Biostatistics for beginners: Method employs computational thinking
Biostatistics has become a crucial part of public health practice over the past few decades. However, students in public health degree programs have different backgrounds in mathematics that can make it challenging to teach the fundamental concepts of biostatistics.
In a new paper published in the proceedings of the 6th International Conference on Distance Education and Learning, Qi Zheng, professor in the Department of Epidemiology and Biostatistics at the Texas A&M University School of Public Health, outlines a new biostatistics education approach. The paper describes teaching methods meant to give a better understanding of biostatistics concepts in students without a strong undergraduate mathematics background.
In the paper, Zheng describes an approach that emphasizes a new way of knowledge elaboration. In education theory, knowledge elaboration encompasses learning activities that promote deep processing of new information, in contrast to superficial processing of information such as rote learning. Theorem proving is an example of knowledge elaboration in statistical education. However, public health students have scant prior knowledge of higher mathematics to go that traditional route.
Zheng’s alternative approach leverages the power of computational thinking to impart conceptual statistical knowledge to public health students. Computational thinking was articulated as a new core skill in 2006, and it is now regarded as a fundamental skill for every 21st century citizen.
Zheng’s approach hinges on carefully designed computational exercises that he wrote according to the complexity level of the intended course. For example, a problem for an introductory biostatistics course requires less sophisticated computer coding skills than a problem in a follow-on biostatistics course does. These computational exercises focus on promoting conceptual understanding of biostatistics, and they require only high school-level mathematics. Computational thinking skills are seamlessly woven into the problem-solving process. By shifting attention from mathematics to computation, this incremental, hands-on approach enables public health students to have a firm grasp of statistical concepts more easily, and to acquire computational thinking skills as a by-product.