Work-integrated learning, also known as co-operative education, allows students to alternate between on-campus classes and off-campus work terms. This provides an enhanced learning experience for students and a talent pipeline for employers. We observe that co-operative job postings are a rich source of information about the required skills, working environment and company culture. We present a text mining methodology to extract and cluster informative terms from unstructured job descriptions, and we demonstrate the utility of our methodology on a co-op job posting corpus from a large North American university. [For the full proceedings, see ED593090.]