Mine Dogucu, Sinem Demirci, Harry Bendekgey, Federica Zoe Ricci, Catalina M. Medina
{"title":"本科数据科学教育:谁拿着麦克风,他们在说什么?","authors":"Mine Dogucu, Sinem Demirci, Harry Bendekgey, Federica Zoe Ricci, Catalina M. Medina","doi":"arxiv-2403.03387","DOIUrl":null,"url":null,"abstract":"The presence of data science has been profound in the scientific community in\nalmost every discipline. An important part of the data science education\nexpansion has been at the undergraduate level. We conducted a systematic\nliterature review to (1) specify current evidence and knowledge gaps in\nundergraduate data science education and (2) inform policymakers and data\nscience educators/practitioners about the present status of data science\neducation research. The majority of the publications in data science education\nthat met our search criteria were available open-access. Our results indicate\nthat data science education research lacks empirical data and reproducibility.\nNot all disciplines contribute equally to the field of data science education.\nComputer science and data science as a separate field emerge as the leading\ncontributors to the literature. In contrast, fields such as statistics,\nmathematics, as well as other fields closely related to data science exhibit a\nlimited presence in studies. We recommend that federal agencies and researchers\n1) invest in empirical data science education research; 2) diversify research\nefforts to enrich the spectrum of types of studies; 3) encourage scholars in\nkey data science fields that are currently underrepresented in the literature\nto contribute more to research and publications.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":"32 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Undergraduate data science education: Who has the microphone and what are they saying?\",\"authors\":\"Mine Dogucu, Sinem Demirci, Harry Bendekgey, Federica Zoe Ricci, Catalina M. Medina\",\"doi\":\"arxiv-2403.03387\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The presence of data science has been profound in the scientific community in\\nalmost every discipline. An important part of the data science education\\nexpansion has been at the undergraduate level. We conducted a systematic\\nliterature review to (1) specify current evidence and knowledge gaps in\\nundergraduate data science education and (2) inform policymakers and data\\nscience educators/practitioners about the present status of data science\\neducation research. The majority of the publications in data science education\\nthat met our search criteria were available open-access. Our results indicate\\nthat data science education research lacks empirical data and reproducibility.\\nNot all disciplines contribute equally to the field of data science education.\\nComputer science and data science as a separate field emerge as the leading\\ncontributors to the literature. In contrast, fields such as statistics,\\nmathematics, as well as other fields closely related to data science exhibit a\\nlimited presence in studies. We recommend that federal agencies and researchers\\n1) invest in empirical data science education research; 2) diversify research\\nefforts to enrich the spectrum of types of studies; 3) encourage scholars in\\nkey data science fields that are currently underrepresented in the literature\\nto contribute more to research and publications.\",\"PeriodicalId\":501323,\"journal\":{\"name\":\"arXiv - STAT - Other Statistics\",\"volume\":\"32 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - STAT - Other Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2403.03387\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - STAT - Other Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2403.03387","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Undergraduate data science education: Who has the microphone and what are they saying?
The presence of data science has been profound in the scientific community in
almost every discipline. An important part of the data science education
expansion has been at the undergraduate level. We conducted a systematic
literature review to (1) specify current evidence and knowledge gaps in
undergraduate data science education and (2) inform policymakers and data
science educators/practitioners about the present status of data science
education research. The majority of the publications in data science education
that met our search criteria were available open-access. Our results indicate
that data science education research lacks empirical data and reproducibility.
Not all disciplines contribute equally to the field of data science education.
Computer science and data science as a separate field emerge as the leading
contributors to the literature. In contrast, fields such as statistics,
mathematics, as well as other fields closely related to data science exhibit a
limited presence in studies. We recommend that federal agencies and researchers
1) invest in empirical data science education research; 2) diversify research
efforts to enrich the spectrum of types of studies; 3) encourage scholars in
key data science fields that are currently underrepresented in the literature
to contribute more to research and publications.