BPC-DP: Coding Like a Data Miner: A Culturally Relevant Data Analytics Intervention for High School Students

随着互联网的出现和使用的增加，社交媒体已成为人们生活中不可或缺的一部分。Facebook、Twitter和TikTok等平台产生了大量数据，可以对这些数据进行分析，以获得一系列见解。这强调了对教育机会的需求，学生可以探索大数据方法来提取，可视化和批判性地分析复杂的算法和数据结构。这个示范项目将开发一个大数据课程，通过Twitter和文化相关的设计使用尖端的社交媒体数据挖掘技术，吸引来自西德克萨斯州/埃尔帕索地区代表性不足的群体的学生。该课程将由一个教师和学生团队共同设计，然后在埃尔帕索高中进行试点，这些高中的学生人数众多，人数不足。该项目的成果有可能改变计算和数据素养的模式，学生可以利用自己的个人兴趣参与创建计算工件并浏览他人的产品。该BPC示范项目旨在为代表性不足的大学预科学生群体提供以“大数据”为中心的计算机和数据科学教学和学习的循证见解。该团队将迭代开发和试点一个与文化相关的数据挖掘和分析课程单元，教师和学生分别服务于或来自代表性不足的群体。该团队将利用混合方法来检查CS教育和学习科学的学习成果。本研究由两个研究问题指导：（1）需要哪些关键的学习和教学资源来有效地维持CS课程干预，强调文化相关的数据挖掘和分析？(2)在实施CS教育计划时，强调文化相关的数据挖掘和分析，会产生什么样的学习体验和成果？该奖项反映了NSF的法定使命，并被认为是值得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的支持。

With the advent and increased use of the internet, social media has become an integral part of people’s lives. Platforms such as Facebook, Twitter, and TikTok generate a large volume of data that can be analyzed for a range of insights. This underscores the need for educational opportunities in which students can explore big data approaches to extract, visualize, and critically analyze complex algorithms and data structures. This demonstration project will develop a big data curriculum that uses cutting-edge social media data mining techniques via Twitter and a culturally relevant design to engage students from underrepresented groups in the West Texas/El Paso region. The curriculum will be co-designed by a team of teachers and students, and then piloted in El Paso high schools, which have a large population of students who are underrepresented. The outcomes of this project have the potential to transform models of computing and data literacy in which students access their own personal interests to participate in the creation of computational artifacts and navigate the products of others.This BPC Demonstration Project aims to provide evidence-based insights on “Big Data”-centric computer and data science teaching and learning with underrepresented pre-college student populations. The team will iteratively develop and pilot a culturally relevant data mining and analytics curricular unit with groups of teachers and students who, respectively, serve or come from underrepresented groups. The team will leverage mixed-methodological approaches to examine learning outcomes for CS education and the learning sciences. This research is guided by two research questions: (1) What critical learning and instructional resources are needed to productively sustain a CS curricular intervention that emphasizes culturally relevant data mining and analytics?, (2) What learning experiences and outcomes result when implementing a CS education program that emphasizes culturally relevant data mining and analytics?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.