One of the most persistently difficult aspects of vocabulary for foreign language learners is collocation. This paper describes a browser-based agent that assists learners in acquiring collocations in context during their unrestricted Web browsing. The agent overcomes the limitations imposed by learner models in traditional ITS. Its capacity to function in noisy unscripted contexts derives from a well-understood theory of lexical knowledge that attributes a word's identity to its contextual features. Collocations constitute a central feature type, and we extract these features computationally from a 20-million-word portion of BNC. These we are able to detect and highlight in real time for learners in the noisy Web environments they freely browse. Our learner model, derived by semi-automatic techniques from our 3-million word corpus of learner English, maps detected collocations onto corresponding collocation errors produced by this learner population, alerting learners to the non-substitutability of words within the target collocations. A notebook offers a push function for individualized repeated exposure to examples of these collocations in context.