Data Science for Undergraduates by unknow

Author:unknow
Language: eng
Format: epub
Tags: Computers and Information Technology: Information Technology, Math, Chemistry, and Physics: Math and Statistics, Education: Higher Education, Education: Math and Science Education, Surveys and Statistics: Surveys and Statistics
Publisher: National Academies Press
Published: 2018-05-01T20:00:00+00:00

Recommendation 3.1: Four-year and two-year institutions should establish a forum for dialogue across institutions on all aspects of data science education, training, and workforce development.

MIDDLE AND HIGH SCHOOL EDUCATION

There is considerable potential to infuse data science education into middle school and high school curricula, particularly in laying the groundwork for many of the aspects of data acumen discussed in the previous chapter (Finzer, 2013). There is an opportunity for college-level courses to drive data science content down into middle and high school curricula. For example, the curriculum from Jevin West’s and Carl Bergstrom’s course at the University of Washington, Calling B.S.: Data Reasoning in a Digital World, is being adapted by and adopted in high school classrooms across the country (UW, 2017). High school teachers have found the curriculum to provide an innovative method to teach students how to analyze information responsibly. Having such experiences at the high school level better prepares students both for postsecondary curricula and for the data-driven workforce that awaits them. For example, the New York Hall of Science62 provides an opportunity for children, young adults, and their families to increase their understanding of data science through various interactive museum exhibits, data fests, and mobile city science programs. Such programs appeal to many students and community members as they offer an engaging alternative to traditional classroom learning.

However, infusing data science into the middle and high school curriculum is not a simple task, especially with changes in mathematics education as a result of the Common Core State Standards.63 Although, as of early 2018, 42 states and the District of Columbia have adopted their academic standards to be aligned with the Common Core (for some states with modifications), there are still some states that have not. This means that there is still variability across states in terms of the progression and coherence of learning opportunities that students are presented with for mathematics instruction. Teachers, particularly middle school educators, have expressed feeling overburdened with the standards as the development of curricular materials has lagged and guidance is still needed with respect to classroom implementation (Bay-Williams, Duffett, and Griffith, 2016). Whereas this could be viewed as a complication for a simple infusion of data science into at least the middle school classroom, it could also be presented as an opportunity given the need to generate materials instead of overhauling existing ones.

Course sequencing issues that are prevalent at the middle school level also exist at the high school level, although such issues can be heightened owing to limitations in the organizational structure of high school mathematics curricula. Specifically, some courses become “gatekeepers” to more rigorous mathematics courses, and students who are “tracked” through a particular sequence of courses may not be presented with the opportunity to develop strong postsecondary mathematical skills (Gamoran, 2009; Lucas, 1999; Lucas and Berends, 2002; Oakes, 2005). These issues can be compounded by issues of equity and access (Cha, 2015; Dondero and Muller, 2012; Lleras, 2008), which could have implications for students’ access to any data science instruction in middle and high school.

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