Ronald W. Ferguson
Statement of Teaching Interests
Teaching is an activity that I enjoy for many reasons. First, there is the simple satisfaction of
explaining a topic to students, showing them how it works and why it is important. As a
cognitive scientist, I find additional satisfaction in exploring – in a very immediate and practical
way – techniques that facilitate learning.
Courses Taught
I have taught three classes at Northwestern University: an upper-level AI course, which I taught
twice, and an introductory cognitive science course.
The course I taught this fall, “Cognitive Science 207: Introduction to Cognitive Modeling,” was
an unexpected but enjoyable assignment. Two weeks before the term, the scheduled professor
retired and I stepped in to teach a class of 67. This class provides a non-technical introduction to
artificial intelligence and cognitive science for liberal arts majors, and prepares cognitive science
majors for more advanced study. The format in previous years was highly Socratic: students
engaged in class discussions on topics such as “what does it mean to remember?” and wrote
weekly one-page essays. I kept the Socratic discussion format and the weekly essays, which
students found extremely motivating. However, finding evidence that students were having
difficulty in follow-on courses, I also assigned a general cognitive science text and additional
readings to better ground the students in current theories and terminology. I lectured on
foundational issues, and guest lecturers were brought in for areas such as computer vision and
natural language processing. The results were very positive. Students covered more challenging
material than previously, and student reviews were the second highest in four years.
Twice previously I taught CS 344, “The Design of Computer Problem Solvers.” Both students
and professors have told me that CS 344 is traditionally Northwestern’s most difficult AI course.
This upper-level class covers classical problem solving, several different truth-maintenance
systems, constraint languages, and other advanced AI techniques, ranging from open-coded
unification to qualitative physics (I also added a week on Bayesian networks). The class format
is project-based, with weekly programming assignments in the early weeks that taper in difficulty
as students dig more deeply into their personal projects. In project-based classes, I like to provide
multiple opportunities for peer review. For this reason, each student provided an initial proposal,
and then critiqued two other proposals via email. For the final session, students presented their
results to the class. Students built reasoners for a variety of domains, ranging from beer brewing
to generating musical chords for simple melodies. Student reviews of the class were very positive.
Teaching Methodology
Like most instructors, I combine lectures with classroom discussion. However, I have also tried
(and continue to try) new teaching techniques, including project-based assignments, collaborative
learning exercises, dividing the class into small discussion groups, and student presentations.
In all my classes, I have also used the web and mailing lists. In general, I have used web sites to
post syllabi, reading lists, and assignments. However, the web has also been useful for posting
project proposals for class discussion, to provide materials for slow or advanced students, and to
Ferguson Teaching Statement - Page 1 of 2
clarify homework assignments. My course web sites are still online (for the details, see
http://www.qrg.nwu.edu/people/ferguson).
Future Course Opportunities
I would enjoy teaching more artificial intelligence and cognitive science classes. I am also
adequately trained to teach classes in object-oriented programming, data structures, and software
engineering, among other areas.
I am interested in developing some advanced-level courses. For example, I hope to develop a
seminar course on computer models of analogy and similarity. The course’s motivation stems
from the current fragmentation in analogical reasoning between case-based reasoning techniques
in artificial intelligence, computer-based psychological models of analogical reasoning (such as
structure-mapping theory), and other related areas, such as statistical clustering and data-mining
techniques. An integrated overview of these techniques could encourage a wider application of
analogical reasoning in industry.
I also hope to develop a course on diagrammatic reasoning, an area motivated by the increasingly
graphical nature of both paper and web-based communications. A practical course could
reasonably cover the basics of visual structure in perceptual psychology, computer models of
spatial representation, and diagram and sketch analysis systems.
Ferguson Teaching Statement - Page 2 of 2