Promoting learning and interest in mathematics for urban Black and Latinx children through culturally relevant daily robot coding activities

该开发和测试创新项目将推进“面向学生和教师的创新技术体验”（ITEST）项目的工作，以更好地理解和促进提高学生对科学、技术、工程和数学（STEM）以及信息和通信技术（ICT）职业的知识和兴趣的实践。具体来说，该项目将提高纽约市黑人和拉丁裔学生在一至三年级的数学兴趣和成绩。研究表明，由于贫困或教育机会有限，有色人种学生在三年级之前就已经经历了数学成绩的停滞和下降，这使得他们很难想象在STEM领域的职业生涯。该项目以机器人技术为平台，通过开发创新的适合年龄的数学课程，重点关注这一关键问题。与文化相关的机器人编码活动将与教师共同设计，并在他们的课堂上实施。该项目还将吸引在职业生涯中使用机器人技术的少数STEM专业人士，为学生树立榜样。该项目由哥伦比亚大学师范学院、社区STEM组织Vision Education & Media和两所纽约市一级学校合作完成。研究结果将告诉研究人员，与文化相关的机器人编码活动如何与学生的数学学习、参与和数学兴趣联系起来。对于实践者来说，研究结果将有助于更好地理解如何在基于标准的数学课程中设计、开发和实施与文化相关的简短编码活动。采用基于设计的研究方法，该项目将产生关于城市黑人和拉丁裔小学生是否以及如何通过参与与文化相关的简短机器人编码活动来提高数学成绩的新知识。该项目还将研究300名小学生在数学方面的成就、动机和兴趣背后的潜在因果特征和机制，并向12名教师介绍应用机器人编码活动进行数学学习时可能的学习轨迹。该项目还将探索一种新的专业发展方法，让教师参与到与学生的经历和文化直接相关的机器人编程活动中来。新补充课程的开发、测试和实施在这个项目中起着核心作用，并涉及一个跨学科的研究团队。多种形式的数据（如教师工件、观察、访谈和学生调查、成绩数据、焦点小组讨论）将用于编制与文化相关的数学教学法和课程设计（来自教师）以及数学兴趣和学习（来自学生）相关的参与和实践的多维画像。定量数据分析将包括描述性和推断性统计，包括回归分析、ANCOVA和重复测量方差分析。定性数据将使用恒定的比较分析和时序分析进行分析。利用从情境分析和问题探索中获得的信息，本研究将发展考虑社会文化视角和教育研究经验知识的领域特定理论。该奖项反映了美国国家科学基金会的法定使命，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。

This Developing and Testing Innovations project will advance efforts of the Innovative Technology Experiences for Students and Teachers (ITEST) program program to better understand and promote practices that increase students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. Specifically, the project will increase the interest and improve achievement in mathematics of New York City Black and Latinx students in 1st - 3rd grades. Research shows that students of color experience stagnation and downward achievement in mathematics already by the 3rd grade due primarily to poverty or limited educational opportunities, making it difficult for them to imagine a career in the STEM field. This project focuses on this critical issue by developing innovative age-appropriate mathematics curriculum using robotics technology as a platform. Culturally relevant robot coding activities will be co-designed with teachers and implemented in their classrooms. The project will also engage minority STEM professionals who use robotics in their careers to serve as role models for students. The project is a partnership between Teachers College of Columbia University, Vision Education & Media, a community-based STEM organization, and two NYC Title I schools. Findings will inform researchers on how culturally relevant robot coding activities may link to student’s mathematics learning, engagement, and interest in mathematics. For practitioners, the findings will contribute to better understanding of how to design, develop, and implement short culturally relevant coding activities into standard-based mathematics curriculum.Using a design-based research approach, the project will generate new knowledge about whether, and how, urban Black and Latinx elementary school students can increase mathematics achievement by engaging in short culturally relevant robot coding activities. This project will also examine potential causal features and mechanisms that underlie 300 elementary school students’ achievement, motivation, and interest in mathematics, and inform 12 teachers of possible learning trajectories when applying robot coding activities for mathematics learning. The project will also explore a new approach to professional development by involving teachers in co-designing robot coding activities directly linked to experiences and cultures of their students. The development, testing, and implementation of the new supplemental curriculum takes a central role in this project and involves an interdisciplinary research team. Multiple forms of data (e.g. teacher artifacts, observations, interviews, and student surveys, achievement data, focus group discussions) will be used to compile multidimensional portraits of engagement and practice related to culturally relevant math pedagogy and curriculum design (from the teachers) and math interest and learning (from the students). Quantitative data analysis will consist of descriptive and inferential statistics, including regression analysis, ANCOVA and repeated measures ANOVA. Qualitative data will be analyzed using constant comparative analysis and timelining analysis. Using the information obtained from context analysis and exploration of the problem/issues, the study will develop domain-specific theories that consider the sociocultural perspective and empirical knowledge from educational research.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.