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  1. Videos of group interactions contain a wealth of information beyond the information directly communicated in a transcript of the discussion. Tracking who has participated throughout an extended interaction and what each of their trajectories has been in relation to one another is the foundation for joint activity understanding, though it comes with some unique challenges in videos of tightly coupled group work. Motivated by insights into the properties of such scenarios, including group composition and the properties of task-oriented, goal-directed tasks, we present a successful proof-of-concept. In particular, we present a transfer experiment to a dyadic robot construction task, an ablation study, and a qualitative analysis. 
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  2. Jun Oshima, Toshio Mochizuki (Ed.)
    Designing activities for maximizing collaborative learning in advanced computer science contexts is of broad interest. While programming exercises remain the dominant form of pedagogy here, prior work showed that collaborative reflection over worked examples is as good or even better for conceptual learning and future programming. This work used a “phased” design, with separate collaborative reflection and programming phases, and varied the time boundary between the two to determine their differential impact. A more effective design, however, could involve collaborative reflection prompted “in the flow” of programming, with benefits similar to self-explanation prompts interleaved into individual problem-solving. While total time-on-task is the same, this “interleaved” design might allow learners to spend a larger proportion of this time on reflection. Thus, this paper compares this novel interleaved approach to the phased design. We determine that interleaving increases the proportion of time available for reflection resulting in performance improvements on future programming. 
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  3. 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. 
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  4. null (Ed.)
    Modelling persuasion strategies as predictors of task outcome has several real-world applications and has received considerable attention from the computational linguistics community. However, previous research has failed to account for the resisting strategies employed by an individual to foil such persuasion attempts. Grounded in prior literature in cognitive and social psychology, we propose a generalised framework for identifying resisting strategies in persuasive conversations. We instantiate our framework on two distinct datasets comprising persuasion and negotiation conversations. We also leverage a hierarchical sequence-labelling neural architecture to infer the aforementioned resisting strategies automatically. Our experiments reveal the asymmetry of power roles in non-collaborative goal-directed conversations and the benefits accrued from incorporating resisting strategies on the final conversation outcome. We also investigate the role of different resisting strategies on the conversation outcome and glean insights that corroborate with past findings. We also make the code and the dataset of this work publicly available at this https URL. 
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  5. null (Ed.)
    Abstract Professional and lifelong learning are a necessity for workers. This is true both for re-skilling from disappearing jobs, as well as for staying current within a professional domain. AI-enabled scaffolding and just-in-time and situated learning in the workplace offer a new frontier for future impact of AIED. The hallmark of this community’s work has been i) data-driven design of learning technology and ii) machine-learning enabled personalized interventions. In both cases, data are the foundation of AIED research and data-related ethics are thus central to AIED research. In this paper we formulate a vision how AIED research could address data-related ethics issues in informal and situated professional learning. The foundation of our vision is a secondary analysis of five research cases that offer insights related to data-driven adaptive technologies for informal professional learning. We describe the encountered data-related ethics issues. In our interpretation, we have developed three themes: Firstly, in informal and situated professional learning, relevant data about professional learning – to be used as a basis for learning analytics and reflection or as a basis for adaptive systems - is not only about learners. Instead, due to the situatedness of learning, relevant data is also about others (colleagues, customers, clients) and other objects from the learner’s context. Such data may be private, proprietary, or both. Secondly, manual tracking comes with high learner control over data. Thirdly, learning is not necessarily a shared goal in informal professional learning settings. From an ethics perspective, this is particularly problematic as much data that would be relevant for use within learning technologies hasn’t been collected for the purposes of learning. These three themes translate into challenges for AIED research that need to be addressed in order to successfully investigate and develop AIED technology for informal and situated professional learning. As an outlook of this paper, we connect these challenges to ongoing research directions within AIED – natural language processing, socio-technical design, and scenario-based data collection - that might be leveraged and aimed towards addressing data-related ethics challenges. 
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  6. null (Ed.)
    Contributing to the literature on aptitude-treatment interactions between worked examples and problem-solving, this paper addresses differential learning from the two approaches when students are positioned as domain experts learning new concepts. Our evaluation is situated in a team project that is part of an advanced software engineering course. In this course, students who possess foundational domain knowledge but are learning new concepts engage alternatively in programming followed by worked example-based reflection. They are either allowed to finish programming or are curtailed after a pre-specified time to participate in a longer worked example-based reflection. We find significant pre- to post-test learning gains in both conditions. Then, we not only find significantly more learning when students participated in longer worked example-based reflections but also a significant performance improvement on a problem-solving transfer task. These findings suggest that domain experts learning new concepts benefit more from worked example-based reflections than from problem-solving. 
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  7. null (Ed.)
    Knowledge Graph (KG) completion research usually focuses on densely connected benchmark datasets that are not representative of real KGs. We curate two KG datasets that include biomedical and encyclopedic knowledge and use an existing commonsense KG dataset to explore KG completion in the more realistic setting where dense connectivity is not guaranteed. We develop a deep convolutional network that utilizes textual entity representations and demonstrate that our model outperforms recent KG completion methods in this challenging setting. We find that our model’s performance improvements stem primarily from its robustness to sparsity. We then distill the knowledge from the convolutional network into a student network that re-ranks promising candidate entities. This re-ranking stage leads to further improvements in performance and demonstrates the effectiveness of entity re-ranking for KG completion. 
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  8. Dynamic conversational agent-based support for collaborative learning has shown significant positive effects on learning over no-support or static-support control conditions in prior studies. In order to understand the boundary between human-led and AI-led support for collaboration, we compare in this study an approach where the agent’s primary role is to help students regulate their own collaboration with two more typical prompting strategies that are used only during a reflection phase: one designed to provide a specific informational focus for the reflection, and the other designed to draw out evaluation, elaboration, and exploration of alternative perspectives. Significant positive effects on learning over and above just the human-led form of support are observed when either of the prompting strategies are used. 
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  9. 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. 
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  10. Purpose In response to the evolving COVID-19 pandemic, many universities have transitioned to online instruction. With learning promising to be online, at least in part, for the near future, instructors may be thinking of providing online collaborative learning opportunities to their students who are increasingly isolated from their peers because of social distancing guidelines. This paper aims to provide design recommendations for online collaborative project-based learning exercises based on this research in a software engineering course at the university level. Design/methodology/approach Through joint work between learning scientists, course instructors and software engineering practitioners, instructional design best practices of alignment between the context of the learners, the learning objectives, the task and the assessment are actualized in the design of collaborative programming projects for supporting learning. The design, first segments a short real-time collaborative exercise into tasks, each with a problem-solving phase where students participate in collaborative programming, and a reflection phase for reflecting on what they learned in the task. Within these phases, a role-assignment paradigm scaffolds collaboration by assigning groups of four students to four complementary roles that rotate after each task. Findings By aligning each task with granular learning objectives, significant pre- to post-test learning from the exercise as well as each task is observed. Originality/value The roles used in the paradigm discourage divide-and-conquer tendencies often associated with collaborative projects. By requiring students to discuss conflicting ideas to arrive at a consensus implementation, their ideas are made explicit, thus providing opportunities for clarifying misconceptions through discussion and learning from the collaboration. 
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