We first explore the paedogogic value, in assessment, of questions which
elicit short text answers (as opposed to either multiple choice questions or
essays). Related work attempts to develop deeper processing for fully
automatic marking. In contrast, we show that light-weight, robust, generic
Language Engineering techniques for text clustering in a human-computer
collaborative CAA system can contribute significantly to the speed, accuracy,
and consistency of human marking. Examples from real summative
assessments demonstrate the potential, and the inherent limitations, of this
approach. Its value as a framework for formative feedback is also discussed
