Название: Statistics and Data Science for TeachersАвтор: Anna Bargagliotti, Christine Franklin
Издательство: American Statistical Association
Год: 2021
Страниц: 248
Язык: английский
Формат: pdf
Размер: 34.9 MB
A main goal of Statistics and Data Science for Teachers is to provide teacher educators with a resource to guide entire courses and professional development, or portions of courses and professional development when preparing teachers of all school level grade levels to teach the foundations of statistics and data science in their classrooms. In supporting the spirit of Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): A Framework for Statistics and Data Science Education, this book presents statistical ideas through investigations and engagement with the statistical problem-solving process of formulating statistical investigative questions, collecting/considering data, analyzing data, and interpreting results.
The book is organized into three sections:
• “Statistics as a Problem-Solving Process”
• “Toward Data Science”
• “Probability Unpacked”
Within each of these sections, standard topics of descriptive statistics, associations and relationships, distributions, probability, and sampling distributions are all developed. In addition, the “Toward Data Science” section illustrates how the principles of data science can be delivered in K–12 throughout all of the grade bands.
Now more than ever, data are part of our daily lives. We collect data, we are inundated with data, we are shown data in myriad displays, and we are asked to interpret data to help us make decisions and form opinions every single day. Students are exposed to data in the form of text messages, pictures, sounds, and tweets through social media outlets and technological devices. There are also large amounts of data being collected daily and automatically, based on our behaviors. For example, data collected through an exercise-tracking device or purchase-history data from Amazon document daily routines in our life. Data may be produced by social networking (such as Twitter, Facebook, or LinkedIn) or gaming devices and smartphones, or streamed from satellites used to understand climate change. All of these examples of data fall under the general heading of “Big Data.” The term originally referred to data sets of great size that had volume, variety, velocity, and veracity; however, over time, it has expanded to include data that merely have characteristics that can potentially lead to great size. Big Data may include images, locations, and dates. These data are rich and worthy of analysis. We will refer to all of these types of data as “nontraditional data.”
The focus of this unit is to show how nontraditional types of data can be collected, accessed, and analyzed in the elementary-, middle-, and high-school levels. If our goal as educators is to have students graduate high school statistically literate, we must incorporate curricula that address how to manage and analyze nontraditional data.
The investigations become more sophisticated and complex, working toward data science. Data Science is a relatively new term coined to describe statistics in the context of unconventional data. Data Science requires thinking about multiple variables at a time (multivariate thinking), asking questions to help sift through larger and more complex data sets, using technology to help wrangle and manipulate data, and understanding appropriate conclusions and limitations to the data. The next several investigations are meant to be used at the high-school level; thus, they rely heavily on technology and students’ ability to reason statistically.
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