Foundations of Data Science for Students in Grades K-12 Proceedings of a Workshop (2023) / Chapter Skim
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4 Supporting Implementation: Tools, Resources, and Teacher Preparation
4 Supporting Implementation: Tools, Resources, and Teacher Preparation
Pages 51-66
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From page 51... ...
However, there are also offline tools that can be a useful entry point into data science; Erickson asked panelists to describe their experience with unplugged data science learning. Rolf Biehler (Paderborn University, Germany)
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From page 52... ...
to digital operations, in particular in the software TinkerPlots.1 One use for these cards is to introduce students to decision trees as a machine learning method; this topic is part of the required curriculum in fifth and sixth grade in some states in Germany. Biehler described how the cards are used.
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V Lee said that unplugged data science learning can be done even without specialized materials like data cards.
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As students advance, educators open cells to show the code and use it to introduce students to simple coding for graphs. CODAP Our computer science education always begins with CODAP, said Biehler, which can be used to introduce data exploration in a simple way.
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The other important component of early data science education, he said, is an "intentional little bit of delight" that entices students to start exploring, to find insights, and to realize that there are multiple insights to be found in data. Rubin said that progression in data science tools is a research area that is understudied; important questions that need to be answered include the following: • What does the progression of tools look like?
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"It behooves us to explore a whole range of ways of data expression using sound, texture, human body," and other ways to explore data that are not solely visual. Unique approaches like the Dance Data project shared by DesPortes (Chapter 3)
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Finding datasets that work for teachers is hard, he said, and is an area that is ripe for progress. Teachers need access to "abundant and continually replenishing datasets that are good for pedagogical purposes." Wraparound Needs Erickson asked Kochevar to discuss the need for wraparound resources that help teachers and students use data science tools.
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Selection of Tools Hollylynne Lee (North Carolina State University) asked panelists about selecting tools for various data science education purposes.
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For example, when people gather information from news sources, they are selecting data sources, deciding what sources to trust, building arguments based on curated data, and making decisions. She asked panelists if there are tools that can help students develop these types of data literacy skills, or if there are innovations being made in this area.
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These opportunities include using tools other than spreadsheets, expanding beyond visualizing data and toward modeling data, and using data as a bridge between math and science classes. On the last point, Rosenberg said that while it takes time and effort to co-develop materials that are both mathematically and scientifically meaningful, data science is an effective way to serve the learning objectives of both math and science.
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Due to this challenge, Leftwich agreed with Bargagliotti that teacher preparation must be relevant to teachers' content area and grade level and must show examples that they can achieve. For example, teacher preparation for elementary teachers can emphasize how data science can help young students process the world around them and lead to a stronger understanding of math and problem-solving practices, whereas teacher preparation for physical education teachers can emphasize how data science can help students improve their understanding of their own health.
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First, teacher educators -- including content course teachers, methods course teachers, and mentor teachers -- need professional development. Second, preservice teachers need meaningful clinical experiences involving the teaching of data science.
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Lee asked panelists to describe the existing research on teacher preparation for data science education and to identify areas where there are research gaps. One thing we know about teacher learning, said Leftwich, is that teachers want to find ways to make an impact on their students and their learning.
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Rosenberg agreed that clarity is needed on what data science is; for example, teachers may want to do more than data visualization but aren't sure what the next step is. Leftwich said that in order to move data science forward, there is a need for more readily accessible and free software that allows students and teachers to use and manipulate data easily.
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Shifting Course Requirements Given the discussion earlier in the session about how requirements for teacher education largely drive the curriculum, Melville asked panelists if and how these requirements might be changed to require courses in data science education. "Is that something worth making a noise about," she asked, or is it best to continue on the path to infusing it into existing courses?
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From page 66... ...
Perez noted that there is a critical teacher shortage right now, and while this is an unfortunate situation, it opens up potential opportunities. She explained that nontraditional routes toward licensure have more flexibility than traditional teacher preparation programs and requirements, and there may be opportunities to infuse data science in these spaces.
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