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In the immediate vicinity of a source, there are strong gradients in the seismic wavefield that are tamed and modified in distributed acoustic sensing (DAS) recording due to combined effects of gauge-length averaging and local stacking on the local strain field. Close to a source broadside propagation effects are significant, and produce a characteristic impact on the local DAS channels. In the presence of topography, of surface or cable, additional effects are introduced that modify the expected signal. All these influences mean that the results of tap tests used to calibrate the channel positions along a DAS cable may give a distorted view of the actual geometry. Such effects can be important for detailed mapping of faulting processes and comparable features.

 

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. 

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. 

At the core‐mantle boundary, most observed ultralow velocity zones (ULVZs) cluster along the edges of the large low shear velocity provinces (LLSVPs) and provide key information on the composition, dynamics, and evolution of the lower mantle. However, their detailed structure near slab‐like structures beneath the mid‐Pacific remains particularly challenging because of the lack of station coverage. While most studies of ULVZs concentrate on SKS‐complexity, here we report on the multipathing of ScS, which expands the sampling for ULVZs. We find the strongest multipathing along a ULVZ patch located just south of Hawaii and the far northeastern edge of the LLSVP, in a zone ~200 km in width and extending 600 km southward. The anomalous ScS travel times and distortedS_diffwaveforms further reveal patches interrupted by observed enhanced D″ indicative of slab‐debris influence on the complexity of the northeastern boundary of the mid‐Pacific LLSVP.