Processes in Learning

                                   Paul Cohen

   School of Information: Science, Technology and Arts, University of Arizona
                             cohen@cs.arizona.edu



Abstract. One can productively model the learner, the teacher, their dialog, and
other factors that influence learning as statistical processes. For example, one
can model a student as a machine that moves from state to state
probabilistically, conditioned on teachersʼ actions and other learning events.
Models can be personalized by fitting their parameters with data from
individuals, or models might represent groups of learners that are specified a
priori or emerge from clustering. Given models of learning, one can use data
gathered in online teaching/learning systems to optimize learning. I will
describe three efforts in the University of Arizona School of Information to
model aspects of learning statistically: Teaching robots the meanings of verbs
by demonstrating the verb; teaching softbots plans through dialog with ordinary
people; and the undesirable (and unintended!) consequences of inadequate
personalization of tutoring in a conventional intelligent tutoring system.