Question: what opportunities are highlighted?

Evidence:
raw_evidence: Our annotation scheme introduces opportunities for the educational community to conduct further research on the relationship between features of student talk, student learning, and discussion quality. Although Chisholm and Godley Chisholm:11 and we found relations between our coding constructs and discussion quality, these were small-scale studies based on manual annotations. Once automated classifiers are developed, such relations between talk and learning can be examined at scale. Also, automatic labeling via a standard coding scheme can support the generalization of findings across studies, and potentially lead to automated tools for teachers and students.
The proposed annotation scheme also introduces NLP opportunities and challenges. Existing systems for classifying specificity and argumentation have largely been designed to analyze written text rather than spoken discussions. This is (at least in part) due to a lack of publicly available corpora and schemes for annotating argumentation and specificity in spoken discussions. The development of an annotation scheme explicitly designed for this problem is the first step towards collecting and annotating corpora that can be used by the NLP community to advance the field in this particular area. Furthermore, in text-based discussions, NLP methods need to tightly couple the discussion with contextual information (i.e., the text under discussion). For example, an argument move from one of the discussions mentioned in Section 4 stated “She's saying like free like, I don't have to be, I don't have to be this salesman's wife anymore, your know? I don't have to play this role anymore." The use of the term salesman shows the presence of specificity element (3) (see Section 3.2) because the text under discussion is indeed Death of a Salesman. If the students were discussing another text, the mention of the term salesman would not indicate one of the specificity elements, therefore lowering the specificity rating. Thus, using existing systems is unlikely to yield good performance. In fact, we previously BIBREF31 showed that while using an off-the-shelf system for predicting specificity in newspaper articles resulted in low performance when applied to classroom discussions, exploiting characteristics of our data could significantly improve performance. We have similarly evaluated the performance of two existing argument mining systems BIBREF18 , BIBREF33 on the transcripts described in Section SECREF4 . We noticed that since the two systems were trained to classify only claims and premises, they were never able to correctly predict warrants in our transcripts. Additionally, both systems classified the overwhelming majority of moves as premise, resulting in negative kappa in some cases. Using our scheme to create a corpus of classroom discussion data manually annotated for argumentation, specificity, and knowledge domain will support the development of more robust NLP prediction systems.

Answers:
Our annotation scheme introduces opportunities for the educational community to conduct further research , Once automated classifiers are developed, such relations between talk and learning can be examined at scale,  automatic labeling via a standard coding scheme can support the generalization of findings across studies, and potentially lead to automated tools for teachers and students, collecting and annotating corpora that can be used by the NLP community to advance the field in this particular area