Implementing Application-Driven Data Analytics in Engineering and Engineering Technology Courses With Student Feedback Assessment

Abstract

Abstract The integration of Application-Driven Data Analytics into Engineering and Engineering Technology courses has become increasingly important due to the growing significance of data in the modern industry. This research aims to explore the potential of introducing new application-driven data analytics modules to advance the teaching effectiveness of the existing fundamental Engineering and Engineering Technology courses in undergraduate education. This pilot project involves one study module called “The Value of Data Analytics in Engineering” in a Machine Design course at Miami University Engineering Technology Department and a Data Analytics Programming course at Oklahoma State University Industrial Engineering & Management School. The lecture in this module includes topics of data collection, manipulation, analysis, and visualization. A lab session is also conducted to analyze the collected tensile test data using Python programming, where various analyses are performed to understand the material properties of the test samples. These analyses include stress-strain curves learning and visualization, material properties calculation, and results comparison to reference data. To gather feedback on the effectiveness of this new study module, students are given surveys after the lectures and lab sessions. The surveys focus on the outcomes of this approach in helping students understand the course material, the ease of tools usage, and the student’s interests in learning further of Data Analytics topics in their future courses. In total 39 students are surveyed for feedback on the lecture and lab session. Overall, the feedback gathered from the students indicates that the approach has been effective. The survey results show that 72% of the students have gained better understanding of data analytics, and 79% of the students are interested in integrating more of the topics into existing curriculum. The use of Python in Google Colab has also been well received by the students, with many finding them easy to use with limited prior data analytics programming experience. Additionally, the students have expressed the usefulness of the skills acquired in their future careers and are interested in learning more about the topics in their curriculum. Therefore, this pilot studies recommend that an application-driven data analytics module can be effectively integrated into more courses in Engineering and Engineering Technology curricula. The future research involves courses such as Introduction to Programming, Manufacturing Processes, Quality Control, Machine Design, Thermodynamics, and Materials Science.