The researchers sought to evaluate other strategies of feedback-targeting, using the collected data, to evaluate the performance of the researcher's NLP strategy, which was used to pick out the right feedback message from the response. Researchers implemented and tested four strategies to evaluate in regards to learning gain: oracle, multiple-choice targeted policy, and two NLP targeted policies. There was a 95% confidence interval used in these tests. Participants' learning gains by four different one-questions policies were compared and results showed that policies targeted through NLP outperformed multiple-choice targeted strategies on all of the exercises except the last one (3/4 exercises). This shows the effectiveness of using NLP responses, rather than multiple-choice responses to provide the most effective feedback. Furthermore, compared to multiple-choice responses alone, the NLP strategy proved to contain more variation between questions. Unlike the multiple-choice models, the NLP models can generalize to unseen questions by learning a general notion of how students respond to prompts depending on what they know. These results show the promising effects of using natural-language inputs from students for feedback selection.