(241c) Development of Static and Dynamic Simulation-Based Active-Learning Modules for Chemical Engineering Curriculum

As technology and specifically dynamic simulators become commonplace in engineering programs, engineering educators have a great opportunity to evolve the way they approach engineering education. Most students are familiar with the traditional lecture structure commonly used in engineering classrooms across the United States. In this structure, a new concept is introduced to the students, a derivation is performed to create a useful tool for solving problems, an example problem is then proposed, and the example problem is solved using the derived formula. Although this structure feels like a logical methodology for teaching a new concept and reinforcing it through application via problem-solving, there is little evidence in literature to support this approach. In a study done by Prince, et. al. involving 344 undergraduate chemical engineering students in a heat transfer course, they found that this traditional approach to teaching STEM (science, technology, engineering, math) generally does not develop conceptual understanding of the material [1]. The education literature seems to confirm this finding as well [2]–[4].One of the contributing factors to this is the lack of active learning involved. Although it may feel like solving a real-world problem as described earlier should cover this, it rarely engages or challenges students. Rather than students learning the concepts to use in the problem-solving process, they learn the procedures/algorithm for solving a very narrow family of problems.

Another major factor in the success and failure of students in engineering courses is the difference between “sensors” and “intuitors.” According to the literature, sensors are students who prefer the practical side of learning whereas the intuitor enjoys exploring abstract concepts. If the vast majority of engineering teaching relies on this derivation-based approach to teaching, then it will disproportionately benefit intuitors and sensors will be left behind [5]. This means, there is a gap in engineering pedagogy when it comes to active learning techniques that supplement this practical side to STEM education that can complement the theoretical side. With technology becoming more common in engineering course work, dynamic simulators offer a great opportunity for improving engineering pedagogy in this area.

In this work, the authors have identified ten courses common to most chemical engineering undergraduate programs in the United States. For each course, four active-learning activities are developed to help reinforce concepts taught in these courses. To do this, the activities are developed into individual modules consisting of background, the activity itself, and a debrief. Literature has found that students attention in the traditional lecture begins to decrease around ten minutes into the lecture [4]. Because of this, the background sections are limited to 15 minutes or less. The activity consists of either solving problems by hand and validating them in the dynamic simulator or by solving the problem by using the only simulator. The simulator employed here is AVEVA Process Simulation, which provides both options of simulation or model writing roles, being an ideal fit for different equation-oriented and simulation-based courses. Lastly, suggested debrief questions are provided to help reinforce the concepts learned through the activity. These modules are then packaged into one teaching reference guide.

The development of such a guide will be a useful tool for faculty interested in using the technology that is becoming more common in engineering programs in a way that promotes active learning. Such a resource greatly reduces the effort required of faculty and creates a better learning environment for students. This ultimately helps faculty become more effective teachers and students to become better learners.

References

[1] M. J. Prince, M. A. S. Vigeant, and K. E. K. Nottis, “Assessing the prevalence and persistence of engineering students’ misconceptions in heat transfer,” J. Eng. Educ., vol. 101, no. 3, pp. 412–438, 2012.

[2] S. A. Ambrose, M. W. Bridges, M. DiPietro, M. C. Lovett, and M. K. Norman, How Learning Works: Seven Research-Based Principles for Smart Teaching. San Francisco, CA: Jossey-Bass, 2010.

[3] A. K. Taylor and P. Kowalski, “Student misconceptions: Where do they come from and what can we do?,” in Applying science of learning in education: Infusing psychological science into the curriculum, Washington, DC, US: Society for the Teaching of Psychology, 2014, pp. 259–273.

[4] R. M. Felder and R. Brent, Teaching and Learning STEM: A Practical Guide. Hoboken, UNITED STATES: John Wiley & Sons, Incorporated, 2016.

[5] R. M. Felder, G. N. Felder, and E. J. Dietz, “The Effects of Personality Type on Engineering Student Performance and Attitudes,” J. Eng. Educ., vol. 91, no. 1, pp. 3–17, 2002, doi: https://doi.org/10.1002/j.2168-9830.2002.tb00667.x.