Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice

Autor:	Shah, Karan, Butler, Julie, Knaub, Alexis, Zenginoğlu, Anıl, Ratcliff, William, Soltanieh-ha, Mohammad
Rok vydání:	2024
Předmět:	Physics - Physics Education Computer Science - Machine Learning Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
Popis:	It is becoming increasingly important that physics educators equip their students with the skills to work with data effectively. However, many educators may lack the necessary training and expertise in data science to teach these skills. To address this gap, we created the Data Science Education Community of Practice (DSECOP), bringing together graduate students and physics educators from different institutions and backgrounds to share best practices and lessons learned from integrating data science into undergraduate physics education. In this article we present insights and experiences from this community of practice, highlighting key strategies and challenges in incorporating data science into the introductory physics curriculum. Our goal is to provide guidance and inspiration to educators who seek to integrate data science into their teaching, helping to prepare the next generation of physicists for a data-driven world. Comment: 21 pages, 4 figures, 2 tables. The associated GItHub repository can be found at https://github.com/GDS-Education-Community-of-Practice/DSECOP
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.00961 Zobrazit plný text záznamu View this record from Arxiv