Developing Modernized Data Science Instruction in Psychology Curricula

该项目旨在通过开发，评估和共享基于心理学的本科教育数据科学课程材料来服务于国家利益。数据科学是一个新兴且快速发展的跨学科领域，它使用计算方法来理解大型数据集中的信息。迫切需要以证据为基础的教育材料，以支持本科生进行大量学术课程的数据科学培训。心理学是数据科学整合的优秀课程，因为它是最受欢迎的本科学术专业之一，对女性和代表性不足的学生特别有吸引力。这个学生学习（1级）项目将有助于心理学本科生的示范STEM教育和数据科学准备的发展。它将揭示教授现代数据科学技能的有效策略，并为此类教学中的最佳实践提供科学指导。最后，通过免费分发任何学科的教师都可以使用的数据科学注入的心理学课程材料，该项目将使教育工作者和学习者能够适应基于证据的数据科学内容，这些内容有可能大大提高本科生在动态中取得学术和职业成功的准备，这个项目将导致心理学实验室研究课程的发展，在整个15周的学期中有意嵌入和支撑数据科学技能。这些数据科学增强课程将加强学生的计算能力，同时为他们未来的专业和学术发展提供多种机会。这些课程将使学生能够应用广泛的有价值的现代数据科学技能，这些技能植根于计算机编程，建立有效的数据采集，编码和分析，探索大型数据集，可视化复杂的行为和生理数据，并透明地传达算法流程，以保证分析的可复制性。该项目包括一项为期三年的严格的前/后研究，以系统地评估新开发的数据科学课程经验，并确定它们如何影响学生的批判性思维能力，定量素养以及对科学和数据科学技能的信心。该数据集将包括一个数据科学心理学治疗组和一个非数据科学对照组，以比较数据科学技能和数据素养信心的提高。最后，开发的课程材料将作为可公开访问的开放教育资源（OER）发布，以便该项目的产品将有助于数据科学的新兴教学法。NSF IUSE：EDU计划支持研究和开发项目，以提高所有学生STEM教育的有效性。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。

This project aims to serve the national interest by developing, assessing, and sharing psychology-based data science course materials for undergraduate education. Data science is an emerging and rapidly growing interdisciplinary field that uses computational methods to understand information in large datasets. There is an urgent need for evidence-based educational materials that support data science training for undergraduate students pursuing a vast array of academic programs. Psychology is an excellent program for data science integration because it is among the most popular undergraduate academic majors and it is especially attractive to women and underrepresented students. This Engaged Student Learning (Level 1) project will contribute to the development of exemplary STEM education and data science preparation for undergraduate students within psychology. It will reveal effective strategies for teaching modern data science skills and provide scientific guidance regarding best practices in such instruction. Finally, by freely distributing data science-infused psychology course materials that are available to teachers of any discipline, this project will empower educators and learners to adapt evidence-based data science content that has the potential to dramatically increase undergraduate students’ readiness for academic and professional success in a dynamic, information-rich world.This project will lead to the development of psychology laboratory research courses that intentionally embed and scaffold data science skills throughout a 15-week semester. These data science-enhanced courses will strengthen students’ computational skills while providing them with multiple opportunities for future professional and academic growth. The courses will empower students to apply a broad array of valuable, modern data science skills, rooted in computer programming, establish effective data acquisition, coding and analysis, explore large datasets, visualize complex behavioral and physiological data, and transparently communicate algorithmic processes to guarantee analysis replicability. The project includes a three-year, rigorous, pre/post research study to systematically assess the newly developed data science course experiences and determine how they influence students’ capabilities in critical thinking, quantitative literacy, and confidence in scientific and data science skills. The dataset will include a data science in psychology treatment group and a non-data science control group to compare gains in data science skills and data literacy confidence. Finally, the developed course materials will be released as publicly accessible Open Educational Resources (OER) so that the products of this project will contribute to the nascent pedagogy of data science. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.