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Transcripts of teaching episodes can be effective tools to understand discourse patterns in classroom instruction. According to most educational experts, sustained classroom discourse is a critical component of equitable, engaging, and rich learning environments for students. This paper describes the TalkMoves dataset, composed of 567 human annotated K-12 mathematics lesson transcripts (including entire lessons or portions of lessons) derived from video recordings. The set of transcripts primarily includes in-person lessons with whole-class discussions and/or small group work, as well as some online lessons. All of the transcripts are human-transcribed, segmented by the speaker (teacher or student), and annotated at the sentence level for ten discursive moves based on accountable talk theory. In addition, the transcripts include utterance-level information in the form of dialogue act labels based on the Switchboard Dialog Act Corpus. The dataset can be used by educators, policymakers, and researchers to understand the nature of teacher and student discourse in K-12 math classrooms. Portions of this dataset have been used to develop the TalkMoves application, which provides teachers with automated, immediate, and actionable feedback about their mathematics instruction. 

Using new technology to provide automated feedback on classroom discourse offers a unique opportunity for educators to engage in self-reflection on their teaching, in particular to ensure that the instructional environment is equitable and productive for all students. More information is needed about how teachers experience automated data tools, including what they perceive as relevant and helpful for their everyday teaching. This mixed-methods study explored the perceptions and engagement of 21 math teachers over two years with a big data tool that analyzes classroom recordings and generates information about their discourse practices in near real-time. Findings revealed that teachers perceived the tool as having utility, yet the specific feedback that teachers perceived as most useful changed over time. In addition, teachers who used the tool throughout both years increased their use of talk moves over time, suggesting that they were making changes due to their review of the personalized feedback. These findings speak to promising directions for the development of AI-based, big data tools that help shape teacher learning and instruction, particularly tools that have strong perceived utility. 

Inclusion in mathematics education is strongly tied to discourse rich classrooms, where students ideas play a central role. Talk moves are specific discursive practices that promote inclusive and equitable participation in classroom discussions. This paper describes the development of the TalkMoves application, which provides teachers with detailed feedback on their usage of talk moves based on accountable talk theory. Building on our recent work to automate the classification of teacher talk moves, we have expanded the application to also include feedback on a set of student talk moves. We present results from several deep learning models trained to classify student sentences into student talk moves with performance up to 73% F1. The classroom data used for training these models were collected from multiple sources that were pre-processed and annotated by highly reliable experts. We validated the performance of the model on an out-of-sample dataset which included 166 classroom transcripts collected from teachers piloting the application. 

Engaging in physical computing activities involving both hard- ware and software provides a hands-on introduction to computer science. The move to remote learning for primary and secondary schools during the 2020-2021 school year due to COVID-19 made implementing physical computing activities especially challenging. However, it is important that these activities are not simply eliminated from the curriculum. This paper explores how a unit centered around students investigating how programmable sensors that can support data-driven scientific inquiry was collaboratively adapted for remote instruction. A case study of one teacher’s experience implementing the unit with a group of middle school students (ages 11 to 14) in her STEM elective class examines how her students could still engage in computational thinking practices around data and programming. The discussion includes both the challenges and unexpected affordances of engaging in physical computing activities remotely that emerged from her implementation. 

TalkMoves is an innovative application designed to support K-12 mathematics teachers to reflect on, and continuously improve their instructional practices. This application combines state-of-the-art natural language processing capabilities with automated speech recognition to automatically analyze classroom recordings and provide teachers with personalized feedback on their use of specific types of discourse aimed at broadening and deepening classroom conversations about mathematics. These specific discourse strategies are referred to as “talk moves” within the mathematics education community and prior research has documented the ways in which systematic use of these discourse strategies can positively impact student engagement and learning. In this article, we describe the TalkMoves application’s cloud-based infrastructure for managing and processing classroom recordings, and its interface for providing teachers with feedback on their use of talk moves during individual teaching episodes. We present the series of model architectures we developed, and the studies we conducted, to develop our best-performing, transformer-based model (F1 = 79.3%). We also discuss several technical challenges that need to be addressed when working with real-world speech and language data from noisy K-12 classrooms. 

TalkMoves is an innovative application designed to support K-12 mathematics teachers to reflect on, and continuously improve their instructional practices. This application com- bines state-of-the-art natural language processing capabilities with automated speech recognition to automatically analyze classroom recordings and provide teachers with personalized feedback on their use of specific types of discourse aimed at broadening and deepening classroom conversations about mathematics. These specific discourse strategies are referred to as “talk moves” within the mathematics education com- munity and prior research has documented the ways in which systematic use of these discourse strategies can positively impact student engagement and learning. In this article, we describe the TalkMoves application’s cloud-based infrastruc- ture for managing and processing classroom recordings, and its interface for providing teachers with feedback on their use of talk moves during individual teaching episodes. We present the series of model architectures we developed, and the studies we conducted, to develop our best-performing, transformer-based model (F1 = 79.3%). We also discuss sev- eral technical challenges that need to be addressed when working with real-world speech and language data from noisy K-12 classrooms. 

This article describes a professional development (PD) model, the CT-Integration Cycle, that supports teachers in learning to integrate computational thinking (CT) and computer science principles into their middle school science and STEM instruction. The PD model outlined here includes collaborative design (codesign; Voogt et al., 2015) of curricular units aligned with the Next Generation Science Standards (NGSS) that use programmable sensors. Specifically, teachers can develop or modify curricular materials to ensure a focus on coherent, student-driven instruction through the investigation of scientific phenomena that are relevant to students and integrate CT and sensor technology. Teachers can implement these storylines and collaboratively reflect on their instructional practices and student learning. Throughout this process, teachers may develop expertise in CT-integrated science instruction as they plan and use instructional practices aligned with the NGSS and foreground CT. This paper describes an examination of a group of five middle school teachers’ experiences during one iteration of the CT-Integration Cycle, including their learning, planning, implementation, and reflection on a unit they codesigned. Throughout their participation in the PD, the teachers expanded their capacity to engage deeply with CT practices and thoughtfully facilitated a CT-integrated unit with their students. 

This article describes a professional development (PD) model, the CT- Integration Cycle, that supports teachers in learning to integrate computational thinking (CT) and computer science principles into their middle school science and STEM instruction. The PD model outlined here includes collaborative design (codesign; Voogt et al., 2015) of curricular units aligned with the Next Generation Science Standards (NGSS) that use programmable sensors. Specifically, teachers can develop or modify curricular materials to ensure a focus on coherent, student-driven instruction through the investigation of scientific phenomena that are relevant to students and integrate CT and sensor technology. Teachers can implement these storylines and collaboratively reflect on their instructional practices and student learning. Throughout this process, teachers may develop expertise in CT-integrated science instruction as they plan and use instructional practices aligned with the NGSS and foreground CT. This paper describes an examination of a group of five middle school teachers’ experiences during one iteration of the CT- Integration Cycle, including their learning, planning, implementation, and reflection on a unit they codesigned. Throughout their participation in the PD, the teachers expanded their capacity to engage deeply with CT practices and thoughtfully facilitated a CT-integrated unit with their students. 

We describe a professional development model that supports teachers to integrate computational thinking (CT) and computer science principles into middle school science and STEM classes. The model includes the collaborative design (co-design) (Voogt et al., 2015) of storylines or curricular units aligned with the Next Generation Science Standards (NGSS Lead States, 2013) that utilize programmable sensors such as those contained on the micro:bit. Teachers spend several workshops co-designing CT-integrated storylines and preparing to implement them with their own students. As part of this process, teachers develop or modify curricular materials to ensure a focus on coherent, student driven instruction through the investigation of scientific phenomena that are relevant to the students and utilize sensor technology. Teachers implement the storylines and meet to collaboratively reflect on their instructional practices as well as their students’ learning. Throughout this cyclical, multi-year process, teachers develop expertise in CT-integrated science instruction as they plan for and use instructional practices that align with three dimension science teaching and foreground computational thinking. Throughout the professional learning process, teachers alternate between wearing their “student hats” and their “teacher hats”, in order to maintain both a student and teacher perspective as they co-design and reflect on their implementation of CT-integrated units. This paper illustrates two teachers’ experiences of the professional development process over a two-year period, including their learning, planning, implementation, and reflection on two co-designed units.