Unnatural Language Processing

Computer-based logic proofs are a form of unnatural language in which the process and structure of proof generation can be observed in considerable detail. We have been studying how students respond to multimodal logic teaching, and performance measures have already indicated that students' pre-existing cognitive styles have a significant impact on teaching outcome. Furthermore, a large corpus of proofs has been gathered via automatic logging of proof development. This paper applies a series of techniques, including corpus statistical methods, to the proof logs. The results indicate that students' cognitive styles influence the structure of their logical discourse, via their differing methods of handling abstract information in diagrams, and transferring information between modalities.