Light-weight clustering techniques for short text answers in human computer collaborative (HCC) CAA

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.