The most challenging aspects of teaching probability and statistics to engineering students are the theoretical nature of the topic and the disconnection of the material taught with real-world engineering problems. Although the engineering curriculum in most cases has been updated and expanded to incorporate group work and project-based learning, most of the mathematical oriented courses are still taught in a passive manner.
Our goal is to enhance students’ critical thinking by integrating case studies to our introductory course in probability and statistics. This is typically a sophomore-level core course in the industrial engineering curriculum. Students who complete this course, should be able to understand the role of uncertainty in engineering models, apply critical probability concepts (e.g. independence, expectation, variance), identify and analyze discrete and continuous random variables, and formulate and conduct statistical analyses of observed data.
One key innovation that we implemented is the introduction of real-world data-driven case studies. We wish to expose our students to engineering problems that will help them relate the material taught in class with their own major. The primary enabling technology is statistical programming with Python. The case studies are introduced as group assignments and are motivated in class or discussion sessions. Students select their own groups and in the end of each case study, they do a peer-evaluation in order to assess the degree of in-group collaboration. In this way, students build valuable competencies, such as problem solving, critical thinking, and collaboration. We have also updated the way the students are being evaluated; therefore, case studies are graded based on rubrics that clearly communicate our expectations to the students. Finally, to track the progress and evaluate the success of the above innovations, we have created an attitudes survey (beginning/end of the semester) and an informal early feedback survey (middle of the semester).
Based on the feedback we got from the students, as well as their grades in the case studies and exams, we are confident that the implemented innovations improved our students’ critical thinking and trained them in working in groups. Furthermore, by having them work in realistic case studies, they gained a deeper understanding of statistical concepts, enhanced by the necessary technical foundations in theory and programming.
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