Conceptual Thinking in Statistics and Data Science Education: Interactive Formative Assessment with Meaning Equivalence Reusable Learning Objects (MERLO)

Abstract

Computer age statistics, machine learning, data science and in general, data analytics, are having an ubiquitous impact on industry, business and services. Deploying a data transformation strategy requires a workforce which is up to the job in terms of knowledge, experience and capabilities. The application of analytics needs to address organizational needs, invoke proper methods, build on adequate infrastructures and ensure availability of the right skills to the right people. such upskilling requires a focus on conceptual understanding affecting both the pedagogical approach and the complementary learning assessment tools, This paper is about the application of advanced educational concepts to the teaching and evaluation of statistical and data science related concepts. Two educational elements will be included in the discussion: i) the use of simulations to facilitate problem based experiential learning and ii) an emphasis on information quality, as the overall objective of statistics and data science activity. We begin with an introduction to conceptual thinking and meaning equivalence and the application of Meaning Equivalence Reusable Learning Objects (MERLO) in the classroom. We then describe how MERLO and concept science can be applied in the domain of Statistics and Data Science. Section 3 is about the use of simulations in statistical education. In section 4 we discuss practical aspects of an education program focused on conceptual understanding. Section 5 is on the conceptual mapping of information quality as a MERLO infrastructure. The paper concludes with a discussion.