Designing a Peer Assessment for Identification of Students’ Group Work Problems

Group projects are part of the core educational experience in higher education, but many students report bad experiences. Group problems may undermine learning and cause stress and frustration. This may be prevented by monitoring and supporting groups, but this is often not feasible for teachers, who lack time and resources. This research aims to find a method for early identification of group work problems via computer-supported assessment. First, interviews and focus groups provided insights into the most common group problems and which visual features students preferred in a peer assessment. Next, two assessment versions were created: a simple, time-efficient version, and a more engaging, interactive one. We also created an initial version of E-Mate, a virtual agent that provides initial feedback on the assessment. These were tested in a field study. Most students reported a positive experience with the peer assessment, regardless of the visualization used. Teachers were also positive about its usefulness. The research also supports the use of five attributes to assess group collaboration

Symbiotic Child Emotional Support with Social Robots and Temporal Knowledge Graphs

In current youth-care programs, children with needs (mental health, family issues, learning disabilities, and autism) receive support from youth and family experts as one-to-one assistance at schools or hospitals. Occasionally, social robots have featured in such settings as support roles in a one-to-one interaction with the child. In this paper, we suggest the development of a symbiotic framework for real-time Emotional Support (ES) with social robots Knowledge Graphs (KG). By augmenting a domain-specific corpus from the literature on ES for children (between the age of 8 and 12) and providing scenario-driven context including the history of events, we suggest developing an experimental knowledge-aware ES framework. The framework both guides the social robot in providing ES statements to the child and assists the expert in tracking and interpreting the child's emotional state and related events over time

Adaptive Emotional Support for Groups

When faced with a difficult situation, searching for emotional support is one of the most natural things to do. Being supported not only helps regulating the negative emotions, but it also promotes coping skills and psychological adjustment to stressful situations. However, not all emotional support attempts are effective and always available. The increasing usage of technology may offer a solution by providing an emotional support virtual agent, capable to deliver support via smartphone or computer, anytime and anywhere. Such agent may adapt to one’s characteristics and situation, providing supportive feedback tailored to the needs. In my thesis, I will focus on how to provide emotional support to groups of students. Emotional support will be adapted to the stressors that students typically encounter, and to the challenges linked to working in groups. I will study how people adapt emotional support statements to both individual and situational factors, and investigate how this can be implemented in a virtual agent

Empathizing with virtual agents: the effect of personification and general empathic tendencies

For interactions to be natural, virtual agents should understand humans’ emotions, and humans should have emotional reactions towards them. In human-to-human interaction, this is achieved through empathic processes between individuals. So, improving empathic responses towards virtual agents represents a crucial step in improving human-virtual agent interactions. This study aims to identify whether the presence of a personification story and individual differences in the ability to empathize predict the empathic response towards a virtual agent. Furthermore, it investigates the effect of previous experience with virtual agents and gender on empathy towards the virtual agent. In an experiment, participants witnessed a virtual reality scene in which a virtual agent experienced sadness. Half of the participants were previously presented with a personification story about the virtual agent, and all completed a self-report questionnaire about empathy and a post-experiment survey about their empathic response towards the virtual agent. Results showed that individual differences in empathy significantly predict the ability to empathize with the virtual agent: people who are naturally predisposed to feel more empathy towards others tend to be more empathic towards the virtual agent. The personification story, previous experience and participants’ gender did not affect the empathic response. Implications and future direction for the design of virtual agents are discussed

GMAP 2023: 2nd Workshop on Group Modeling, Adaptation and Personalization

Although most existing recommender systems support single users, there are many scenarios where these systems target the needs of groups. Traits such as group mood, emotional contagion, and interpersonal relationships are often ill-defined characteristics, tend to mutate over time, and are usually missing from the systems’ modeling, even though they play an indispensable part in group modeling. Furthermore, producing timely and accurate recommendations for groups that are explainable, fair, and privacy-protecting is a notoriously tricky challenge since group members may have divergent views and needs. The second GMAP workshop aims at bringing together a community of researchers focused on group modeling, adaptation, and personalization. The objective is to explore the challenges and opportunities of developing effective methods and tools to support group decision-making. The workshop, we brought together researchers from several disciplines, including Psychology, Computer Science, and Organizational Behavior, to discuss their latest research and ideas on this topic. It also provided opportunities for participants to share their research and experiences and to collaborate and network with other researchers in this field. The long-term goal is to foster a vibrant and inclusive community of researchers committed to advancing our understanding of group modeling, adaptation, and personalization by bringing together experts from different disciplines and perspectives. Throughout this workshop, we aim to identify critical challenges and opportunities in this area and develop a shared research agenda to guide future work

A cross-cultural comparison on implicit and explicit attitudes towards artificial agents

Historically, there has been a great deal of confusion in the literature regarding cross-cultural differences in attitudes towards artificial agents and preferences for their physical appearance. Previous studies have almost exclusively assessed attitudes using self-report measures (i.e., questionnaires). In the present study, we sought to expand our knowledge on the influence of cultural background on explicit and implicit attitudes towards robots and avatars. Using the Negative Attitudes Towards Robots Scale (NARS) and the Implicit Association Test (IAT) in a Japanese and Dutch sample, we investigated the effect of culture and robots’ body types on explicit and implicit attitudes across two experiments (total n = 669). Partly overlapping with our hypothesis, we found that Japanese individuals had a more positive explicit attitude towards robots compared to Dutch individuals, but no evidence of such a difference was found at the implicit level. As predicted, the implicit preference towards humans was moderate in both cultural groups, but in contrast to what we expected, neither culture nor robot embodiment influenced this preference. These results suggest that only at the explicit but not implicit level, cultural differences appear in attitudes towards robots

Symbiotic Child Emotional Support with Social Robots and Temporal Knowledge Graphs

In current youth-care programs, children with needs (mental health, family issues, learning disabilities, and autism) receive support from youth and family experts as one-to-one assistance at schools or hospitals. Occasionally, social robots have featured in such settings as support roles in a one-to-one interaction with the child. In this paper, we suggest the development of a symbiotic framework for real-time Emotional Support (ES) with social robots Knowledge Graphs (KG). By augmenting a domain-specific corpus from the literature on ES for children (between the age of 8 and 12) and providing scenario-driven context including the history of events, we suggest developing an experimental knowledge-aware ES framework. The framework both guides the social robot in providing ES statements to the child and assists the expert in tracking and interpreting the child's emotional state and related events over time