Capacity building through project based learning in Bayesian statistics

Abstract

Outcome-based education requires statistics education in the 21st century to be structured holistically by allowing the students to work with real life data along with visualization, computation, and learning outputs. The new K-12 curriculum resulted to a chain reaction in the Statistics undergraduate program by including alternative statistical frameworks such as Bayesian statistics. This paper focuses on project-based learning approach on designing learning outputs for undergraduate and graduate students in Bayesian statistics. The stages of project-based learning in completing these Bayesian learning outputs have helped in building the capacities of the students to understand the essential concepts in Bayesian inference and do computations using software. Some of the learning outputs are cited. Insights are helpful in making the syllabus of Bayesian Statistics and Inference of undergraduate Statistics program. Project based learning incapacitates the students to do Statistics research in an organized manner and make decisions based on data.