Exploring Early Childhood Teachers’ Abilities to Identify Computational Thinking Precursors to Strengthen Computer Science in Classrooms

探索幼儿教师识别计算思维先驱的能力，以加强课堂上的计算机科学

• 批准号: 2006595
• 负责人: Sean Justice
• 金额: $ 44.91万
• 依托单位: Texas State University - San Marcos
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006595&HistoricalAwards=false
• 关键词: Exploring; Early; Childhood; Teachers; Abilities

Strengthening computer science education is a national priority with special attention to increasing the number of teachers who can deliver computer science education in schools. Yet computer science education lacks the evidence to determine how teachers come to think about computational thinking (a problem-solving process) and how it could be integrated within their day-to-day classroom activities. For teachers of pre-kindergarten to 2nd (PK-2) grades, very little research has specifically addressed teacher learning. This oversight challenges the achievement of an equitable, culturally diverse, computationally empowered society. The project team will design a replicable model of PK-2 teacher professional development in San Marcos, Texas, to address the lack of research in early computer science education. The model will emphasize three aspects of teacher learning: a) exploration of and reflection on computer science and computational thinking skills and practices, b) noticing and naming computer science precursor skills and practices in early childhood learning, and c) collaborative design, implementation and assessment of learning activities aligned with standards across content areas. The project will explore PK-2 teachers’ content knowledge by investigating their understanding of the design and implementation of culturally relevant computer science learning activities for young children. The project includes a two-week computational making and inquiry institute focused on algorithms and data in the context of citizen science and historical storytelling. The project also includes monthly classroom coaching sessions, and teacher meetups.The research will include two cohorts of 15 PK-2 teachers recruited from the San Marcos Consolidated Independent School District (SMCISD) in years one and two of the project. The project incorporates a 3-phase professional development program to be run in two cycles for each cohort of teachers. Phase one (summer) includes a 2-week Computational Making and Inquiry Institute, phase two (school year) includes classroom observations and teacher meetups and phase three (late spring) includes an advanced computational thinking institute and a community education conference. Research and data collection on impacts will follow a mixed-methods approach based on a grounded theory design to document teachers learning. The mixed-methods approach will enable researchers to triangulate participants' acquisition of new knowledge and skills with their developing abilities to implement learning activities in practice. Data analysis will be ongoing, interweaving qualitative and quantitative methods. Qualitative data, including field notes, observations, interviews, and artifact assessments, will be analyzed by identifying analytical categories and their relationships. Quantitative data includes pre to post surveys administered at three-time points for each cohort. Inter-item correlations and scale reliabilities will be examined, and a repeated measures ANOVA will be used to assess mean change across time for each of five measures. Project results will be communicated via peer-reviewed journals, education newsletters, annual conferences, family and teacher meetups, and community art and culture events, as well as on social media, blogs, and education databases. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

加强计算机科学教育是国家的优先事项，特别注意增加能够在学校提供计算机科学教育的教师人数。 然而，计算机科学教育缺乏证据来确定教师如何看待计算思维（解决问题的过程）以及如何将其融入日常课堂活动。 对于学前班到二年级（PK-2）的教师，很少有研究专门针对教师学习。这种监督对实现一个公平的、文化多样的、计算赋权的社会提出了挑战。该项目团队将在德克萨斯州圣马科斯设计一个可复制的PK-2教师专业发展模型，以解决早期计算机科学教育研究不足的问题。 该模式将强调教师学习的三个方面：a）对计算机科学和计算思维技能和实践的探索和反思，B）注意并命名幼儿学习中的计算机科学先驱技能和实践，以及c）协同设计，实施和评估与内容领域标准一致的学习活动。 该项目将通过调查PK-2教师对设计和实施与文化相关的幼儿计算机科学学习活动的理解来探索他们的内容知识。该项目包括一个为期两周的计算制作和调查研究所，专注于公民科学和历史故事背景下的算法和数据。该项目还包括每月一次的课堂辅导会议和教师聚会。该研究将包括两组15名PK-2教师，他们是在项目的第一年和第二年从圣马科斯综合独立学区（SMCISD）招募的。 该项目包括一个三阶段的专业发展方案，将在两个周期内为每个教师群体运行。第一阶段（夏季）包括为期两周的计算制作和探究研究所，第二阶段（学年）包括课堂观察和教师聚会，第三阶段（春末）包括高级计算思维研究所和社区教育会议。关于影响的研究和数据收集将遵循基于扎根理论设计的混合方法，以记录教师的学习。混合方法的方法将使研究人员能够三角参与者的新知识和技能的获得与他们的发展能力，在实践中实施学习活动。数据分析将持续进行，将定性和定量方法结合起来。定性数据，包括现场记录，观察，访谈和工件评估，将通过识别分析类别及其关系进行分析。定量数据包括每个队列在三个时间点进行的前后调查。将检查项目间相关性和量表可靠性，并使用重复测量ANOVA评估五项测量中每项测量随时间的平均变化。项目成果将通过同行评审期刊、教育通讯、年度会议、家庭和教师聚会、社区艺术和文化活动以及社交媒体、博客和教育数据库进行交流。探索研究preK-12计划（DRK-12）旨在通过研究和开发创新资源，模型和工具，显着提高preK-12学生和教师的科学，技术，工程和数学（STEM）的学习和教学。DRK-12项目中的项目建立在STEM教育的基础研究以及为拟议项目提供理论和经验依据的先前研究和开发工作的基础上。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。