Foundations of Data Science for Students in Grades K-12 Proceedings of a Workshop (2023) / Chapter Skim
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Appendix C: Submitted Cases
Appendix C: Submitted Cases
Pages 103-140
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Appendix C Submitted Cases Workshop registrants were invited to submit case examples on K–12 data science education for one of three topic areas: Examples of challenges educators face when working to enact data science activities; Examples that reflect successful real-world application of data science with youth; or Examples of an existing software/tool/technology that has been used and could be adopted to supplement data science learning. Cases were featured during the workshop.
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What are examples of successful partnerships to facilitate data science teaching and learning at the K–12 levels? Lack of Data Science Learning the fundamentals of how data are collected, analyzed, and Fundamentals communicated starts with a solid foundation in the science and engineering practices outlined in the Framework for Science Education.
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And, like most climate phenomena, they need a tool that allows them to view change over both space and time. Need for High-Quality, Regardless of how hard we work at designing clear, important, and meaningful Consistent Data Science district and state science and math standards and guidelines that incorporate Training for Practitioners data science skill development, what actually ends up happening in the classroom may be vastly different than our intentions.
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Some of these challenges included time restraints -- including Courses the excessive number of math standards they are expected to cover; scheduling restraints -- many teachers in Idaho teach in rural schools where there is often only one or two high school math teachers for the entire school; the infeasibility of adding in a full data science class; their own fear or lack of time to learn a new software/tool/technology in order to teach data science -- we are thinking as a state to offer a follow-up professional development course that is intentionally designed to help 6–12 educators get over this hurdle; and explicit connections between data science and mathematics content standards -- many math teachers want to know they are still teaching math when they are leading data science activities.
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As an answer to these problems, I often suggest to students that before using SPSS, first read the SPSS Manual to get an in-depth picture of the contents of the SPSS program, especially related to the use of statistical formulas in SPSS. Difficulties Finding Clean, With regard to teaching science, it is very challenging to find relatively clean, Multivariate Datasets multivariate datasets that allow for rich exploration of science topics that are directly relevant to what teachers are currently teaching.
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of teachers was due to increased expectations for students to learn statistics at the secondary level that needed to be matched by enhancement and prioritization of statistics teacher education. The ESTEEM project packages materials into e-modules.
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This raises the challenge of better understanding how data science education intersects with subject matter taught across the curriculum, the pedagogical content knowledge teachers from different disciplines draw upon as they integrate data investigations with existing curricula, and the resources teachers need to do this. Hesitation in Students Data science is so new that students do not have a fundamental understanding Surrounding Data Science of what it is, or even who uses it, which makes diving into the curriculum difficult.
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Topic areas include Earth science, biology, social studies, and civil engineering. In all cases, students work with "CLIP" data, which are complex, large, interactively accessed using a computer, and professionally collected.
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Integration of CODAP and SageModeler data windows into Canvas. Canvas would allow integrations of these tools into a single page, but website navigation takes up much of the screen real estate.
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HistoricalAwards=false These challenges arose because we were trying to fulfill two different learning goals: Introduce the basics of data science, and Embed science concepts dictated by the NGSS. NGSS suggests that students in ninth grade use mathematical and computational models in science, but simultaneously teaching science and data fluency is not straightforward because students may lack the skills they need to do this effectively.
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We conclude that when students produce data in an experiment for which they feel a sense of ownership, they can exhibit an almost proprietary interest in representations of their data. As they work to create alignments between their conceptions of their data and the unexpected data patterns they see in the graphs, they begin to use their graphs as epistemic tools.
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CourseKata CourseKata Statistics and Data Science, an innovative interactive online https://coursekata.org textbook for teaching introductory statistics and data science in colleges, universities, and high schools. Part of CourseKata's Better Book Project is to leverage research and student data to guide continuous improvement of online learning resources.
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Science education researchers at the Massachusetts Institute of Technology (MIT) and Carleton College measured students' experience using Pivot Interactives interactive video compared to traditional methods: 85% reported that interactive video made it easier to understand the scenario being investigated, 92% said they'd encourage their friends to take courses that use interactive video, 65% wished MIT's online physics course included more of our interactive video, and 60% reported an increase in using scientific skills like measurement techniques when using our interactive video.
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Invitations to Inquiry Invitations to Inquiry are short instructional activities designed to help middle https://bscs.org/resources/ and high school students work with large data sets hosted on FieldScope. invitations-to-inquiry/ Teachers and students use the interactive FieldScope platform to collect, visualize, and analyze environmental data.
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Functionally, students can produce a wide variety of data visualizations, and get support with graph choice, applying inferential statistics, and learning the math behind statistical tests with animation. The DataClassroom U version of the tool contains a Bridge to R which generates well annotated R code for any of the graphs or statistical tests called through the easy to use interface.
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Schoolytics transforms Google Classroom assignment data into meaningful key performance indicators and time series trends for users to reflect on and respond to. By using their Schoolytics dashboard, students get exposure to simple statistics and graphs based on their own data, which takes on a personal and real quality that more theoretical discussions on data science struggle to match.
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In this way, Coding Like a Data Miner is couched in real work applications suited to reflect the nature of the increasingly digital world around us! Ohio Data Science This year I am involved in the Ohio Data Science Foundations Course Pilot: Information about course Foundations Course Pilot using the Data Science Curriculum (UCLA: Introduction to Data Science)
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The course does two things: Students work together in small teams on an end-to-end data science project, which covers the Big Idea of a data science pipeline. Students select a project from an open-source repository of municipally generated data.
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We provide C43B789C2136F62C030FAE3E professional development and coaching to teachers to help them develop the 7A8EB58C45EF823FACF skills they need to successfully teach this course. We are creating data science projects with Florida themes in agriculture, health care, science, marine health, manufacturing, finance, and other career related areas working with industry partners for data sets.
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Jupyter Notebooks Educationally designed Jupyter Notebooks based on Python make complex Fleischer, Y., Biehler, R., and machine learning algorithms accessible and transparent to students. Jupyter Schulte, C
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These datasets on hood-three-new-codap-plugins/ their own are much too large and complicated for the middle school audience the project targets. Choosy, a simple data tool designed to address this issue, could be adopted broadly to enhance data science curricula and approaches.
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Signification Plugin for Students often have issues facing complex datasets. However, a significant CODAP fraction of learners are blind or visually impaired, rendering traditional visual data analysis tools practically inaccessible.
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Users can assign different attributes of a dataset to different aspects of the audio output to allow for examination of multiple attributes simultaneously, modify the speed and repetition rate of the looping to enable closer study of patterns, and easily compare portions of a dataset with each other to make differences more readily apparent. Story Builder Plugin for Inspired by the NSF-funded Writing Data Stories project's goal of helping Information about Story Builder CODAP middle school students become "data storytellers," the Story Builder plugin plugin: https://concord.org/ allows students to build story "moments," with each interactive moment newsletter/2021-fall/under-the capturing the state of a CODAP document at a given time.
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discussions, in the spirit of "number talks," to critically pWvjVDd8RlgH0UQ7N4DUVzc analyze and interpret data visualizations in ways that connect to students' lives eWw44/edit and important issues in society. Teachers can select from a series of questions designed to guide students through the following: Making sense of trends and relationships in the data or visualization, what these patterns mean, and how they connect to key science concepts.
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Additionally, we have a self-paced free course for teachers to become familiar with the project, the lessons, and FieldScope. We also provide some program level resources for helping students with data (e.g., using data in discussions, introducing variables, choosing data representations)
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1,886 bone density scans and age data of several hundred patients from three
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The researchers then converted BMD to a BMD percent relative to the 66–70 year age group. Indirect Estimate of Potato Students use data analysis to complete an activity based on the following Link to the full activity: https:// Cell Cytoplasm Molarity background (full activity included in link)
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Thus, 789C2136F62C030FAE3E7A8EB Science Learning Activity students who engage in such educational data mining (picking distance from the 58C45EF823FACF Google map) tend to focus on using Google Maps as a tool for discovering data.
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This design commitment came from observations and analyses of interactions between residents and urban planners that were talking, and making decisions about the future built environment, using complex spatial data visualizations. National Science Teaching There is a NSTA Daily Do Playlist of three lessons forming an instructional https://www.nsta.org/resources/ Association (NSTA)
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Hub for Innovation and For many years, the projects within the Hub for Innovation and Research in Video 1: Students Working on the Research in Statistics Statistics Education [HI-RiSE] at NC State have worked in classrooms with Roller Coaster Investigation Education (HI-RiSE)
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We also found signs of greater social and political awareness and agency with data -- outcomes associated with increased critical data literacy. In this module, students work through seven lessons and a final team data investigation, exploring different forms of income inequality, their scope, and possible explanations.
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Children sort the data cards, identify reasonable Using data cards for teaching split criteria, and experience the different rates of false classifications. The data based decision trees in dependence of the decision rule on the somewhat subjective labeling and the middle school.
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introduces middle school students to data science in the context of "using data com/social-impact/ for social good." Data Explorers focuses on the 17 United Nations Sustainable data-science-education/ Development Goals (SDGs) , which present a "blueprint to achieve a better and more sustainable future for all," and introduces participants to the data the United Nations has collected to track progress in the SDGs.
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program at the University of Puerto Rico. luquillo/education-outreach/k-12 After a series of introductory activities, students work in teams to come up with education-luquillo/ a research question, analyze the data using CODAP, and use the evidence from their analyses to answer their research question.
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Financial Literacy: Students decide on a retirement age (my students used age 65) to calculate their https://themint.org/~theminto/kids/ Retirement Calculations age until retirement.
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TABLE C-1 Continued 138 Case Type: Real-World Application Case Title Description Relevant Links/Citations DataJam Pittsburgh DataWorks, along with the West Big Data Innovation Hub and the https://www.pghdataworks.org/ New York Hall of Science, developed a virtualized version of the DataJam, an data-jam annual program and competition for high school students that runs throughout the academic year and introduces youth to data visualization and analytic skills to answer a research question of their own design. DataJam mentors -- volunteer university undergraduate students trained in mentorship, statistics, data ethics, and community-based research principles -- guide students.
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Students process poetry to identify and characterize features of its language such as the number and type of syllables in each line, then use patterns uncovered in the data to develop and train a machine learning model in the StoryQ app, a plugin to CODAP. The goal of the curriculum is to scaffold text analytics for English Language Arts students, so they learn to understand and appreciate both poetry and applications of data and AI.
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The students working on this project ALL became STEM majors at universities across the United States, and the teacher was contracted by Microsoft as a big data analyst. She wrote FarmBeats for Students machine learning and AI curricula for Future Farmers of America (FFA)
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