Adaptive language-learning systems often model progress through correctness in constrained exercises, where the target response is predefined. In dialogue-based tutors, by contrast, learners can respond appropriately in many ways, making evidence of progress harder to interpret. This raises a learner-modeling problem: determining whether learner production provides useful evidence of progress, which aspects are informative, and how they might support adaptation. We address this problem using pilot data from an LLM-based English grammar tutor, comprising 40 pre- and post-test tasks, treatment interactions, and 2,406 learner messages. We propose a coding scheme for learner production in dialogue and explore whether the resulting evidence types can support future adaptive decisions. Findings show that learner production in dialogue can support adaptive grammar practice: prior target use predicted short-term performance, while finer-grained evidence helped distinguish different levels of productive control. We discuss implications for adaptive grammar-based dialogue systems that use learner production to support communicative practice.