Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917695953305600 |
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| author | Shah, Karan Butler, Julie Knaub, Alexis Zenginoğlu, Anıl Ratcliff, William Soltanieh-ha, Mohammad |
| author_facet | Shah, Karan Butler, Julie Knaub, Alexis Zenginoğlu, Anıl Ratcliff, William Soltanieh-ha, Mohammad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00961 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice Shah, Karan Butler, Julie Knaub, Alexis Zenginoğlu, Anıl Ratcliff, William Soltanieh-ha, Mohammad Physics Education Machine Learning Data Analysis, Statistics and Probability 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. |
| title | Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice |
| topic | Physics Education Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2403.00961 |