Reflecting on Graphs: Attributes of Graph Choice and Construction Practices in Biology

Angra, Aakanksha; Gardner, Stephanie M.

doi:10.1187/cbe.16-08-0245

Cited by 31 publications

(50 citation statements)

References 62 publications

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“…Yet, despite its importance and ubiquity, the use of authentic data in the classroom is limited by the availability of high-quality classroom resources, appropriate instructor training, and research to determine effective teaching strategies for developing student data literacy (Picone et al , 2007; Metz, 2008; Gould et al , 2014; Schultheis and Kjelvik, 2015; Harsh and Schmitt-Harsh, 2016; Angra and Gardner, 2017). We are currently at the beginning of an exciting shift in science education, creating targeted areas for collaboration for science educators, curriculum developers, programmers, and education researchers.…”

Section: Discussionmentioning

confidence: 99%

Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student Data Literacy

Kjelvik

Schultheis

2019

LSE

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Data are becoming increasingly important in science and society, and thus data literacy is a vital asset to students as they prepare for careers in and outside science, technology, engineering, and mathematics and go on to lead productive lives. In this paper, we discuss why the strongest learning experiences surrounding data literacy may arise when students are given opportunities to work with authentic data from scientific research. First, we explore the overlap between the fields of quantitative reasoning, data science, and data literacy, specifically focusing on how data literacy results from practicing quantitative reasoning and data science in the context of authentic data. Next, we identify and describe features that influence the complexity of authentic data sets (selection, curation, scope, size, and messiness) and implications for data-literacy instruction. Finally, we discuss areas for future research with the aim of identifying the impact that authentic data may have on student learning. These include defining desired learning outcomes surrounding data use in the classroom and identification of teaching best practices when using data in the classroom to develop students’ data-literacy abilities.

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Section: Discussionmentioning

confidence: 99%

Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student Data Literacy

Kjelvik

Schultheis

2019

LSE

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“…Although student data literacy is currently low (Steen, 1999;National Council on Education and the Disciplines, 2001;Wilkins, 2010;Manyika et al, 2011;Oceans of Data Institute, 2014), it can be improved by giving students opportunities to interact with authentic data (Duschl, 1990;Gould et al, 2014;Kastens et al, 2015). This value has been recognized by educators and curriculum reform efforts, which comes at a perfect time to tap into the resources made available through freely available data sets and educational resources (Picone et al, 2007; Metz, 2008; Gould et al, 2014; Schultheis & Kjelvik, 2015;Harsh & Schmitt-Harsh, 2016;Angra & Gardner, 2017).…”

Section: Resultsmentioning

confidence: 99%

Using Messy, Authentic Data to Promote Data Literacy & Reveal the Nature of Science

Schultheis

Kjelvik

2020

The American Biology Teacher

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Authentic, “messy data” contain variability that comes from many sources, such as natural variation in nature, chance occurrences during research, and human error. It is this messiness that both deters potential users of authentic data and gives data the power to create unique learning opportunities that reveal the nature of science itself. While the value of bringing contemporary research and messy data into the classroom is recognized, implementation can seem overwhelming. We discuss the importance of frequent interactions with messy data throughout K–16 science education as a mechanism for students to engage in the practices of science, such as visualizing, analyzing, and interpreting data. Next, we describe strategies to help facilitate the use of messy data in the classroom while building complexity over time. Finally, we outline one potential sequence of activities, with specific examples, to highlight how various activity types can be used to scaffold students' interactions with messy data.

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“…(Goldstein and Flynn 2011) Other authors detail the use of inquiry-based learning about statistics in their biology labs (Metz 2008;Thompson et al 2011), illustrating specific examples used in their classrooms. The interpretation of graphs was also of interest, specifically how students and faculty draw conclusions from visual representations of data (Angra and Gardner 2017). A recurring recommendation was to use graphs throughout the entire biology curriculum (Taylor 2010;Angra and Gardner 2017).…”

Section: Statistics Education In Stemmentioning

confidence: 99%

“…The interpretation of graphs was also of interest, specifically how students and faculty draw conclusions from visual representations of data (Angra and Gardner 2017). A recurring recommendation was to use graphs throughout the entire biology curriculum (Taylor 2010;Angra and Gardner 2017). A single introductory course was not the only avenue explored for introducing statistics: Colon-Berlingeri and Burrowes (2011) described integrating statistics content into genetics and zoology courses, and Remsburg, Harris, and Batzli (2014) taught statistical content in labs through a two-semester biology sequence.…”

Section: Statistics Education In Stemmentioning

confidence: 99%

The State of Statistics Education Research in Client Disciplines: Themes and Trends Across the University

Schwab-McCoy

2019

Journal of Statistics Education

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The reform movement in statistics education has led to a revitalization of the undergraduate introductory statistics course. However, many students satisfy their degree requirements by taking statistics courses in "client departments" such as business, the social sciences, and the lab sciences, typically taught by nonstatisticians. This article presents the findings of a metasynthesis of the existing literature on teaching statistics in these client disciplines to learn (1) what is currently being taught and how, and (2) the most important challenges for statistics teachers in other departments. Articles were reviewed using qualitative axial coding and quantitative text analysis to identify common research themes and ideas in the literature for each discipline. Research themes, attitudes toward statistics instruction, and pedagogical techniques were found to vary from discipline to discipline. Collaboration with instructors in other disciplines may be a welcome step toward improving statistics instruction across the university.

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Reflecting on Graphs: Attributes of Graph Choice and Construction Practices in Biology

Cited by 31 publications

References 62 publications

Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student Data Literacy

Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student Data Literacy

Using Messy, Authentic Data to Promote Data Literacy & Reveal the Nature of Science

The State of Statistics Education Research in Client Disciplines: Themes and Trends Across the University

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