(299c) Comparison of Expert and Novice Solution Approaches to An Industrially Situated Process Development Project

Authors: 
Koretsky, M. D., Oregon State University
Sherrett, B., Oregon State University
Gummer, E., Education Northwest
Gilbuena, D., Oregon State University
Nefcy, E., Oregon State University


Engineering
educators must be mindful of the diverse range of careers their students will pursue
and the rapid integration of new technologies into those careers. Such a
perspective naturally leads to a shift of focus from teaching students ?what to
think? to teaching them ?how to think? and, even more specifically, ?how to
approach novel problems.?  Within this context, a greater emphasis is
being placed on developing students' abilities to transfer knowledge and
understanding from the classroom to professional practice. The research
reported in this paper seeks to identify and compare methods that expert and
novice engineers use to solve the same novel process development project.
Through this study, we seek to contribute to the understanding of transfer in
ill-structured engineering problems.

Participants
in the study were tasked with developing an optimal ?recipe? for a Low Pressure
Chemical Vapor Deposition (LPCVD) reactor that deposits thin films of silicon
nitride on polished silicon wafers, an initial step in the manufacture of
transistors. The optimization problem is completed using a virtual laboratory
and involves iteratively developing, testing, and refining solutions based on
adjusting input parameters and measuring outputs.  The computer simulation
generates data representative of an industrial reactor and includes both random
and systematic process and measurement error.  Users are also encouraged
to apply sound engineering methods since they are charged virtual money for
each reactor run and each measurement.  This problem is complex and a
typical team spends approximately 15-25 hours to complete it.

For
many years, the virtual LPCVD project has been delivered in a capstone chemical
engineering course. The solution process of one student cohort is used to
represent different possible novice solution approaches. Additionally, three
industrially accomplished experts led teams complete the project.  Two of
the experts had no direct experience with CVD reactors.  This choice was
deliberate as we seek to characterize how experts will solve the problem by
transferring their core engineering knowledge and skills to a novel problem.
One expert was a mechanical engineer who lacked domain specific knowledge, but
is an internationally recognized leader in engineering
design.   

The
virtual LPCVD project contains the features of an ill-structured problem. It is
authentic, ambiguous, and has many possible solutions and solution paths. 
However, it has more constraints than design problems found ?in the wild.? This
aspect reduces the degrees of freedom and facilitates comparison of different solution
processes.  Additionally, the virtual environment enables a more thorough
assessment of a team's proposed ?recipe.? Since the error can be removed and
film thickness ?measured everywhere,? an absolute metric of performance can be
obtained and used for comparison.

Solution
processes were documented using talk-aloud protocol analysis, collection of
laboratory notebooks and written reports, and data acquired by the computer
interface. Previously we have reported a graphical method, termed model
representation and usage maps, which allows us to characterize participants'
model development as they solve the problem.  These maps show the nature
of each model component (quantitative, qualitative, graphical, empirical,
statistical), its correctness, and its use in the solution process (did it
direct the values of input parameters for a future run, was a run used to
quantify model parameters, was the model qualitatively verified, etc.). The
model maps are the primary artifact of analysis and form the basis to compare
participants' solution processes. Additionally, semi-structured interviews are
used to validate the results of the study and provide further insight.

Solution characteristics and
approaches of the expert and the student teams are identified. Compared to the
student teams, the expert engineers devote a higher proportion of time to
information gathering and problem scoping. They also tend to access large
?chunks? of information during the problem solving process.  Once
activated, these chunks tend to remain relatively constant in form and are used
throughout the solution. However, regarding performance, the final recipes of
the experts were no better than a large fraction of the student teams. Specific
ways that the participants' prior knowledge and previous experiences were
applied to this new situation are identified and will be discussed.