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Journal articles on the topic 'Data Science Education'

To see the other types of publications on this topic, follow the link: Data Science Education.

Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Serik, M., G. Nurbekova, and J. Kultan. "Big data technology in education." Bulletin of the Karaganda University. Pedagogy series 100, no. 4 (December 28, 2020): 8–15. http://dx.doi.org/10.31489/2020ped4/8-15.

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The article discusses the implementation of big data in the educational process of higher education. The authors, analyzing a large amount of data, referring to the types of services provided by e-government, indicate that there are many pressing problems, many services are not yet automated. In order to improve the professional training of teachers of Computer Science of the L.N. Gumilyov Eurasian National University, educational programs and courses have been developed 7M01514 — «Smart City technologies», «Big Data and cloud computing» and 7М01525 — «STEM-Education», «The Internet of Things and Intelligent Systems «on the theoretical and practical foundations of big data and introduced into the educational process. The arti-cle discusses several types of programs for teaching big data and analyzes data on the implementation of big data in some educational institutions. For the introduction and implementation of special courses in the educational process in the areas of magistracy in the educational program Computer Science, the curriculum, educational and methodological complex, digital educational resources are considered, as well as hardware and software that collects, stores, sorts big data, well as the introduction into the educational process of theoretical foundations and methods of using the developed technical and technological equipment.

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Logan, Jessica A. R., Sara A. Hart, and Christopher Schatschneider. "Data Sharing in Education Science." AERA Open 7 (January 2021): 233285842110064. http://dx.doi.org/10.1177/23328584211006475.

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Many research agencies are now requiring that data collected as part of funded projects be shared. However, the practice of data sharing in education sciences has lagged these funder requirements. We assert that this is likely because researchers generally have not been made aware of these requirements and of the benefits of data sharing. Furthermore, data sharing is usually not a part of formal training, so many researchers may be unaware of how to properly share their data. Finally, the research culture in education science is often filled with concerns regarding the sharing of data. In this article, we address each of these areas, discussing the wide range of benefits of data sharing, the many ways by which data can be shared; provide a step by step guide to start sharing data; and respond to common concerns.

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DE VEAUX, RICHARD, ROGER HOERL, RON SNEE, and PAUL VELLEMAN. "TOWARD HOLISTIC DATA SCIENCE EDUCATION." STATISTICS EDUCATION RESEARCH JOURNAL 21, no. 2 (July 4, 2022): 2. http://dx.doi.org/10.52041/serj.v21i2.40.

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Holistic data science education places data science in the context of real world applications, emphasizing the purpose for which data were collected, the pedigree of the data, the meaning inherent in the daa, the deploying of sustainable solutions, and the communication of key findings for addressing the original problem. As such it spends less emphasis on coding, computing, and high-end black-box algorithms. We argue that data science education must move toward a holistic curriculum, and we provide examples and reasons for this emphasis.

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Cao, Longbing. "Data Science: Profession and Education." IEEE Intelligent Systems 34, no. 5 (September 1, 2019): 35–44. http://dx.doi.org/10.1109/mis.2019.2936705.

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Cooper, M. M. "Data-Driven Education Research." Science 317, no. 5842 (August 31, 2007): 1171. http://dx.doi.org/10.1126/science.317.5842.1171.

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Gürsakal, Necmi, Ecem Ozkan, Fırat Melih Yılmaz, and Deniz Oktay. "How Should Data Science Education Be?" International Journal of Energy Optimization and Engineering 9, no. 2 (April 2020): 25–36. http://dx.doi.org/10.4018/ijeoe.2020040103.

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The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

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Dill-McFarland, Kimberly A., Stephan G. König, Florent Mazel, David C. Oliver, Lisa M. McEwen, Kris Y. Hong, and Steven J. Hallam. "An integrated, modular approach to data science education in microbiology." PLOS Computational Biology 17, no. 2 (February 25, 2021): e1008661. http://dx.doi.org/10.1371/journal.pcbi.1008661.

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We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.

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8

HIGUCHI, Isao. "Data Science Education Focused on Statistics." Journal of JSEE 70, no. 4 (2022): 4_8–4_11. http://dx.doi.org/10.4307/jsee.70.4_8.

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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. "Data science: opportunities to transform education." CTE Workshop Proceedings 6 (March 21, 2019): 48–73. http://dx.doi.org/10.55056/cte.368.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.

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10

BIEHLER, ROLF, RICHARD DE VEAUX, JOACHIM ENGEL, SIBEL KAZAK, and DANIEL FRISCHEMEIER. "EDITORIAL: RESEARCH ON DATA SCIENCE EDUCATION." STATISTICS EDUCATION RESEARCH JOURNAL 21, no. 2 (July 4, 2022): 1. http://dx.doi.org/10.52041/serj.v21i2.606.

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A very warm welcome to this Special Issue of the Statistics Education Research Journal (SERJ) on data science education. Our hope is to give an overview of selected theoretical thoughts and empirical studies on data science education from a statistics education research perspective. Data science education is rapidly developing but research into data science education is still in its infancy. The current issue presents a snapshot of this developing field.

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McFarland, Daniel A., Saurabh Khanna, Benjamin W. Domingue, and Zachary A. Pardos. "Education Data Science: Past, Present, Future." AERA Open 7 (January 2021): 233285842110520. http://dx.doi.org/10.1177/23328584211052055.

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This AERA Open special topic concerns the large emerging research area of education data science (EDS). In a narrow sense, EDS applies statistics and computational techniques to educational phenomena and questions. In a broader sense, it is an umbrella for a fleet of new computational techniques being used to identify new forms of data, measures, descriptives, predictions, and experiments in education. Not only are old research questions being analyzed in new ways but also new questions are emerging based on novel data and discoveries from EDS techniques. This overview defines the emerging field of education data science and discusses 12 articles that illustrate an AERA-angle on EDS. Our overview relates a variety of promises EDS poses for the field of education as well as the areas where EDS scholars could successfully focus going forward.

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12

WATANABE, Michiko. "Data Science Education in AI Society." Journal of JSEE 70, no. 1 (2022): 1_30–1_35. http://dx.doi.org/10.4307/jsee.70.1_30.

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13

Keßler, Carsten, Mathieu d'Aquin, and Stefan Dietze. "Linked Data for science and education." Semantic Web 4, no. 1 (2013): 1–2. http://dx.doi.org/10.3233/sw-120091.

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14

Kross, Sean, Roger D. Peng, Brian S. Caffo, Ira Gooding, and Jeffrey T. Leek. "The Democratization of Data Science Education." American Statistician 74, no. 1 (October 28, 2019): 1–7. http://dx.doi.org/10.1080/00031305.2019.1668849.

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15

Foster, Marva, and Zarin Tasnim. "Data Science and Graduate Nursing Education." Clinical Nurse Specialist 34, no. 3 (2020): 124–31. http://dx.doi.org/10.1097/nur.0000000000000516.

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16

Klašnja-Milićević, Aleksandra, Mirjana Ivanović, and Zoran Budimac. "Data science in education: Big data and learning analytics." Computer Applications in Engineering Education 25, no. 6 (June 9, 2017): 1066–78. http://dx.doi.org/10.1002/cae.21844.

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17

Hagen, Loni, James Andrews, Lisa Federer, and Gerald Benoit. "Data Science Education in Library and Information Science Schools." Proceedings of the Association for Information Science and Technology 56, no. 1 (January 2019): 536–37. http://dx.doi.org/10.1002/pra2.84.

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18

Bishop, Bradley Wade, Ashley Marie Orehek, and Hannah R. Collier. "Job Analyses of Earth Science Data Librarians and Data Managers." Bulletin of the American Meteorological Society 102, no. 7 (July 2021): E1384—E1393. http://dx.doi.org/10.1175/bams-d-20-0163.1.

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AbstractThis study’s purpose is to capture the skills of Earth science data managers and librarians through interviews with current job holders. Job analysis interviews were conducted of 14 participants—six librarians and eight data managers—to assess the types and frequencies of job tasks. Participants identified tasks related to communication, including collaboration, teaching, and project management activities. Data-specific tasks included data discovery, processing, and curation, which require an understanding of the data, technology, and information infrastructures to support data use, reuse, and preservation. Most respondents had formal science education and six had a master’s degree in Library and Information Sciences. Most of the knowledge, skills, and abilities for these workers were acquired through on-the-job experience, but future professionals in these careers may benefit from tailored education informed through job analyses.

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19

Kumar, Deepak. "Data science overtakes computer science?" ACM Inroads 3, no. 3 (September 2012): 18–19. http://dx.doi.org/10.1145/2339055.2339060.

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20

HATTORI, Masashi. "How Should Higher Education Comprehend Data Science Education and DX?" Journal of JSEE 70, no. 1 (2022): 1_3–1_6. http://dx.doi.org/10.4307/jsee.70.1_3.

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21

Pennington, Deana, Imme Ebert-Uphoff, Natalie Freed, Jo Martin, and Suzanne A. Pierce. "Bridging sustainability science, earth science, and data science through interdisciplinary education." Sustainability Science 15, no. 2 (September 25, 2019): 647–61. http://dx.doi.org/10.1007/s11625-019-00735-3.

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22

VANCE, ERIC A., DAVID R. GLIMP, NATHAN D. PIEPLOW, JANE M. GARRITY, and BRETT A. MELBOURNE. "INTEGRATING THE HUMANITIES INTO DATA SCIENCE EDUCATION." STATISTICS EDUCATION RESEARCH JOURNAL 21, no. 2 (July 4, 2022): 9. http://dx.doi.org/10.52041/serj.v21i2.42.

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Despite growing calls to develop data science students’ ethical awareness and expand human-centered approaches to data science education, introductory courses in the field remain largely technical. A new interdisciplinary data science program aims to merge STEM and humanities perspectives starting at the very beginning of the data science curriculum. Existing literature suggests that humanities integration can make STEM courses more appealing to a wider range of students, including women and students of color, and enhance student learning of essential concepts and foundational reasoning skills, such as those collectively known as data acumen. Cultivating students’ data acumen requires a more inclusive vision of how the knowledge and insights generated through computational methods and statistical analysis relates to other ways of knowing.

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23

Hazzan, Orit, and Koby Mike. "A journal for interdisciplinary data science education." Communications of the ACM 64, no. 8 (August 2021): 10–11. http://dx.doi.org/10.1145/3469281.

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The Communications website, http://cacm.acm.org, features more than a dozen bloggers in the BLOG@CACM community. In each issue of Communications , we'll publish selected posts or excerpts. twitter Follow us on Twitter at http://twitter.com/blogCACM http://cacm.acm.org/blogs/blog-cacm Orit Hazzan and Koby Mike on the need for a journal to cover data science education exclusively.

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24

Bonnell, Jerry, Mitsunori Ogihara, Yelena Yesha, and Irena Bojanova. "Challenges and Issues in Data Science Education." Computer 55, no. 2 (February 2022): 63–66. http://dx.doi.org/10.1109/mc.2021.3128734.

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25

Witmer, Jeff. "Inclusivity in Statistics and Data Science Education." Journal of Statistics and Data Science Education 29, no. 1 (January 2, 2021): 2–3. http://dx.doi.org/10.1080/26939169.2021.1906555.

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26

Libarkin, Julie C., and Josepha P. Kurdziel. "Research Methodologies in Science Education: Qualitative Data." Journal of Geoscience Education 50, no. 2 (March 2002): 195–200. http://dx.doi.org/10.1080/10899995.2002.12028052.

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27

Johnson, Jeffrey, Luca Tesei, Marco Piangerelli, Emanuela Merelli, Riccardo Paci, Nenad Stojanovic, Paulo Leitão, José Barbosa, and Marco Amador. "Big Data: Business, Technology, Education, and Science." Ubiquity 2018, July (July 26, 2018): 1–13. http://dx.doi.org/10.1145/3158350.

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28

Oh, Sam, Il‐Yeol Song, Javed Mostafa, Yin Zhang, and Dan Wu. "Data science education in the iSchool context." Proceedings of the Association for Information Science and Technology 56, no. 1 (January 2019): 558–60. http://dx.doi.org/10.1002/pra2.90.

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29

Doonan, Ashley, Dharma Akmon, and Evan Cosby. "An Exploratory Analysis of Social Science Graduate Education in Data Management and Data Sharing." International Journal of Digital Curation 15, no. 1 (July 22, 2020): 18. http://dx.doi.org/10.2218/ijdc.v15i1.671.

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Effective data management and data sharing are crucial components of the research lifecycle, yet evidence suggests that many social science graduate programs are not providing training in these areas. The current exploratory study assesses how U.S. masters and doctoral programs in the social sciences include formal, non-formal, and informal training in data management and sharing. We conducted a survey of 150 graduate programs across six social science disciplines, and used a mix of closed and open-ended questions focused on the extent to which programs provide such training and exposure. Results from our survey suggested a deficit of formal training in both data management and data sharing, limited non-formal training, and cursory informal exposure to these topics. Utilizing the results of our survey, we conducted a syllabus analysis to further explore the formal and non-formal content of graduate programs beyond self-report. Our syllabus analysis drew from an expanded seven social science disciplines for a total of 140 programs. The syllabus analysis supported our prior findings that formal and non-formal inclusion of data management and data sharing training is not common practice. Overall, in both the survey and syllabi study we found a lack of both formal and non-formal training on data management and data sharing. Our findings have implications for data repository staff and data service professionals as they consider their methods for encouraging data sharing and prepare for the needs of data depositors. These results can also inform the development and structuring of graduate education in the social sciences, so that researchers are trained early in data management and sharing skills and are able to benefit from making their data available as early in their careers as possible.

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Sumarni, Woro, Zulfatul Faizah, Bambang Subali, W. Wiyanto, and Ellianawati Ellianawati. "The urgency of religious and cultural science in STEM education: A meta data analysis." International Journal of Evaluation and Research in Education (IJERE) 9, no. 4 (December 1, 2020): 1045. http://dx.doi.org/10.11591/ijere.v9i4.20462.

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The 21st century education engages students to have higher order thinking and scientific literacy skills. However, these abilities in Indonsia are still relatively low, especially student’s scientific literacy skills. One solution to the problem of low scientific literacy skills in Indonesia is the application of STEM education. However, as technology advances pursued in STEM education, religious and cultural sciences increasingly separate themselves from science. Cultural science is rarely implemented in learning so that many students do not know their own culture. The contra between science and religion also makes students’s perception of religion and science are two independent knowledge and cannot be united. Through literature studies that have been carried out from various journal article search sites, this article discusses how important religious and cultural sciences are to be implemented into STEM education. The relationship between religion and science, culture and science, culture and religion and the urgency of religion and culture in STEM education are also discussed in this article. The results of this study propose solutions so that science education can be implemented with RE-STEM learning to overcome the gap between religion, culture and science. So, that students will have a more balanced knowledge in religion, ethnoscience and science from RE-STEM integration.

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SUZUKI, Shizuo, Kohei TANAKA, Takumi OHNUMA, Shingo TAMAKI, Katsuhisa OOBA, Motoshi SAKAI, Masayuki TAKEGUCHI, Hidenori SHIRAI, and Kyoji YOSHINO. "Data Science Education by Remote Method with Satellite Data Platform." Journal of JSEE 69, no. 4 (2021): 4_80–4_85. http://dx.doi.org/10.4307/jsee.69.4_80.

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32

Yu, Bei, and Xiao Hu. "Toward Training and Assessing Reproducible Data Analysis in Data Science Education." Data Intelligence 1, no. 4 (November 2019): 381–92. http://dx.doi.org/10.1162/dint_a_00053.

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Reproducibility is a cornerstone of scientific research. Data science is not an exception. In recent years scientists were concerned about a large number of irreproducible studies. Such reproducibility crisis in science could severely undermine public trust in science and science-based public policy. Recent efforts to promote reproducible research mainly focused on matured scientists and much less on student training. In this study, we conducted action research on students in data science to evaluate to what extent students are ready for communicating reproducible data analysis. The results show that although two-thirds of the students claimed they were able to reproduce results in peer reports, only one-third of reports provided all necessary information for replication. The actual replication results also include conflicting claims; some lacked comparisons of original and replication results, indicating that some students did not share a consistent understanding of what reproducibility means and how to report replication results. The findings suggest that more training is needed to help data science students communicating reproducible data analysis.

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Farmer, Mike. "Science project data bases." Journal of Chemical Education 67, no. 10 (October 1990): A257. http://dx.doi.org/10.1021/ed067pa257.

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Glukhov, Pavel, Andrey Deryabin, and Aleksandr Popov. "Data Literacy as a meta-skill: options for Data Science curriculum implementation." SHS Web of Conferences 98 (2021): 05006. http://dx.doi.org/10.1051/shsconf/20219805006.

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Data science is affecting an increasingly wide area of everyday life but general education in Russia has not yet reacted to the new challenges associated with this aspect of digitalization. The changes in technologies, the economy, and society over the last two decades have formed a new agenda for teaching mathematics and information technologies, as well as media education and social sciences. Education in all these fields requires a reconsideration of the content and methods of teaching due to the increasing importance of data science and artificial intelligence in the context of fundamental changes in the economy and the labor market. As many areas of human life are changing, there is a need to formulate new types and kinds of educational results, at which modern pedagogy should be aimed. A modern way of meeting such challenges is to distinguish new literacies (media literacy, environmental literacy, functional literacy, etc.). The article deals with the concept of data literacy, examines its content and composition, and substantiates its relevance as an educational result consistent with digitalization trends that one can observe in modern society. A distinction is made between approaches to in-depth and general studies of data science. A description is given of various types of tasks aimed at developing data literacy among students in the context of their setting on different educational material. The authors consider possible ways of deploying programs aimed at mastering data science by students without the need to formalize it into a separate discipline or school subject.

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Song, Il-Yeol, and Yongjun Zhu. "Big Data and Data Science: Opportunities and Challenges of iSchools." Journal of Data and Information Science 2, no. 3 (August 22, 2017): 1–18. http://dx.doi.org/10.1515/jdis-2017-0011.

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AbstractDue to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools’ opportunities and suggestions in data science education. We argue that iSchools should empower their students with “information computing” disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application-based. These three foci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework (DSEF) with building blocks that include the three pillars of data science (people, technology, and data), computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.

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Chang, Mido, Kusum Singh, and Yun Mo. "Science engagement and science achievement: Longitudinal models using NELS data." Educational Research and Evaluation 13, no. 4 (August 2007): 349–71. http://dx.doi.org/10.1080/13803610701702787.

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Wilkerson, Michelle Hoda, and Joseph L. Polman. "Situating Data Science: Exploring How Relationships to Data Shape Learning." Journal of the Learning Sciences 29, no. 1 (December 20, 2019): 1–10. http://dx.doi.org/10.1080/10508406.2019.1705664.

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38

Blatecky, Alan, Jean-Claude Guédon, and Carlos Morais Pires. "Scientific Data Infrastructures: Transforming Science, Education, and Society." Zeitschrift für Bibliothekswesen und Bibliographie 60, no. 6 (December 12, 2013): 325–31. http://dx.doi.org/10.3196/186429501360653.

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39

YAMAMOTO, Yoshiro, and Makoto TOMITA. "Cross-Departmental Data Science Education at a University." Journal of JSEE 70, no. 1 (2022): 1_26–1_29. http://dx.doi.org/10.4307/jsee.70.1_26.

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40

HAMAMOTO, Kazuhiko, and Mitsukuni YASUI. "Purpose of Special Issue on “Data Science Education”." Journal of JSEE 70, no. 1 (2022): 1_2. http://dx.doi.org/10.4307/jsee.70.1_2.

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41

Taber, Keith S. "Visual data and their use in science education." Teacher Development 19, no. 4 (October 2, 2015): 573–76. http://dx.doi.org/10.1080/13664530.2015.1093281.

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42

DeMasi, Orianna, Alexandra Paxton, and Kevin Koy. "Ad hoc efforts for advancing data science education." PLOS Computational Biology 16, no. 5 (May 7, 2020): e1007695. http://dx.doi.org/10.1371/journal.pcbi.1007695.

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43

Pratsri, Sajeewan, Prachyanun Nilsook, and Panita Wannapiroon. "Synthesis of Data Science Competency for Higher Education Students." International Journal of Education and Information Technologies 16 (January 31, 2022): 101–9. http://dx.doi.org/10.46300/9109.2022.16.11.

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The research aims to Data Science Performance Synthesis for Higher Education Students and Data Science Performance Suitability Assessment for Higher Education Students. The research instruments include 1) data science performance synthesis tables, 2) expert interviews in data science performance assessments, 3) expert questionnaires to assess the consistency of data science performance. Analytical methods include 1) analyzing the frequency obtained from the content analysis table, 2) synthesis of content from interviews, 3) analyzing performance consistency, and components of data science performance, from data science synthesis for higher education students, finding that data performance for higher education students consists of five performances: 1) programming skills, 2)elementary statistics, 3) fundamentals of data science, 4) data preparation, and 5) Big data analytics.

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Ives, Zachary G., Rachel Pottinger, Arun Kumar, Johannes Gehrke, and Jana Giceva. "The future of data(base) education." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 3239. http://dx.doi.org/10.14778/3476311.3476394.

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This panel encourages a debate over the future of database education and its relationship to Data Science: Are Computer Science (CS) and Data Science (DS) different disciplines about to split, and how does that effect how we teach our field? Is there a "data" course that belongs in CS that all of our students should take? Who is the traditional database course, e.g. based on the "cow book", relevant to? What traditional topics should we not be teaching in our core data course(s) and which ones should be added? What do we teach the student who has one elective for data science? How does our community position itself for leadership in CS given the popularity of DS?

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Saeed, Zeeshan, Zaryab Fatima, and Umair Ahmed. "SOCIAL SCIENCE EDUCATION OF PAKISTAN IN DIRE STRAITS: A SECONDARY DATA ANALYSIS." Gomal University Journal of Research 38, no. 03 (October 3, 2022): 271–84. http://dx.doi.org/10.51380/gujr-38-03-03.

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Social science disciplines have consistently been viewed for granted when contrasted with the physical sciences in the academia of Pakistan. The key objective of this research was to analyze the progress in the Social Science Education in terms of curriculum development, faculty increment and HEC policy Priority through content analysis. The Academic Dependency Theory was used for theoretical underpinnings. The qualitative research paradigm was used for this study. The relevant secondary data was gathered from books, articles, diaries, Bureau of statistics surveys, and other chronicled records of HEC and broken down appropriately. Applicable published and non-published information from 2003 to 2019 was chosen arbitrarily, of which approx. One hundred fifty articles were taken as sample. According to research findings, higher Education in Pakistan was designed without a solid policy and methodology. As a result, social science disciplines faced numerous challenges, including the defenseless instructional program development, a scarcity of skilled labor, dubious research frameworks, and inappropriate research projects.

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Duschl, Richard, Lucy Avraamidou, and Nathália Helena Azevedo. "Data-Texts in the Sciences." Science & Education 30, no. 5 (April 28, 2021): 1159–81. http://dx.doi.org/10.1007/s11191-021-00225-y.

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AbstractGrounded within current reform recommendations and built upon Giere’s views (1986, 1999) on model-based science, we propose an alternative approach to science education which we refer to as the Evidence-Explanation (EE) Continuum. The approach addresses conceptual, epistemological, and social domains of knowledge, and places emphasis on the epistemological conversations about data acquisitions and transformations in the sciences. The steps of data transformation, which we refer to as data-texts, we argue, unfold the processes of using evidence during knowledge building and reveal the dynamics of scientific practices. Data-texts involve (a) obtaining observations/measurements to become data; (b) selecting and interpreting data to become evidence; (c) using evidence to ascertain patterns and develop models; and (d) utilizing the patterns and models to propose and refine explanations. Throughout the transformations of the EE continuum, there are stages of transition that foster the engagement of learners in negotiations of meaning and collective construction of knowledge. A focus on the EE continuum facilitates the emergence of further insights, both by questioning the nature of the data and its multiple possibilities for change and representations and by reflecting on the nature of the explanations. The shift of emphasis to the epistemics of science holds implications for the design of learning environments that support learners in developing contemporary understandings of the nature and processes of scientific practices.

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Braaten, Melissa, Chris Bradford, Kathryn L. Kirchgasler, and Sadie Fox Barocas. "How data use for accountability undermines equitable science education." Journal of Educational Administration 55, no. 4 (July 3, 2017): 427–46. http://dx.doi.org/10.1108/jea-09-2016-0099.

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Purpose When school leaders advance strategic plans focused on improving educational equity through data-driven decision making, how do policies-as-practiced unfold in the daily work of science teachers? The paper aims to discuss this issue. Design/methodology/approach This ethnographic study examines how data-centric accountability and improvement efforts surface as practices for 36 science teachers in three secondary schools. For two years, researchers were embedded in schools alongside teachers moving through daily classroom practice, meetings with colleagues and leaders, data-centric meetings, and professional development days. Findings Bundled initiatives created consequences for science educators including missed opportunities to capitalize on student-generated ideas, to foster science sensemaking, and to pursue meaningful and equitable science learning. Problematic policy-practice intersections arose, in part, because of school leaders’ framing of district and school initiatives in ways that undermined equity in science education. Practical implications From the perspective of science education, this paper raises an alarming problem for equitable science teaching. Lessons learned from missteps seen in this study have practical implications for others attempting similar work. The paper suggests alternatives for supporting meaningful and equitable science education. Originality/value Seeing leaders’ framing of policy initiatives, their bundling of performance goals, equity and accountability efforts, and their instructional coaching activities from the point of view of teachers affords unique insight into how leadership activities mediate policies in schools.

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Shayer, Michael. "Data processing and science investigation in schools." Research Papers in Education 1, no. 3 (October 1986): 237–53. http://dx.doi.org/10.1080/0267152860010305.

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Vellom, R. Paul, and Charles W. Anderson. "Reasoning about data in middle school science." Journal of Research in Science Teaching 36, no. 2 (February 1999): 179–99. http://dx.doi.org/10.1002/(sici)1098-2736(199902)36:2<179::aid-tea5>3.0.co;2-t.

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Virkus, Sirje, and Emmanouel Garoufallou. "Data science and its relationship to library and information science: a content analysis." Data Technologies and Applications 54, no. 5 (October 13, 2020): 643–63. http://dx.doi.org/10.1108/dta-07-2020-0167.

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PurposeThe purpose of this paper is to present the results of a study exploring the emerging field of data science from the library and information science (LIS) perspective.Design/methodology/approachContent analysis of research publications on data science was made of papers published in the Web of Science database to identify the main themes discussed in the publications from the LIS perspective.FindingsA content analysis of 80 publications is presented. The articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences. The category of tools, techniques and applications of data science was most addressed by the authors, followed by data science from the perspective of health sciences, data science education and training and knowledge and skills of the data professional. However, several publications fell into several categories because these topics were closely related.Research limitations/implicationsOnly publication recorded in the Web of Science database and with the term “data science” in the topic area were analyzed. Therefore, several relevant studies are not discussed in this paper that either were related to other keywords such as “e-science”, “e-research”, “data service”, “data curation”, “research data management” or “scientific data management” or were not present in the Web of Science database.Originality/valueThe paper provides the first exploration by content analysis of the field of data science from the perspective of the LIS.

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