Towards Automatic Description of Knowledge Components
Cyril Goutte, Guillaume Durand, Serge Leger · 2015
A key aspect of cognitive diagnostic models is the specification of the Q-matrix associating the items and some underlying student attributes.In many data-driven approaches, test items are mapped to the underlying, latent knowledge components (KC) based on observed student performance, and with little or no input from human experts.As a result, these latent skills typically focus on modeling the data accurately, but may be hard to describe and interpret.In this paper, we focus on the problem of describing these knowledge components.Using a simple probabilistic model, we extract, from the text of the test items, some keywords that are most relevant to each KC.On a small dataset from the PSLC datashop, we show that this is surprisingly effective, retrieving unknown skill labels in close to 50% of cases.We also show that our method clearly outperforms typical baselines in specificity and diversity.