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We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We design a multi-faceted prompt to extract a textual explanation of the reasoning that connects visible cues to underlying social meanings. These extracted explanations or rationales serve as augmentations to the conversational text to facilitate dialogue understanding and transfer. Our empirical results over 2,340 experimental settings demonstrate the significant positive impact of adding these rationales. Our findings hold true for in-domain classification, zero-shot, and few-shot domain transfer for two different social meaning detection tasks, each spanning two different corpora. 

By aligning the functional components derived from the activations of transformer models trained for AES with external knowledge such as human-understandable feature groups, the proposed method improves the interpretability of a Longformer Automatic Essay Scoring (AES) system and provides tools for performing such analyses on further neural AES systems. The analysis focuses on models trained to score essays based on organization, main idea, support, and language. The findings provide insights into the models’ decision-making processes, biases, and limitations, contributing to the development of more transparent and reliable AES systems. 

Our work aims to increase the collaborative ability of college students in computer science classrooms where students must work towards a shared goal with peers from different backgrounds and abilities. Our work focuses specifically on leveraging high-quality collaborative design to bridge the gap between fiber arts and robotics by enlightening students to their shared foundations in mathematics and computational thinking. We achieve this goal through the design of SPEERLoom (Semi-automated Pattern Executing Educational Robotic Loom), a new open-source Jacquard loom kit designed to foster students' exploration of weaving, mechatronics, mathematics, and computational thinking. In this demonstration we present SPEERLoom and allow the exploration of a sample lesson using the loom. 

Computer science pedagogy, especially in the higher education and vocational training context, has long-favored the hands-on practice provided by programming tasks due to the belief that this leads to better performance on hands-on tasks at work. This assumption, however, has not been experimentally tested against other modes of engagement such as worked example-based reflection. While theory suggests that example-based reflection could be better for conceptual learning, the concern is that the lack of practice will leave students unable to implement the learned concepts in practice, thus leaving them unprepared for work. In this paper, therefore, we experimentally contrast programming practice with example-based reflection to observe their differential impact on conceptual learning and performance on a hands-on task in the context of a collaborative programming project. The industry paradigm of Mob Programming, adapted for use in an online and instructional context, is used to structure the collaboration. Keeping with the prevailing view held in pedagogy, we hypothesize that example-based reflection will lead to better conceptual learning but will be detrimental to hands-on task performance. Results support that reflection leads to conceptual learning. Additionally, however, reflection does not pose an impediment to hands-on task performance. We discuss possible explanations for this effect, thus providing an improved understanding of prior theory in this new computer science education context. We also discuss implications for the pedagogy of software engineering education, in light of this new evidence, that impacts student learning as well as work performance in the future. 

For the past 15 years, in computer-supported collaborative learning applications, conversational agents have been used to structure group interactions in online chat-based environments. A series of experimental studies has provided an empirical foundation for the design of chat based conversational agents that significantly improve learning over no-support control conditions and static-support control conditions. In this demo, we expand upon this foundation, bringing conversational agents to structure group interaction into physical spaces, with the specific goal of facilitating collaboration and learning in workplace scenarios. 

Open-domain Keyphrase extraction (KPE) on the Web is a fundamental yet complex NLP task with a wide range of practical applications within the field of Information Retrieval. In contrast to other document types, web page designs are intended for easy navigation and information finding. Effective designs encode within the layout and formatting signals that point to where the important information can be found. In this work, we propose a modeling approach that leverages these multi-modal signals to aid in the KPE task. In particular, we leverage both lexical and visual features (e.g., size, font, position) at the micro-level to enable effective strategy induction, and metalevel features that describe pages at a macrolevel to aid in strategy selection. Our evaluation demonstrates that a combination of effective strategy induction and strategy selection within this approach for the KPE task outperforms state-of-the-art models. A qualitative post-hoc analysis illustrates how these features function within the model. 

The notion of face refers to the public self-image of an individual that emerges both from the individual’s own actions as well as from the interaction with others. Modeling face and understanding its state changes throughout a conversation is critical to the study of maintenance of basic human needs in and through interaction. Grounded in the politeness theory of Brown and Levinson (1978), we propose a generalized framework for modeling face acts in persuasion conversations, resulting in a reliable coding manual, an annotated corpus, and computational models. The framework reveals insights about differences in face act utilization between asymmetric roles in persuasion conversations. Using computational models, we are able to successfully identify face acts as well as predict a key conversational outcome (e.g. donation success). Finally, we model a latent representation of the conversational state to analyze the impact of predicted face acts on the probability of a positive conversational outcome and observe several correlations that corroborate previous findings. 

Developers of open-source software projects tend to collaborate in bursts of activity over a few days at a time, rather than at an even pace. A project might find its productivity suffering if bursts of activity occur when a key person with the right role or right expertise is not available to participate. Open-source projects could benefit from monitoring the way they orchestrate attention among key developers, finding ways to make themselves available to one another when needed. In commercial software development, Sociotechnical Congruence (STC) has been used as a measure to assess whether coordination among developers is sufficient for a given task. However, STC has not previously been successfully applied to open-source projects, in which some industrial assumptions do not apply: managementchosen targets, mandated steady work hours, and top-down task allocation of inputs and targets. In this work we propose an operationalization of STC for open-source software development. We use temporal bursts of activity as a unit of analysis more suited to the natural rhythms of open-source work, as well as open source analogues of other component measures needed for calculating STC. As an illustration, we demonstrate that opensource development on PyPI projects in GitHub is indeed bursty, that activities in the bursts have topical coherence, and we apply our operationalization of STC. We argue that a measure of socio-technical congruence adapted to open source could provide projects with a better way of tracking how effectively they are collaborating when they come together to collaborate.