Issue 6.2, Spring 2024最新文献

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Data Science Experiences and Emotions:  From Lonesome to Awesome 数据科学体验与情感: 从孤独到令人敬畏
Issue 6.2, Spring 2024 Pub Date : 2024-05-01 DOI: 10.1162/99608f92.16558941
Xiao-Li Meng
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引用次数: 0
Understanding the Data in K-12 Data Science 了解 K-12 数据科学中的数据
Issue 6.2, Spring 2024 Pub Date : 2024-03-25 DOI: 10.1162/99608f92.4f3ac3da
Rotem Israel-Fishelson, Peter F. Moon, Rachel Tabak, David Weintrop
{"title":"Understanding the Data in K-12 Data Science","authors":"Rotem Israel-Fishelson, Peter F. Moon, Rachel Tabak, David Weintrop","doi":"10.1162/99608f92.4f3ac3da","DOIUrl":"https://doi.org/10.1162/99608f92.4f3ac3da","url":null,"abstract":"Our increasingly data-driven world is amplifying the need for everyone to develop foundational data literacy skills. In response, a growing number of K-12 data science curricula are being designed to introduce all students to data. These curricula define what data science is at the high school level and directly shape how students are introduced to and understand the discipline. Ensuring these curricula are effective, engaging, and, most critically, equitable is of paramount importance. This paper presents a qualitative analysis of four curricula, focusing on the data used to introduce learners to the field of data science. The analysis uses a series of analytical lenses to evaluate the 296 distinct datasets used across the curricula and identifies trends and best practices in dataset selection. The analysis includes using data collected from high school students about their interests and experiences with data to understand if and how contemporary data science curricula are tapping into students' lived experiences to situate data science learning experiences. The findings show that the curricula use relatively recent and small datasets covering a range of topics and that there is limited learner involvement in dataset selection. Further, the analysis reveals gaps between the datasets used and students' self-reported interests. This work highlights the importance of dataset selection, especially as it relates to supporting learners from historically excluded populations in technology fields. Finally, this paper provides practical implications to assess existing curricula and advances our understanding of","PeriodicalId":517001,"journal":{"name":"Issue 6.2, Spring 2024","volume":" 909","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140383111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
In the Academy, Data Science Is Lonely: Barriers to Adopting Data Science Methods for Scientific Research 在学术界,数据科学是孤独的:科学研究采用数据科学方法的障碍
Issue 6.2, Spring 2024 Pub Date : 2024-02-09 DOI: 10.1162/99608f92.7ca04767
Gabrielle O’Brien, Jordan Mick
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引用次数: 0
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