Students write a program (using a programing language) to manage and curate data by finding, managing, analyzing data, or extracting information from the data.Īssessment of students’ work with statistical computing tools has been largely limited to whether students complete tasks with the tools and the responses they give with the output they have produced.
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Students use current statistical software or statistical packages that are appropriate to the discipline and context beyond basic Excel or a calculator to manage data, extract information from data, and carry out statistical analyses with data. From their research, they suggested two learning outcomes specifically related to statistical computing tools (p. ( 2020) have pointed out, employing technology tools is essential in undergraduate experiences in data science and statistics. Identify statistical computing actions that help lead students toward higher levels of computational sophistication.Īs Bargagliotti et al. Use data from task-based interviews to determine if, how, and when students are using the actions defined in the framework when solving statistical problems. Present a framework that can be used to assess students’ actions while interacting with statistical computing tools. There are three major goals of this article: The study described herein was conducted to test a framework created from both statistics and computing literature for understanding and assessing the actions students take while using statistical computing tools to help solve statistical problems.
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However, the research on how to assess students’ thinking and interaction with these tools is not as evolved as the research that has been conducted on students’ thinking with statistical learning tools (e.g., TinkerPlots, Fathom, CODAP, StatCrunch). In fact, a recent study suggests that utilizing statistical software packages (beyond Excel and calculators) for doing work with data and statistics is a critical learning outcome across undergraduate disciplines to develop students’ data acumen (Bargagliotti et al. 2014 Horton, Baumer, and Wickham 2014 Pruim, Kaplan, and Horton 2014). With the advancement of data science education, many instructors and field experts have designed successful curricula that utilize tools meant for doing statistics such as R, Julia, and Python (e.g., Nolan and Temple Lang 2010 Chance et al. Extensive research has been conducted on designing curricula to teach students when interacting with tools meant for learning statistics, and how to assess students’ thinking and interaction with these tools (Ben-Zvi 2006 delMas, Garfield, and Zieffler 2014 Lee et al. R, Julia, and Python are examples of tools meant for doing statistics (McNamara 2016). Software such as TinkerPlots, Fathom, CODAP, and StatCrunch are examples of those meant for learning statistics.
Moore ( 1997) defined two groups of tools that are used in statistics: those that are meant to help learn statistics and those that are meant for doing statistics.