There have been calls for increasing “statistical literacy” for more than a century. And with the rise of big data and fast computing, such cries have grown even louder in recent years.
The reality is that, with an unacceptably high number of students failing algebra courses, students focus on outdated methods and manual calculations. This archaic curriculum has helped many students to survive in the STEM field and to overcome the inequality that prevails in the United States.
Teaching math requires a different approach – one that develops data literacy for all students. Only such an approach would be more relevant and increase student participation, with the potential to reduce the widespread insecurity of misleading information shared through social media.
Research has shown that students They are not prepared as serious customers Data and online resources, which have raised concerns about the threat to democracy, depend on the ability of voters to sort the truth from false. The emerging field of data science, on the other hand, which is defined as a synthesis of statistics, mathematics, and computer science, promises to provide students with powerful problem-solving strategies they will use in the workplace and in their daily lives. And the need for people who can reason with data in almost all jobs in all sectors of the economy.
But there is one long-missing piece: the lack of standards for data science. This situation persists despite the fact that schools and districts across the United States recognize the need for data literacy; That some state frameworks demand attention to data literacy (such as 2021) California Mathematics Framework); And subject area teachers develop their own data lessons and curricula.
Although data science is interdisciplinary, a potential home for data science standards lies in mathematical standards, as there are important mathematical tools and methods that support data science. The second possibility is a different set of standards that is different than mathematics – increasing the likelihood of students developing a truly interdisciplinary approach to developing data skills. In any case, it seems appropriate to put the flag on the ground and give ideas for the development of data literacy and data science by grade. Such standards can be created by students going through secondary school and high school, and can be supplemented and deepened by high school data science courses, which some states and colleges Now accept Algebra 2 as an option.
At the high school level, teaching the synthesis of math, statistics, and computer thinking that makes up Data Science can not only lead students to important and well-paying careers, but it can eliminate the inequalities created in the calculus way. In most districts in the U.S., high-achieving students are known as the “race of calculus,” missing courses in middle school to reach the calculus peak. Yet research shows that most students who take calculus in school Repeat it or take a lower level course in college.
The need to narrow the curriculum to reach the calculus means that most students are filtered out of the way to high school, and the students chosen to move on are white and male. Data Science provides a more equitable alternative to calculus that does not require middle school tracking, and will be linked to students’ daily lives and communities, raising awareness of social-justice issues and Appeal to wider groups of students.
This is not going to be a low level path, as Data Science is a rigorous discipline that is rich and important in STEM subjects and in the humanities, for various college heads. The National Academy of Education has recently called for high school courses that engage students in civic reasoning – focusing on precise mathematical content and methods in the currently available data science courses. One example is the Mobilize Introduction to Data Science course, which was jointly developed by UCLA and the Los Angeles Unified School District and Stanford’s Ukubed: Explorations in Data Science.
In This related publication We produce a set of standards from the American Statistical Association’s Preke-12 Guidelines for assessment and suggestions in statistical education. An important quality of the standards is that, in each grade, they are included in the data investigation cycle. Data science should not be taught as a set of disconnected methods but rather as an approach to problem solving with data, to shed light on mathematical content and methods. As students move forward, they will actively participate in the investigation cycle of solving these problems with increasing levels of sophistication. Although there is a list of important knowledge at each grade level, knowledge is integrated and developed as a coherent whole. The data cycle we envision looks like this:
Our goal in setting these standards is not to claim that grades are the only way to develop data literacy, but to raise awareness and start or enrich conversations across the United States Some of the salient features of the proposed data science standards include students Which can be considered with data, learning to pose their own statistical exploratory questions on topics of interest and affecting them and coping with the ethical consequences of data collection and analysis.
Establishing standards is only the first step. There is a lot of work to be done in preparing teachers to teach data science, setting expectations from parents and securing the necessary resources. Institutions across the country are working together to spread awareness about the need for data science in schools (see for example, Confused data alignment And youcubed’s Data Science Resources)), and online courses are being conducted to build the knowledge and teaching pedagogy teachers need (see your own for example) YouCubed program. In addition, the American Statistical Association has a Extensive repository of education resources.
But standards are an important part of the puzzle, and we hope that the next task and thought will be unlocked – expanding the field of content that is currently in its infancy, but strong data can be the most important in the preparation of literate citizens. Navigating and understanding their data-filled world.