Academic literature on the topic 'Data Science Education'
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Journal articles on the topic "Data Science Education"
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.
Full textLogan, 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.
Full textDE 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.
Full textCao, 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.
Full textCooper, M. M. "Data-Driven Education Research." Science 317, no. 5842 (August 31, 2007): 1171. http://dx.doi.org/10.1126/science.317.5842.1171.
Full textGürsakal, 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.
Full textDill-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.
Full textHIGUCHI, 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.
Full textVolkova, 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.
Full textBIEHLER, 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.
Full textDissertations / Theses on the topic "Data Science Education"
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.
Full textMacIntyre, Thomas Gunn. "Using and applying international survey data on mathematics and science education." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/10542.
Full textAnderson, 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.
Full textThe 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.
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/.
Full textNylén, Aletta, and Christina Dörge. "Using competencies to structure scientific writing education." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6485/.
Full textNadarajah, 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.
Full textKeiler, 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.
Full textNg, 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.
Full textThis 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.
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.
Full textLaMar, 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.
Full textThe 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.
Books on the topic "Data Science Education"
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.
Full textCasola, Linda, ed. Roundtable on Data Science Postsecondary Education. Washington, D.C.: National Academies Press, 2020. http://dx.doi.org/10.17226/25804.
Full textLiu, 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.
Full textFinson, Kevin D., and Jon E. Pedersen. Visual data and their use in science education. Charlotte, NC: Information Age Publishing, Inc., 2013.
Find full textUsing secondary data in educational and social research. Buckingham: Open University, 2008.
Find full textACM 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.
Find full textTurrin, Margie. Earth science puzzles: Making meaning from data. Arlington, Va: NSTA Press, 2010.
Find full textservice), SpringerLink (Online, ed. Missing Data: Analysis and Design. New York, NY: Springer New York, 2012.
Find full textJulie, Hallmark, and Seidman Ruth K, eds. Sci/tech librarianship: Education and training. New York: Haworth Press, 1998.
Find full textJ, Marzano Robert, ed. Enhancing the art & science of teaching with technology. Bloomington, IN: Marzano Research Laboratory, 2014.
Find full textBook chapters on the topic "Data Science Education"
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.
Full textMagenheim, 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.
Full textMagenheim, 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.
Full textDatta, 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.
Full textStanton, 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.
Full textEstrellado, 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.
Full textHazzan, 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.
Full textZimmerman, 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.
Full textZimmerman, 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.
Full textKrueger, 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.
Full textConference papers on the topic "Data Science Education"
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.
Full textRaj, 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.
Full textHassan, 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.
Full textFinzer, 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.
Full textCuilan 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.
Full textFox, 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.
Full textVan 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.
Full textUnderwood, 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.
Full textUnderwood, 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.
Full textRao, 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.
Full textReports on the topic "Data Science Education"
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.
Full textBenelli, Gabriele. Data Science and Machine Learning in Education. Office of Scientific and Technical Information (OSTI), July 2022. http://dx.doi.org/10.2172/1882567.
Full textShapovalov, 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.
Full textLeu, 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.
Full textWachen, 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.
Full textOleksiuk, 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.
Full textAlpaydın, Yusuf. EDUCATION IN THE TURKEY OF THE FUTURE. İLKE İlim Kültür Eğitim Vakfı, December 2020. http://dx.doi.org/10.26414/gt008.
Full textMayfield, 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.
Full textDempsey, Terri L. Handling the Qualitative Side of Mixed Methods Research: A Multisite, Team-Based High School Education Evaluation Study. RTI Press, September 2018. http://dx.doi.org/10.3768/rtipress.2018.mr.0039.1809.
Full textThomson, Sue, Nicole Wernert, Sima Rodrigues, and Elizabeth O'Grady. TIMSS 2019 Australia. Volume I: Student performance. Australian Council for Educational Research, December 2020. http://dx.doi.org/10.37517/978-1-74286-614-7.
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