How Can the Progressive Education Pedagogy Make Math and AI Education More Efficient for Atmospheric Sciences?

The existing mathematical education system for atmospheric sciences has a 30% or higher DFW rate of Calculus I and II and has blocked many otherwise capable students from entering atmospheric sciences and related fields. Here, DFW refers to a grade abbreviation in education: D (unsatisfactory), F (failing), and W (withdraw from the course or program). This means that Calculus I and II break the dream of one out of two freshmen who wish to major in atmospheric or oceanic sciences. Ironically, the Calculus I and II materials have little relevance to the advanced courses of the majors and the students’ careers, while the most relevant math materials like vectors, matrices, computer graphics, decision-making, partial derivatives, and line integrals are in Calculus III, Data Science, Linear Algebra, Statistics, and Computer Coding. This obsolete filtering education system is biased against underrepresented minorities and females. This talk will focus on three questions: How can we change this biased system using the pedagogy of progressive math and AI education for atmospheric sciences? How can the materials according to the principle of relevant, useful, and modern (RUM) be taught efficiently and promptly? How can the modern computer technologies be used in teaching and learning?

We will show atmospheric science examples of how to incorporate computer programming and real climate data in math and AI education to delineate five learning methods of progressive education following the RUM principle: (i) Learning-by-doing by AI tools, Python or R, NOAA data, and hands-on projects; (ii) student-instructor interaction through the Discussions function on Canvas or Blackboard and through students’ sharing and instruction via online tools like Zoom or Colab; (iii) concept understanding (not rote learning) by data visualization, drawing, and real-world examples; (iv) learning with bigger goals in mind by storytelling and students’ explanation of their study goals and future courses; and (v) individualized learning with flexible and modern curricula that have AI and data science components to make students competitive in the job market of digital economy. The presentation materials are based on our published journal papers, books, and decades of experience in mathematics/AI/data and atmospheric sciences education and administration.