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A Sophomore Level Data Analysis Course Based on Best Practices from the Engineering Education Literature

Introduction

As educators are well aware, the customary educational setting in which students develop problem solving skills is one where the numerical values presented are specific and absolute. The deterministic nature of the end-of-chapter type problems is imbedded in their minds well before students even matriculate.1,2 However, as practicing engineers, they will confront the variation associated with measured data in the real world. Ideally, it is beneficial to prompt students to attend to the concept of variation early in their undergraduate studies. This paper describes the instructional structure and design of a large sophomore level data analysis and statistics class based on best educational practices. It is delivered to chemical, biological and environmental engineers directly following the material and energy balance courses. The goal of the course is to have students recognize that variation is inevitable, and teach them skills to quantify the variation and make engineering decisions which account for it while still utilizing model based problem solving skills.

The instructional design is based on constructivist and social constructivist models of learning. A constructivist perspective views learning as individually constructed based on the learner’s prior knowledge, interpretations, and experience with the world, and views cognitive conflict as a stimulus for learning.3 In addition, a social constructivist perspective views the social interactions and cultural context in which learning occurs as critical.4 Based on these perspectives, it is believed that learning is facilitated when students (1) are engaged in solving real-world problems, (2) use existing knowledge as a foundation for new knowledge, (3) are immersed in a community centered classroom culture, and (4) are prompted to use metacognative skills and strategies.5 The course architecture is designed to match the teaching model of Kolb,6,7 and encourage the development of intellectual growth as modeled by Perry, in which students’ view of knowledge ascends from dualism, to multiplicity of views, and then to contextual relativism.8 While this paper is presented in a course specific context, it is believed these principles are useful to instructional design, in general.

Kolb Learning Cycle and Class Architecture

Kolb6,7 developed a system of selecting classroom activities based upon his research related to adult learning. As schematically shown in Figure 1, there are four “quadrants” of ways that people learn: concrete experience, reflective observation, abstract conceptualization, and active experimentation. Two of these stages, concrete experience and abstract conceptualization, operate in the realm of knowing (how they perceive) while the other two, reflective observation and active experimentation, involve transformation of knowledge. It is by perceiving and then transforming knowledge that people learn. Much has been written about Kolb’s system and its success in engineering education.9-11

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Koretsky, M. (2010, June), A Sophomore Level Data Analysis Course Based On Best Practices From The Engineering Education Literature Paper presented at 2010 Annual Conference & Exposition, Louisville, Kentucky. 10.18260/1-2--16778