Nasir, Jauwairia
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Role-playing activities offer opportunities for developing individuals’ creativity, communication, and problem-solving skills. Recent advances in large language models (LLM) facilitate fluent conversations with machines. To investigate benefits and pitfalls of LLMs in a relatively unexplored context of human-agent role-play as a culturally contextualised activity, a dataset of twelve human-agent interactions produced by two researchers with two state-of theart LLMs was annotated based on a frame analysis scheme from literature. The pilot study shows that human-agent play has a similar complexity as human human play in which players maintain identities of themselves, external observers and play characters simultaneously going beyond the pretend-reality dualism. Results suggest that, while the LLMs can maintain and shift between roles, they play some roles better than others, and display cultural and gender stereotypes. Additionally, the coding scheme shows potential to help identify LLM outputs that require embodied enactment, and to be used for LLM bench-marking for role-play.
When designing social robots for educational settings, there is often an emphasis on domain knowledge. This presents challenges: 1) Either robots must autonomously acquire domain knowledge, a currently unsolved problem in HRI, or 2) the designers provide this knowledge implying re-programming the robot for new contexts. Recent research explores alternative, relatively easier to port, knowledge areas like student rapport, engagement, and synchrony though these constructs are typically treated as the ultimate goals, when the final goal should be students’ learning. Our aim is to propose a shift in how engagement is considered, aligning it naturally with learning. We introduce the notion of a skilled ignorant peer robot: a robot peer that has little to no domain knowledge but possesses knowledge of student behaviours conducive to learning, i.e., behaviours indicative of productive engagement as extracted from student behavioral profiles. We formally investigate how such a robot’s interventions manipulate the children’s engagement conducive to learning. Specifically, we evaluate two versions of the proposed robot, namely, Harry and Hermione, in a user study with 136 students where each version differs in terms of the intervention strategy. Harry focuses on which suggestions to intervene with from a pool of communication, exploration, and reflection inducing suggestions, while Hermione also carefully considers when and why to intervene. While the teams interacting with Harry have higher productive engagement correlated to learning, this engagement is not affected by the robot’s intervention scheme. In contrast, Hermione’s well-timed interventions, deemed more useful, correlate with productive engagement though engagement is not correlated to learning. These results highlight the potential of a social educational robot as a skilled ignorant peer and stress the importance of precisely timing the robot interventions in a learning environment to be able to manipulate moderating variable of interest such as productive engagement.
Transactive discussion during collaborative learning is crucial for building on each other's reasoning and developing problem solving strategies. In a tabletop collaborative learning activity, student actions on the interface can drive their thinking and be used to ground discussions, thus affecting their problem-solving performance and learning. However, it is not clear how the interplay of actions and discussions, for instance, how students performing actions or pausing actions while discussing, is related to their learning. In this paper, we seek to understand how the transactivity of actions and discussions is associated with learning. Specifically, we ask what is the relationship between discussion and actions, and how it is different between those who learn (gainers) and those who do not (non-gainers). We present a combined differential sequence mining and content analysis approach to examine this relationship, which we applied on the data from 32 teams collaborating on a problem designed to help them learn concepts of minimum spanning trees. We found that discussion and action occur concurrently more frequently among gainers than non-gainers. Further we find that gainers tend to do more reflective actions along with discussion, such as looking at their previous solutions, than non-gainers. Finally, gainers discussion consists more of goal clarification, reflection on past solutions and agreement on future actions than non-gainers, who do not share their ideas and cannot agree on next steps. Thus this approach helps us identify how the interplay of actions and discussion could lead to learning, and the findings offer guidelines to teachers and instructional designers regarding indicators of productive collaborative learning, and when and how, they should intervene to improve learning. Concretely, the results suggest that teachers should support elaborative, reflective and planning discussions along with reflective actions.
Rapidly Exploring Random Trees (RRT) are regarded as one of the most efficient tools for planning feasible paths for mobile robots in complex obstacle cluttered environments. The recent development of its variant: RRT* is considered as a major breakthrough as it makes it possible to achieve optimality in paths planning. However, its limitations include the infinite time it takes to reach the optimal solution and a very slow rate of convergence. Just recently the authors have introduced RRT*-Smart which is a rapid convergence implementation of RRT* for improved efficient path planning both in terms of planning time as well as path cost. This paper presents a new scheme for RRT*-Smart that helps it to adapt to various types of environments by tuning its parameters during planning based on the information gathered online. The paper also includes detailed explanation of the algorithm�s characteristics and statistical analysis of its behavior in different environment types including mazes, narrow passages and obstacle cluttered environments in comparison with RRT*. Navigation experiments using the real Pioneer 3-AT Mobile Robot provide a proof of the concept.
Applying IDC theory to education in the Alps region: a response to Chan et al.'s contribution
(2019)
In this paper, we present a response to the Interest-Driven Creator (IDC) theory from a European perspective. Specifically, we raise six questions intended to start a dialog with respect to IDC theory’s placement in existing learning theories, its adoption in educational systems, and how it can be influenced by emerging learning technologies and digitalization, which is currently a driving force in the Alps region. By referring to our own work in vocational education and classroom orchestration, we demonstrate how IDC can begin to play a part in guiding innovations and its potential impact on education both in and outside of Asia. With respect to digitalization, rather than allowing technological innovations to fully guide educational decisions, we call for IDC theory to be part of the conversation to help guide future educational designs.
The learning process depends on the nature of the learning environment, particularly in the case of open-ended learning environments, where the learning process is considered to be non-linear. In this paper, we report on the findings of employing a multimodal Hidden Markov Model (HMM) based methodology to investigate the temporal learning processes of two types of learners that have learning gains and a type that does not have learning gains in an open-ended collaborative learning activity. Considering log data, speech behavior, affective states and gaze patterns, we find that all learners start from a similar state of non-productivity, but once out of it they are unlikely to fall back into that state, especially in the case of the learners that have learning gains. Those who have learning gains shift between two problem solving strategies, each characterized by both exploratory and reflective actions, as well as demonstrate speech and gaze patterns associated with these strategies, that differ from those who don't have learning gains. Further, the teams that have learning gains also differ between themselves in the manner in which they employ the problem solving strategies over the interaction, as well as in the manner they express negative emotions while exhibiting a particular strategy. These outcomes contribute to understanding the multiple pathways of learning in an open-ended collaborative learning environment, and provide actionable insights for designing effective interventions.
Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been prposed to overcome the limitaions of RRT*. The goal of the proposecd method is to accelerate the rate of convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*–Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*-Smart algorithm.
Engagement is a concept of the utmost importance in human-computer interaction, not only for informing the design and implementation of interfaces, but also for enabling more sophisticated interfaces capable of adapting to users. While the notion of engagement is actively being studied in a diverse set of domains, the term has been used to refer to a number of related, but different concepts. In fact it has been referred to across different disciplines under different names and with different connotations in mind. Therefore, it can be quite difficult to understand what the meaning of engagement is and how one study relates to another one accordingly. Engagement has been studied not only in human-human, but also in human-agent interactions i.e., interactions with physical robots and embodied virtual agents. In this overview article we focus on different factors involved in engagement studies, distinguishing especially between those studies that address task and social engagement, involve children and adults, are conducted in a lab or aimed for long term interaction. We also present models for detecting engagement and for generating multimodal behaviors to show engagement.
In educational HRI, it is generally believed that a robots behavior has a direct effect on the engagement of a user with the robot, the task at hand and also their partner in case of a collaborative activity. Increasing this engagement is then held responsible for increased learning and productivity. The state of the art usually investigates the relationship between the behaviors of the robot and the engagement state of the user while assuming a linear relationship between engagement and the end goal: learning. However, is it correct to assume that to maximise learning, one needs to maximise engagement? Furthermore, conventional supervised models of engagement require human annotators to get labels. This is not only laborious but also introduces further subjectivity in an already subjective construct of engagement. Can we have machine-learning models for engagement detection where annotations do not rely on human annotators? Looking deeper at the behavioral patterns and the learning outcomes and a performance metric in a multi-modal data set collected in an educational human–human–robot setup with 68 students, we observe a hidden link that we term as Productive Engagement. We theorize a robot incorporating this knowledge will (1) distinguish teams based on engagement that is conducive of learning; and (2) adopt behaviors that eventually lead the users to increased learning by means of being productively engaged. Furthermore, this seminal link paves way for machine-learning models in educational HRI with automatic labelling based on the data.
