Relevant bibliographies by topics / Data science for education

Academic literature on the topic 'Data science for education'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Data science for education"

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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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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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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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Dissertations / Theses on the topic "Data science for education"

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DeVaney, Jonah E. "tidyTouch: An Interactive Visualization Tool for Data Science Education." Digital Commons @ East Tennessee State University, 2020. https://dc.etsu.edu/honors/529.

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Accessibility and usability of software define the programs used for both professional and academic activities. While many proprietary tools are easy to grasp, some challenges exist in using more technical resources, such as the statistical programming language R. The creative project tidyTouch is a web application designed to help educate any user in basic R data visualization and transformation using the popular ggplot2 and dplyr packages. Providing point-and-click interactivity to explore potential modifications of graphics for data presentation, the application uses an intuitive interface to make R more accessible to those without programming experience. This project is in a state of continual development and will expand to cover introductory data science topics relevant to academics and professionals alike. The code for tidyTouch and this document can be found at https://github.com/devaneyJE/tidyTouch_thesis (see ui.R and server.R files for application code).
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MacIntyre, Thomas Gunn. "Using and applying international survey data on mathematics and science education." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/10542.

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There were two purposes set out in this study, first to identify the principal associations with educational performance of Scottish students as reported in the 2007 wave of the Trends in International Mathematics and Science Study (TIMSS2007), and second to evaluate methods of data analysis where sample surveys use plausible value (PV) methodology. Four sets of data were used for the secondary analysis of TIMSS2007, with student's responses to cognitive items and questionnaire data emanating from two stages (G$ and G*) that each addressed two disciplines (mathematics and science). Explanatory models for each stage and discipline were analysed using hierarchical linear modelling techniques to accommodate the cluster sample design of the survey. Guided by existing literature in STEM education the study examined elements of students' learning experiences that fell within a social constructivist theory of learning to ascertain whether the empirical data supported current claims on effective practice. A number of control variables were included in the analyses, some well-established constructs and others derived from background questionnaires. Overall, the results showed that selected background characteristics were consistently related to mathematics and science achievement. The strength of association with home resources, and although girls were generally associated with lower achievement scores, that gender association was strongest in G4 mathematics achievement. The findings suggest there is limited support for current claims in respect of a reform agenda that privileges discussion and collaborative group work. Other policy initiatives on assessment for learning and using technologies in class are not supported in the data, with either no evidence of association or a significant negative effect in the models of mathematics and science achievement. Aspects of practical work and scientific enquiry are positively associated with G4 science achievement, with particular credence given to 'doing' and 'watching' experiments or investigations, buy there is no association with achievement scores at G8 for any of planning, watching or conducting experiments. This latter finding provides empirical evidence of difference across stages on an aspect of practice that is heavily debated. The primary method of analysis utilised a four-level structure, with PV as the unit of analysis. Substantive findings were compared with alternative methods: first making the dependent variable an average of the five PVs; second using one PV as the response variable; and third computing statistics from all five PVs and merging results using Rubin's Rules for combining multilevel method underestimates standard errors in the model in the same way as witnessed for the average of PVs. This leads to the conclusion that the only valid route to analysing imputed data is through Rubin's method of combining results from all five PVs.
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Anderson, Amie K. "Use of admissions data to predict student success in postsecondary freshman science." Thesis, Capella University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3609412.

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The purpose of this study was to determine if significant relationships exist for any of the variables, age, gender, previous GPA, test scores (ACT, Compass), number of accumulated credits, and student success in Biology. This study strived to determine what academic/admissions data can be used to determine the likelihood of student success in Biology. A quantitative correlational study using stepwise multiple regression analysis was used for this study. The study was a retrospective study. Data was composed of a convenience archival sample from the institutional database. Multiple regression analysis was conducted to determine the effect each independent variable has on the dependent variable of student success. For the data set ACT, the variables math score, prealg score, writing score, reading score, and previous GPA were all significant. For data set CMP the variable of student's age was not significant, but the other variables were significant. For the Blanks data set, the only variable of significance was gender. Using stepwise multiple regression analysis the data sets produced regression models showing predictability based on stepwise significance. For Blanks data set, the variables previous hours earned, gender, age, and previous GPA were used. For the ACT data set, math score and reading score were used. For the CMP data set the variables included math score, writing score, previous GPA, gender, reading score, and previous hours earned. The level of predictability of the regression equation for the ACT data set and Blank data set was low. However, the predictability for the CMP data set was moderate. The highest percent of variance explained by the regression models was 11.6% of the CMP data set.

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Planteu, Lukas, Bernhard Standl, Wilfried Grossmann, and Erich Neuwirth. "Integrating school practice in Austrian teacher education." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6462/.

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We present a concept of better integration of practical teaching in student teacher education in Computer Science. As an introduction to the workshop different possible scenarios are discussed on the basis of examples. Afterwards workshop participants will have the opportunity to discuss the application of the aconcepts in other settings.
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Nylén, Aletta, and Christina Dörge. "Using competencies to structure scientific writing education." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6485/.

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Scientific writing is an important skill for computer science and computer engineering professionals. In this paper we present a writing concept across the curriculum program directed towards scientific writing. The program is built around a hierarchy of learning outcomes. The hierarchy is constructed through analyzing the learning outcomes in relation to competencies that are needed to fulfill them.
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Nadarajah, Kumaravel. "Computers in science teaching: a reality or dream; The role of computers in effective science education: a case of using a computer to teach colour mixing; Career oriented science education for the next millennium." Thesis, Rhodes University, 2000. http://hdl.handle.net/10962/d1003341.

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Science education in South Africa is not improving much. Many science educators do not have appropriate science qualifications. Majority of the learners have limited facilities to learn science. In this dilemma the move to OBE may result in further substantial deterioration of science education. A possible way out is to use computers in science education to facilitate the learning process. This study was designed to investigate how computers contribute to learners’ skills development in a physics course. A series of interactive computer simulations of colour mixing and a number of closely related traditional practical activities are aimed to promote learners’ understanding of colour. It was concluded that while computer environments have greater potentialas learning tools, they also limit interactions in significant ways.
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Keiler, Leslie Susan. "Factors affecting student data handling choices and behaviours in Key Stage 4 science." Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323549.

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Ng, Kevin (Kevin Y. ). "Design of a teacher education model that improves teacher educator efficiency in processing teacher candidate data." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119729.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 49-50).
Existing state of the art practice-based teacher education models either rely on heavy teacher educator time commitment to process teacher candidate performance stored in rich media like audio or video, or rely on teacher candidates to voluntarily share experiences with minimal teacher educator interaction with data. Using an iterative design process, I work with teacher educators to gauge interest in and build a new teacher education model that simplifies how teacher educators interact with rich media. The new model builds on Teacher Moments, an online simulator for preservice teachers, and takes advantage of state of the art speech recognition and data visualization technology to help teacher educators learn the contents of rich media generated by teacher candidates without dedicating the time to listen or watch media. In my investigation, I find that there is an interest in such a model and that the new model succeeds in empowering teacher educators with the ability to use teacher candidate data to inform instructional decisions and substantiate discussion point during group debrief sessions.
by Kevin Ng.
M. Eng.
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Robertson, Laura, Mahua Chakraborty, and Pamela J. Cromie. "Thinking Like a Scientist: Data Analysis in Middle and High School." Digital Commons @ East Tennessee State University, 2012. https://dc.etsu.edu/etsu-works/779.

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LaMar, Michelle Marie. "Models for understanding student thinking using data from complex computerized science tasks." Thesis, University of California, Berkeley, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3686374.

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The Next Generation Science Standards (NGSS Lead States, 2013) define performance targets which will require assessment tasks that can integrate discipline knowledge and cross-cutting ideas with the practices of science. Complex computerized tasks will likely play a large role in assessing these standards, but many questions remain about how best to make use of such tasks within a psychometric framework (National Research Council, 2014). This dissertation explores the use of a more extensive cognitive modeling approach, driven by the extra information contained in action data collected while students interact with complex computerized tasks. Three separate papers are included. In Chapter 2, a mixture IRT model is presented that simultaneously classifies student understanding of a task while measuring student ability within their class. The model is based on differentially scoring the subtask action data from a complex performance. Simulation studies show that both class membership and class-specific ability can be reasonably estimated given sufficient numbers of items and response alternatives. The model is then applied to empirical data from a food-web task, providing some evidence of feasibility and validity. Chapter 3 explores the potential of using a more complex cognitive model for assessment purposes. Borrowing from the cognitive science domain, student decisions within a strategic task are modeled with a Markov decision process. Psychometric properties of the model are explored and simulation studies report on parameter recovery within the context of a simple strategy game. In Chapter 4 the Markov decision process (MDP) measurement model is then applied to an educational game to explore the practical benefits and difficulties of using such a model with real world data. Estimates from the MDP model are found to correlate more strongly with posttest results than a partial-credit IRT model based on outcome data alone.

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Books on the topic "Data science for education"

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Estrellado, Ryan A., Emily A. Freer, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez. Data Science in Education Using R. Abingdon, Oxon; New York, NY: Routledge, 2021.: Routledge, 2020. http://dx.doi.org/10.4324/9780367822842.

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Casola, Linda, ed. Roundtable on Data Science Postsecondary Education. Washington, D.C.: National Academies Press, 2020. http://dx.doi.org/10.17226/25804.

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Liu, Wing Kam, Zhengtao Gan, and Mark Fleming. Mechanistic Data Science for STEM Education and Applications. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87832-0.

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Finson, Kevin D., and Jon E. Pedersen. Visual data and their use in science education. Charlotte, NC: Information Age Publishing, Inc., 2013.

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Using secondary data in educational and social research. Buckingham: Open University, 2008.

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ACM SIGCSE Technical Symposium on Computer Science Education (18th 1987 Saint Louis, Mo.). The papers of the Eighteenth SIGCSE Technical Symposium on Computer Science Education. New York: Association for Computing Machinery, 1987.

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Turrin, Margie. Earth science puzzles: Making meaning from data. Arlington, Va: NSTA Press, 2010.

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service), SpringerLink (Online, ed. Missing Data: Analysis and Design. New York, NY: Springer New York, 2012.

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Julie, Hallmark, and Seidman Ruth K, eds. Sci/tech librarianship: Education and training. New York: Haworth Press, 1998.

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J, Marzano Robert, ed. Enhancing the art & science of teaching with technology. Bloomington, IN: Marzano Research Laboratory, 2014.

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Book chapters on the topic "Data science for education"

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Cao, Longbing. "Data Science Education." In Data Science Thinking, 329–48. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95092-1_11.

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Magenheim, Johannes, and Carsten Schulte. "Data Science Education." In Encyclopedia of Education and Information Technologies, 493–514. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-10576-1_253.

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Magenheim, Johannes, and Carsten Schulte. "Data Science Education." In Encyclopedia of Education and Information Technologies, 1–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-60013-0_253-1.

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Datta, Meera S., and Vijay V. Mandke. "Data Science in Education." In Data Science and Its Applications, 127–50. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003102380-7.

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Stanton, Jeffrey, Carole L. Palmer, Catherine Blake, and Suzie Allard. "Interdisciplinary Data Science Education." In ACS Symposium Series, 97–113. Washington, DC: American Chemical Society, 2012. http://dx.doi.org/10.1021/bk-2012-1110.ch006.

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Estrellado, Ryan A., Emily A. Freer, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez. "Teaching data science." In Data Science in Education Using R, 241–49. Abingdon, Oxon; New York, NY: Routledge, 2021.: Routledge, 2020. http://dx.doi.org/10.4324/9780367822842-16.

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Hazzan, Orit, Noa Ragonis, and Tami Lapidot. "Data Science and Computer Science Education." In Guide to Teaching Computer Science, 95–117. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39360-1_6.

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Zimmerman, Timothy D. "Field-Based Data Collection." In Encyclopedia of Science Education, 1–2. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-6165-0_32-2.

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Zimmerman, Timothy D. "Field-Based Data Collection." In Encyclopedia of Science Education, 432–33. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-007-2150-0_32.

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Krueger, Alice B., Patrick D. French, and Thomas G. Carter. "Student Data Acquisition." In Internet Links for Science Education, 157–76. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5909-2_10.

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Conference papers on the topic "Data science for education"

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Mike, Koby. "Data Science Education." In ICER '20: International Computing Education Research Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3372782.3407110.

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Raj, Rajendra K., Allen Parrish, John Impagliazzo, Carol J. Romanowski, Sherif Aly Ahmed, Casey C. Bennett, Karen C. Davis, Andrew McGettrick, Teresa Susana Mendes Pereira, and Lovisa Sundin. "Data Science Education." In ITiCSE '19: Innovation and Technology in Computer Science Education. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3304221.3325533.

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Hassan, Ismail Bile, and Jigang Liu. "Embedding Data Science into Computer Science Education." In 2019 IEEE International Conference on Electro Information Technology (EIT). IEEE, 2019. http://dx.doi.org/10.1109/eit.2019.8833753.

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Finzer, William. "The data science education dilemma." In Technology in Statistics Education: Virtualities and Realities. International Association for Statistical Education, 2012. http://dx.doi.org/10.52041/srap.12105.

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The need for people fluent in working with data is growing rapidly and enormously, but U.S. K–12 education does not provide meaningful learning experiences designed to develop understanding of data science concepts or a fluency with data science skills. Data science is inherently inter- disciplinary, so it makes sense to integrate it with existing content areas, but difficulties abound. Consideration of the work involved in doing data science and the habits of mind that lie behind it leads to a way of thinking about integrating data science with mathematics and science. Examples drawn from current activity development in the Data Games project shed some light on what technology-based, data-driven might be like. The project’s ongoing research on learners’ conceptions of organizing data and the relevance to data science education is explained.
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Cuilan Qiao, Chenzhou Cui, Xiaoping Zheng, and Yan Xu. "Science data based astronomy education." In 2010 2nd International Conference on Education Technology and Computer (ICETC). IEEE, 2010. http://dx.doi.org/10.1109/icetc.2010.5529488.

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Fox, Geoffrey, Sidd Maini, Howard Rosenbaum, and David Wild. "Data Science and Online Education." In 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, 2015. http://dx.doi.org/10.1109/cloudcom.2015.82.

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Van Dusen, Eric, John DeNero, and Kseniya Usovich. "Innovation in Data Science Education." In SIGCSE 2022: The 53rd ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3478432.3499154.

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Underwood, William, David Weintrop, Michael Kurtz, and Richard Marciano. "Introducing Computational Thinking into Archival Science Education." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622511.

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Underwood, William, and Richard Marciano. "Computational Thinking in Archival Science Research and Education." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005682.

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Rao, A. Ravishankar, Yashvi Desai, and Kavita Mishra. "Data science education through education data: an end-to-end perspective." In 2019 IEEE Integrated STEM Education Conference (ISEC). IEEE, 2019. http://dx.doi.org/10.1109/isecon.2019.8881970.

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Reports on the topic "Data science for education"

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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.

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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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Benelli, Gabriele. Data Science and Machine Learning in Education. Office of Scientific and Technical Information (OSTI), July 2022. http://dx.doi.org/10.2172/1882567.

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Shapovalov, Yevhenii B., Viktor B. Shapovalov, and Vladimir I. Zaselskiy. TODOS as digital science-support environment to provide STEM-education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3250.

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The amount of scientific information has been growing exponentially. It became more complicated to process and systemize this amount of unstructured data. The approach to systematization of scientific information based on the ontological IT platform Transdisciplinary Ontological Dialogs of Object-Oriented Systems (TODOS) has many benefits. It has been proposed to select semantic characteristics of each work for their further introduction into the IT platform TODOS. An ontological graph with a ranking function for previous scientific research and for a system of selection of journals has been worked out. These systems provide high performance of information management of scientific information.
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Leu, Katherine. Data for Students: The Potential of Data and Analytics for Student Success. RTI Press, March 2020. http://dx.doi.org/10.3768/rtipress.2020.rb.0023.2003.

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Postsecondary education is awash in data. Postsecondary institutions track data on students’ demographics, academic performance, course-taking, and financial aid, and have put these data to use, applying data analytics and data science to issues in college completion. Meanwhile, an extensive amount of higher education data are being collected outside of institutions, opening possibilities for data linkages. Newer sources of postsecondary education data could provide an even richer view of student success and improve equity. To explore this potential, this brief describes existing applications of analytics to student success, presents a framework to structure understanding of postsecondary data topics, suggests potential extensions of these data to student success, and describes practical and ethical challenges.
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Soroko, Nataliia V., Lorena A. Mykhailenko, Olena G. Rokoman, and Vladimir I. Zaselskiy. Educational electronic platforms for STEAM-oriented learning environment at general education school. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3884.

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The article is devoted to the problem of the use of educational electronic platform for the organization of a STEAM-oriented environment of the general school. The purpose of the article is to analyze the use of educational electronic platforms for organizing the STEAM-oriented school learning environment and to identify the basic requirements for supporting the implementation and development of STEAM education in Ukraine. One of the main trends of education modernization is the STEAM education, which involves the integration between the natural sciences, the technological sciences, engineering, mathematics and art in the learning process of educational institutions, in particular, general school. The main components of electronic platform for education of the organization STEAM-oriented educational environment should be open e-learning and educational resources that include resources for students and resources for teachers; information and communication technologies that provide communication and collaboration among students; between teachers; between students and teachers; between specialists, employers, students, and teachers; information and communication technologies that promote the development of STEAM education and its implementation in the educational process of the school; online assessment and self-assessment of skills and competences in STEAM education and information and communication technologies fields; STEAM education labs that may include simulators, games, imitation models, etc.; STEAM-oriented educational environment profiles that reflect unconfirmed participants’ data, their contributions to projects and STEAM education, plans, ideas, personal forums, and more. Prospects for further research are the design of an educational electronic platform for the organization of the STEAM-oriented learning environment in accordance with the requirements specified in the paper.
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Wachen, John, Mark Johnson, Steven McGee, Faythe Brannon, and Dennis Brylow. Computer Science Teachers as Change Agents for Broadening Participation: Exploring Perceptions of Equity. The Learning Partnership, April 2021. http://dx.doi.org/10.51420/conf.2021.2.

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In this paper, the authors share findings from a qualitative analysis of computer science teachers’ perspectives about equity within the context of an equity-focused professional development program. Drawing upon a framework emphasizing educator belief systems in perpetuating inequities in computer science education and the importance of equity-focused teacher professional development, we explored how computer science teachers understand the issue of equity in the classroom. We analyzed survey data from a sample of participants in a computer science professional development program, which revealed that teachers have distinct ways of framing their perceptions of equity and also different perspectives about what types of strategies help to create equitable, inclusive classrooms reflective of student identity and voice.
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Oleksiuk, Vasyl P., and Olesia R. Oleksiuk. Exploring the potential of augmented reality for teaching school computer science. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4404.

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The article analyzes the phenomenon of augmented reality (AR) in education. AR is a new technology that complements the real world with the help of computer data. Such content is tied to specific locations or activities. Over the last few years, AR applications have become available on mobile devices. AR becomes available in the media (news, entertainment, sports). It is starting to enter other areas of life (such as e-commerce, travel, marketing). But education has the biggest impact on AR. Based on the analysis of scientific publications, the authors explored the possibilities of using augmented reality in education. They identified means of augmented reality for teaching computer science at school. Such programs and services allow students to observe the operation of computer systems when changing their parameters. Students can also modify computer hardware for augmented reality objects and visualize algorithms and data processes. The article describes the content of author training for practicing teachers. At this event, some applications for training in AR technology were considered. The possibilities of working with augmented reality objects in computer science training are singled out. It is shown that the use of augmented reality provides an opportunity to increase the realism of research; provides emotional and cognitive experience. This all contributes to engaging students in systematic learning; creates new opportunities for collaborative learning, develops new representations of real objects.
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Alpaydın, Yusuf. EDUCATION IN THE TURKEY OF THE FUTURE. İLKE İlim Kültür Eğitim Vakfı, December 2020. http://dx.doi.org/10.26414/gt008.

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The first report prepared under the Turkey of the Future project is on education, where our country has long been in a search for stability and methodology. The report aims to realistically study in 2018 what needs to be accomplished when looking forward to 2030 using quantitative and qualitative data. In this context, the study begins by explaining the state of education in the new millennium and the problems experienced from this perspective. The context necessary in resolving the issues and bettering current circumstances has been also emphasized in the purview of the report. Along with these improvements, students’ national and international examination performances are also analyzed. Finally, the developed policies, solution recommendations, and improvements have been presented in 12 points on the vision of the future. In preparing the report, the primary framework has been shaped by the relevant scientific literature, the framework and principal values established by the İLKE Foundation for Science, Culture and Education within the scope of the Turkey of the Future Project, and the educational perspectives of the research team. Besides multidisciplinarity and systems approach, locality and originality have been the two principal values when preparing this report.
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Mayfield, Colin. Higher Education in the Water Sector: A Global Overview. United Nations University Institute for Water, Environment and Health, May 2019. http://dx.doi.org/10.53328/guxy9244.

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Higher education related to water is a critical component of capacity development necessary to support countries’ progress towards Sustainable Development Goals (SDGs) overall, and towards the SDG6 water and sanitation goal in particular. Although the precise number is unknown, there are at least 28,000 higher education institutions in the world. The actual number is likely higher and constantly changing. Water education programmes are very diverse and complex and can include components of engineering, biology, chemistry, physics, hydrology, hydrogeology, ecology, geography, earth sciences, public health, sociology, law, and political sciences, to mention a few areas. In addition, various levels of qualifications are offered, ranging from certificate, diploma, baccalaureate, to the master’s and doctorate (or equivalent) levels. The percentage of universities offering programmes in ‘water’ ranges from 40% in the USA and Europe to 1% in subSaharan Africa. There are no specific data sets available for the extent or quality of teaching ‘water’ in universities. Consequently, insights on this have to be drawn or inferred from data sources on overall research and teaching excellence such as Scopus, the Shanghai Academic Ranking of World Universities, the Times Higher Education, the Ranking Web of Universities, the Our World in Data website and the UN Statistics Division data. Using a combination of measures of research excellence in water resources and related topics, and overall rankings of university teaching excellence, universities with representation in both categories were identified. Very few universities are represented in both categories. Countries that have at least three universities in the list of the top 50 include USA, Australia, China, UK, Netherlands and Canada. There are universities that have excellent reputations for both teaching excellence and for excellent and diverse research activities in water-related topics. They are mainly in the USA, Europe, Australia and China. Other universities scored well on research in water resources but did not in teaching excellence. The approach proposed in this report has potential to guide the development of comprehensive programmes in water. No specific comparative data on the quality of teaching in water-related topics has been identified. This report further shows the variety of pathways which most water education programmes are associated with or built in – through science, technology and engineering post-secondary and professional education systems. The multitude of possible institutions and pathways to acquire a qualification in water means that a better ‘roadmap’ is needed to chart the programmes. A global database with details on programme curricula, qualifications offered, duration, prerequisites, cost, transfer opportunities and other programme parameters would be ideal for this purpose, showing country-level, regional and global search capabilities. Cooperation between institutions in preparing or presenting water programmes is currently rather limited. Regional consortia of institutions may facilitate cooperation. A similar process could be used for technical and vocational education and training, although a more local approach would be better since conditions, regulations and technologies vary between relatively small areas. Finally, this report examines various factors affecting the future availability of water professionals. This includes the availability of suitable education and training programmes, choices that students make to pursue different areas of study, employment prospects, increasing gender equity, costs of education, and students’ and graduates’ mobility, especially between developing and developed countries. This report aims to inform and open a conversation with educators and administrators in higher education especially those engaged in water education or preparing to enter that field. It will also benefit students intending to enter the water resources field, professionals seeking an overview of educational activities for continuing education on water and government officials and politicians responsible for educational activities
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Barros, Margarida, Cristiana Bessa, Isabel Mesquita, and Paula Queirós. The Expression of Epistemological Beliefs in Initial Teacher Education: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0131.

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Review question / Objective: The purpose of this systematic review is to scrutinize what is known about pre-service teachers’ epistemological beliefs in initial teacher training. The research questions which guided the review of these studies were: (Q1) What is the theoretical framework used? (Q2) What is the domain present in the research? (Q3) What have been the main purposes of the research? (Q4) Which have been the methodological procedures used to access epistemological beliefs? (Q5) What are the main research findings? Information sources: Five databases will be used to search and retrieve the articles: EBSCO, ERIC, Web of Science and SCOPUS. This review will not exclude any work based on the date of conclusion as it intends to understand and illustrate the overview of all the research carried out on the epistemological beliefs of pre-service teachers. This will allow access to the explanatory factors of the contours and manifestations that the EB assume in this training phase.
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