Nasir, Jauwairia
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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 labeling based on the data.
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.
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.
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.
We have all been one such student or seen such students who can maintain the 'good student' image while playing a video game under the table or those loyal backbenchers, seemingly always distracted, who then ace their exams. These intricacies of human behaviors are just a few examples of what makes it non-trivial and challenging even for expert teachers to know how students' visible behaviors relate with learning. As research investigates ways in which robots and AI can support teachers and students, it is faced with the same challenge of inferring students' engagement; thus, making the investigation of this topic increasingly popular in educational HRI. The state of the art usually explores the relationship between the robot behaviors and the engagement state of the learner while assuming a linear relationship between engagement and learning. However, is it correct to assume that to maximize learning, one needs to maximize engagement? Furthermore, conventional supervised engagement models require human annotators to get labels. This not only is laborious but can also introduce subjectivity. Can we have machine-learning engagement models where annotations do not rely on human annotators? Additionally, with the increase in open-ended learning activities which by design employ the 'learning by failing' paradigm, in-task performance can not be the best measure for learning. Can we instead rely on multi-modal behaviors? In an effort to cater for these challenges, this thesis dives deep to identify and quantify the relationship between learning and engagement, which we term as Productive Engagement (PE). In order to develop, design, and evaluate our PE framework, (1) we first designed and developed an open-ended collaborative learning activity that served as a platform for evaluating different robot variants over time. With 98 children interacting with the baseline version from 2 international Swiss schools, we showed that in-task performance and learning are indeed not correlated. Thus, this showed the importance of not being limited to robot interventions that affect only superficial measures of students' learning. (2) Then, with learner's multi-modal behaviors, we showed that indeed there is a hidden link between learner's behaviors and learning that can be quantified, i.e., validating the proposed concept of Productive Engagement. (3) This quantifiable link surfaced three collaborative multi-modal learner profiles, by using a forward and backward clustering and classification technique, two of which are linked to higher learning. This technique gave a possibility to surface data driven labels for engagement; thus, evading the process of human annotations. We then identified similarities and differences between these learner profiles both at an aggregate and at the temporal level. (4) Based on (3), we constructed a PE score that can either be directly used as an assessment metric by a social robot in real-time or as data driven labels for building more sophisticated regression models. (5) With the learner profiles and the PE score, we designed and evaluated more advanced robot variants for the final studies with ~160 students from 7 international Swiss schools. With the design of different robot variants that employ knowledge about the learner's skills conducive to learning, rather than domain knowledge, in order to provide interventions; we provided a complementary perspective on the role of social robots in educational settings.
Understanding the way learners engage with learning technologies, and its relation with their learning, is crucial for motivating design of effective learning interventions. Assessing the learners’ state of engagement, however, is non-trivial. Research suggests that performance is not always a good indicator of learning, especially with open-ended constructivist activities. In this paper, we describe a combined multi-modal learning analytics and interaction analysis method that uses video, audio and log data to identify multi-modal collaborative learning behavioral profiles of 32 dyads as they work on an open-ended task around interactive tabletops with a robot mediator. These profiles, which we name Expressive Explorers, Calm Tinkerers, and Silent Wanderers, confirm previous collaborative learning findings. In particular, the amount of speech interaction and the overlap of speech between a pair of learners are behavior patterns that strongly distinguish between learning and non-learning pairs. Delving deeper, findings suggest that overlapping speech between learners can indicate engagement that is conducive to learning. When we more broadly consider learner affect and actions during the task, we are better able to characterize the range of behavioral profiles exhibited among those who learn. Specifically, we discover two behavioral dimensions along which those who learn vary, namely, problem solving strategy (actions) and emotional expressivity (affect). This finding suggests a relation between problem solving strategy and emotional behavior; one strategy leads to more frustration compared to another. These findings have implications for the design of real-time learning interventions that support productive collaborative learning in open-ended tasks.
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.
