In this study, we explore the potential of a natural language processing (NLP) approach to support discourse analysis of in‐situ, small group learning conversations. The theoretical basis of this work derives from Bakhtin’s notion of speech genres as bounded by educational robotics activity. Our goal is to leverage computational linguistics methods to advance and improve educational research methods. We used a parts‐of‐speech (POS) tagging program to automatically parse a transcript of spoken dialogue collected from a small group of middle school students involved in solving a robotics challenge. We grammatically parsed the dialogue at the level of the trigram. Then, through a deliberative process, we mapped the POS trigrams to our theoretically derived problem solving in computational environments coding system. Next, we developed a stacked histogram visualization to identify rich interactional segments in the data. Seven segments of the transcript were thus identified for closer analysis. Our NLP‐based approach partially replicated prior findings. Here, we present the theoretical basis for the work, our analytical approach in exploring this NLP‐based method, and our research findings. What is already known about this topic Over the last 10 years, several educational research papers indicate that natural language processing (NLP) techniques can be used to help interpret well‐structured, written dialogue, eg, conversations in online class discussions. Two recent papers indicate that NLP techniques can also be used to help interpret well‐structured, spoken dialogue, eg, replies to interview questions and/or comments made during think aloud protocols. Multimodal learning analytic techniques are being used to investigate collaborative learning. These studies use non‐verbal features of data (gaze, gesture, physical actions), prosodic features of verbal data (pitch and tone) and/or turn‐taking and duration of talk per speaker data, as means of predicting group success. None of the MMLA studies attempt semantic analysis of student talk in collaborative settings. What this paper adds A theoretical framework for why and how an automated NLP approach can support discourse analysis research on co‐located, computer‐based, collaborative problem solving interactions. This framework, entitled the Problem Solving in Computational Environment Speech Genre, links children’s physical interactions with computational devices to their verbal exchanges and presents a theoretical rationale for the use of NLP methods in educational research. Description of an interdisciplinary method that combines NLP techniques with qualitative coding approaches to support analysis of student collaborative learning with educational robotics. Identification of student learning outcomes derived from the semantic, PSCE Speech Genre and NLP approach. Implications for practice and/or policy Educational researchers will be able to expand upon our findings towards the goal of using computation and automation to support microgenetic analysis of la...

Robotics is a robust vehicle for supporting the development of computational thinking in students. Educational robotics activities unfold in a multidimensional problem space that requires the integration of programming, building, and environmental aspects of the activity. Students working collaboratively with robotics have the opportunity to adopt roles within the group that are aligned to these multiple dimensions (e.g., programmer, builder, and analyst). Group roles are an important element of all collaborative learning, but especially in a Computer-Supported Collaborative Learning (CSCL) environment, as the roles help to regulate group activity and learning. In this observational, microgenetic case study, we investigated the relationship of the roles middle school-aged girls adopted to the collaborative interactions they engaged in, and, ultimately to the development of computational thinking evidenced as a result of role participation. The video and audiotaped data used in this study were collected at a 1-day introduction to robotics workshop for girls. Our mixed methods approach included sequential and qualitative analysis of the behavioral and verbal interactions of two groups of girls (n 6) who participated in the workshop. Our results indicate that the emergence of distinct roles correlates with periods of collaboration and periods of parallel solo work, which, in turn, had an impact on student's engagement in computational thinking including solution planning, algorithmic and debugging operations, and the design of the robotic device. Moreover, students who engaged in greater levels of collaboration selected more difficult challenges to solve within the robotics environment. Suggestions for future research are provided.