Previous tutoring systems have given feedback in several contexts, including correcting mistakes, providing hints, and metacognitive feedback meant to encourage good habits. The predominant ways in which previous systems targeted their feedback were based on task-specific inputs such as multiple-choice responses, computer code, and math equations written by the student. These systems are missing out because they use less expressive inputs for targeting feedback to students. Other systems have implemented the use of NLP for providing feedback, but these methods proved to have limitations of feedback targeting mechanisms, which depend on a combination of hand-tuned knowledge engineering and semantic matching. These methods are extremely effort-intensive and are not feasible on a larger scale.