Snyder;Sally Dillow;Thomas
通讯作者：
Sally Dillow;Thomas D. Snyder;Thomas D. Snyder;Snyder;Sally Dillow;Thomas
虚拟外展：编码俱乐部应对 COVID-19 的经验教训
DOI：
10.1145/3408877.3432559
发表时间：
2021
期刊：
Proceedings of the 52nd ACM Technical Symposium on Computer Science Education
影响因子：
--
作者：
Andrew McDonald;L. Dillon
通讯作者：
L. Dillon
DOI：
--
发表时间：
2008
期刊：
影响因子：
--
作者：
Rachel Estrella;Joanna Goode;J. J. Holme;Kim Nao
通讯作者：
Kim Nao
PSPP 用于数据分析的免费开源工具
DOI：
--
发表时间：
2014
期刊：
影响因子：
--
作者：
Jignasu Yagnik
通讯作者：
Jignasu Yagnik