We explored the possibility of predicting learners' affective states (boredom, flow/engagement, confusion, and frustration) by monitoring variations in the cohesiveness of tutorial dialogues during interactions with AutoTutor, an intelligent tutoring system with conversational dialogues. Multiple measures of cohesion (e.g., pronouns, connectives, semantic overlap, causal cohesion, coreference) were automatically computed using the Coh-Metrix facility for analyzing discourse and language characteristics of text. Cohesion measures in multiple regression models predicted the proportional occurrence of each affective state, yielding medium to large effect sizes. The incidence of negations, pronoun referential cohesion, causal cohesion, and co-reference cohesion were the most diagnostic predictors of the affective states. We discuss the generalizability of our findings to other domains and tutoring systems, as well as the possibility of constructing real-time, cohesion-based affect detectors.