Just as conversation is the cornerstone of my research, it is also a center piece in my teaching. As a notable example, many of the ideas that form the foundation for the collaborative research on classroom discourse I am leading with Lauren Resnick in the context of the Pittsburgh Science of Learning Center as part of the Social and Communicative Factors in Learning thrust are at the heart of my own classroom teaching. While leading class discussions was a challenge for me when I first began my teaching career, I have continued to work to put into practice the methodologies that research has proven effective, and now the classroom discussions that come out in my own courses are what I most look forward to as an instructor. I believe it is this emphasis on lively class discussion that is largely responsible for the steady increase in teaching scores I have earned over my years of teaching.
What fascinates me most about studying the role of conversation in learning is that new ideas may be created when exchanging alternative viewpoints. The new ideas that emerge through conversation may draw from the differing perspectives of the participants but nevertheless be distinct from the ideas that existed in any of their minds prior to the interaction. The research literature on group learning provides strong evidence that the success of such interactions between students depends upon the ability of the instructor to facilitate this process. The instructor creates opportunities for learning by meeting the students on their own path and offering the support necessary to draw out the students’ differing perspectives and ideas. In the midst of this conversation, the instructor is well situated to present the content of the course in a way that is seen by students as relevant to meeting their own goals. In creating an environment where students see their involvement in a course as a means to move forward on their own path, the instructor has the opportunity to play the role of a mentor who comes along side students to offer experience and wisdom and to help them navigate the maze that is before them. That investment of the instructor in individual students yields the greatest increase when it is internalized by the students and then brought back into small group activities and the whole group discussion. Thus, my philosophy of teaching is to strive for a personal connection through conversation with and between students.
An essential ingredient in this learning conversation is the differing perspectives of the participants who are involved. The School of Computer Science at Carnegie Mellon is made up of distinct, tight knit communities of specialization that are situated in such a way as to provide many opportunities for exchanging views. This is an ideal environment in which this philosophy of teaching can flourish. Thus, in my position with appointments in both the Language Technologies Institute and the Human-Computer Interaction Institute, I have taken advantage of the opportunity to create four bridge courses designed to promote understanding and strengthen interactions between departments and to keep the conversation active.
I designed the first course I have taught, Conversational Interfaces, to raise awareness within the language technologies community to the issues that affect how humans interact with computers through natural language. My goal was to offer students a new lens through which to view and evaluate the significance of their work. Students who take this course leave with a deeper appreciation of the field of human-computer interaction and continue to use methodologies from that field in their language technologies research. Some have used this course as an opportunity to adjust their research direction with the goal of achieving greater human impact or a more usable technological solution. In order to engage more deeply with the HCI community, some have gone on to publish their term projects and subsequent work at human-computer interaction conferences including the ACM SIG-CHI conference, Intelligent User Interfaces, and INTERACT. This course has not been offered in recent years, but I continue to receive requests for this course, and thus I plan to offer it again at some point in the future.
While my first course was meant to build a bridge from language technologies to human-computer interaction, the second course I developed with the converse goal in mind. Machine learning in general, and text processing in particular, are playing a larger role in many areas of human-computer interaction including on-line communities, educational technology, ubiquitous computing, and adaptive user interfaces. I designed the Applied Machine Learning course, also known as Machine Learning in Practice, to make machine learning and basic text processing technology more accessible to HCII PhD students. A great many of these students actively use these technologies in their research, but can benefit from further instruction in how to use it more thoughtfully and effectively. The emphasis of the course is on the process of applying machine learning to a variety of problems rather than emphasizing an understanding of the theory behind machine learning, although theory is covered as necessary to support thoughtful application. In addition to gaining practical skills to apply in their own research, several students who have taken the course have gone on to participate in other language technologies or artificial intelligence courses in order to deepen their understanding of the technology and participate in more of an intensive intellectual exchange. While I developed the applied machine learning course for the HCI PhD students, it has since been expanded to reach out to SCS undergrads and students from other programs (including the Master of Information Science Management and programs within Humanities and Social Sciences and the College of Fine Art), creating a more diverse classroom environment. In Fall of ’07 I produced it as a distance course. Currently, the course is offered as an in person course each Fall with between 25 and 30 students, and as an on-line self-paced course each Spring with between 20 and 25 students. It consistently earns excellent teaching scores in both forms.
Computer Supported Collaborative Learning is the third course that I developed. This course has been one of the most successful at realizing my vision of bringing language technologies and human-computer interaction students together to exchange views and in some cases to work on joint projects. When it was first offered in Spring of ’08, the student population participating in the course was a balance of students from both departments as well as students from Engineering and Public Policy, Architecture, and Information Science as well as Intelligent Systems from the University of Pittsburgh. The progression of topics discussed in the course allowed those students from a technology background and those from a human-computer interaction background to alternate between the role of more knowledgeable student or less knowledgeable student in order to encourage an exchange of views in both directions between students. In one portion of the course students were exposed to the foundational theoretical and methodological issues underlying previous work in collaborative learning, while in another portion students were introduced to the wide range of current approaches to collaborative learning support that exist within the field of Computer Supported Collaborative Learning as well as offered hands on experience with new technologies. Machine learning and text processing technologies offer the potential for greater adaptation to individual needs of students and groups in the support that is offered in collaborative learning environments. While this course was designed by me, the first offering of this course in Spring of 2007 was co-taught with Susan Finger. Involvement of two students from the Engineering in Public Policy department grew into a new reading group on HCI in the Developing World that I led for almost two years, which included students and faculty from SCS and H&SS. The Spring -09 offering of this course had education in the developing world as a running theme.
Summarization and Personal Information Management is my fourth course creation, which was offered for the first time in the Spring 2008 semester, with 23 students enrolled. Similar to Computer Supported Collaborative Learning, this course was meant to bring students from the Language Technologies Institute and the Human-Computer Interaction Institute together to share their perspectives on the problem of personal information management, which has been addressed separately within both communities, but not in a way that integrates perspectives from both. I significantly revamped the course for the Spring 2010 offering, bringing in several readings from systemic functional linguistics where rhetorical analyses of academic writing informs the design of summarization systems and raises new questions both in terms of techniques and evaluation methodologies.
One thing I greatly appreciate about teaching in the School of Computer Science at Carnegie Mellon University is the tremendous freedom we have here as faculty to design and teach courses according to our interests, and I immensely enjoy teaching a wide variety of courses, which nevertheless synergize and build on one another. In addition to the four bridge courses mentioned above, I have designed and taught a cross-cutting course called Research Design and Writing, which emphasized the connection between research design and scientific writing. While the course touched upon basic issues in research methodology, the focus was on writing, evaluating writing, and revision. 8 students enrolled in the course. While the course was small, it was well liked by students and received 5s both for course ratings and instructor ratings. Twice as many students enrolled the second time it was offered in Fall of ’09. I carry themes from this course into my other four courses when I teach them as well as bringing themes from my other course as examples of research issues into this course.
In addition to the four full courses I have developed, I have also developed units that have been included in courses that I have team taught, such as a unit on architectures for robust language understanding that I taught in the Spring 2004 offering of Grammar Formalisms, a unit on Human-Computer Interaction as part of the Software Engineering for Information Systems course in Fall of 2007, and a three week unit on video and verbal protocol analysis that I developed and taught jointly with Marsha Lovett from Psychology in Research Methods for the Learning Sciences in Spring of 2006, 2007, 2008, and 2010. I also added a computational track to the Meaning in Language course, with primary instructor Mandy Simons in H and SS. We co-taught that course in Fall of 2009. I am currently serving on the steering committee for the Computing @ Carnegie Mellon course, which all Freshman Carnegie Mellon students take, and served as a content expert and content developer for a new unit on information literacy that has been part of that course since Fall of 2010. This Spring I am teaching a new course on Computational Models of Discourse Analysis.
In conclusion, just as my research interests in supporting and shaping learning through collaborative conversation informs my teaching, my teaching also informs my research. My conversations with students and observations of their interactions with each other in my courses and in my lab give me insight into their learning processes, which I can then apply in my research.
Carolyn Penstein Rose (cprose@cs.cmu.edu)/ Carnegie Mellon University