We present two case studies of two different pedagogical tasks in a Computer Assisted Language Learning environment called Grim. The main design principle in Grim is to support ‘Focus on Form ’ in second language pedagogy. Grim contains several language technology-based features for exploring linguistic forms (static, rule-based and statistical), intended to be used while writing. Our question is, in what ways does Grim support Focus on Form in actual classroom use. We have explored this question within sociocultural theory, emphasizing tool mediation and how tools shape the learner’s activity. The first case concerns a text-reconstruction exercise in which students worked in a pair within the Grim environment. The second case was conducted with another group of students, who engaged in collaborative revision of texts, written in advance by one of the students, in student pairs. In both studies, students were instructed and encouraged to use the different features of Grim. Data was collected by recording dialogue during the sessions with Grim. Our results show how learners put the features of Grim into use in their writing tasks. In some instances, the program was used creatively, in combination with external tools such as the users’ own dictionaries, knowledge of other languages, or teachers. In other instances, we note that Grim

Direkt Profil is an automatic analyzer of texts written in French as a second language.

Collocation preferences represent the commonly used expressions, idioms, and word pairings of a language. Because collocation preferences arise from consensus usage, rather than a set of well-defined rules, they must be learned on a case-by-case basis, making them particularly challenging for non-native speakers of a language. To assist non-native speakers with these parts of a language, we developed AwkChecker, the first end-user tool geared toward helping non-native speakers detect and correct collocation errors in their writing. As a user writes, AwkChecker automatically flags collocation errors and suggests replacement expressions that correspond more closely to consensus usage. These suggestions include example usage to help users choose the best candidate. We describe AwkChecker’s interface, its novel methods for detecting collocation errors and suggesting alternatives, and an early study of its use by non-native English speakers at our institution. Collectively, these contributions advance the state of the art in writing aids for non-native speakers. ACM Classification: H5.2 [Information interfaces and