In addition to my current explorations related to electronic portfolios and serious games and their roles in education, I have plans that look back at my own PhD work and its merit in teaching and learning, as well as plans to begin work related to student retention in STEM (science, technology, engineering, and math) fields, an area in which some work has been done, but many more questions can be asked, and much more data can be acquired.
I hope you can forgive the ambiguity of my "future work" title, but I think it captures two distinctly different long-term objectives: (1) to explore the use of more intense mathematical modeling instruction in traditional engineering curricula and (2) to develop appropriate mathematical and statistical models to both match historical trends and recent initiatives in STEM education to help prioritize effective changes to the field.
Explorations in Mathematical Modeling
I believe that chemical engineering in particular provides a unique perspective to a number of mathematical models. By performing basic material balances, several areas of seemingly unrelated research can be studied. Equations for modeling the population of trophic levels in an ecosystem, for instance, are readily available by accounting for flows of biomass into and out of a system. Population ecology, when approached from the perspective of a computational chemical engineer, can make aspects of ecological and physiological studies virtually indistinguishable from studies on chemical kinetics. Similarly, epidemiological population models in the literature are also derived from this same careful accounting. I intend to investigate complicated deterministic and stochastic models that result from continuous- and discrete-time versions of such models. By introducing a variety of potential applications to students, we increase the number of opportunities to motivate students and to connect to prior learning, interests, and experiences.
I further intend to aid students in the study of such systems under uncertainty. There are a number of available sampling methods for both continuous- and discrete-time models, as well as rigorous numerical methods for continuous-time models. Introducing uncertainty early in an engineering curriculum is important to combating the typical “plug-and-chug” mindset that a number of science and engineering students bring to college.
Retention of Students in Science, Technology, Engineering, and Mathematics
I have just begun to peruse the literature on this topic, but I certainly agree with the importance of this work. These fields, and especially engineering, are remarkably easy to drop as majors, but do not have the same accessibility as a some of liberal arts fields, so they are not particularly easy to add. There are a number of questions related to migration among majors in college.
Because of the necessary mathematical and scientific background in engineering, a number of students do not consider entering the discipline. Is there a way to lower that apparent "barrier" to attract students who have what it takes to be engineers? Does the problem simply belong to remedial math and science programs? How can engineering disciplines get involved to improve these programs?
If the "barrier" to engineering cannot be overcome, then our attention must turn to retention: how do we get students who have considered engineering to stay there? What resources must be made available? How do different social groups consider their major in engineering? What is the effect of having a common first-year program versus a discipline-specific one?