Collaborative Task Performance Research Articles

Exploring the Influence of Virtual Avatar Heads in Mixed Reality on Social Presence, Performance and User Experience in Collaborative Tasks.

In Mixed Reality (MR), users' heads are largely (if not completely) occluded by the MR Head-Mounted Display (HMD) they are wearing. As a consequence, one cannot see their facial expressions and other communication cues when interacting locally. In this paper, we investigate how displaying virtual avatars' heads on-top of the (HMD-occluded) heads of participants in a Video See-Through (VST) Mixed Reality local collaborative task could improve their collaboration as well as social presence. We hypothesized that virtual heads would convey more communicative cues (such as eye direction or facial expressions) hidden by the MR HMDs and lead to better collaboration and social presence. To do so, we conducted a between-subject study ($\mathrm{n}=88$) with two independent variables: the type of avatar (CartoonAvatar/RealisticAvatar/NoAvatar) and the level of facial expressions provided (HighExpr/LowExpr). The experiment involved two dyadic communication tasks: (i) the "20-question" game where one participant asks questions to guess a hidden word known by the other participant and (ii) a urban planning problem where participants have to solve a puzzle by collaborating. Each pair of participants performed both tasks using a specific type of avatar and facial animation. Our results indicate that while adding an avatar's head does not necessarily improve social presence, the amount of facial expressions provided through the social interaction does have an impact. Moreover, participants rated their performance higher when observing a realistic avatar but rated the cartoon avatars as less uncanny. Taken together, our results contribute to a better understanding of the role of partial avatars in local MR collaboration and pave the way for further research exploring collaboration in different scenarios, with different avatar types or MR setups.

The Results of a Virtual Exchange (VE) Project Carried Out by Universities from Three Different Countries

The VE project was implemented with the cooperation of three universities: Budapest Business University, University of Applied Sciences, Hungary; University of Sfax, Tunisia; and Izmir Democracy University, Turkey, in 2023. The main objective of the Virtual Exchange project was to provide students with opportunities for intercultural and interactional development through the performance of collaborative intercultural tasks. The further aim of the project was to provide students with international intercultural experience and to foster mutual understanding, global education, and digital literacy. We focused significantly on creating joint activities that encouraged international students to communicate in ways that enabled them to grasp knowledge beyond what they could merely acquire from websites or books. Both a preliminary survey and a follow-up survey were conducted among the students involved from the three universities, exploring their initial expectations and cultural perspectives before and after the project. The present study summarises the details and the experiences of the implementation of the Virtual Exchange Project, while another paper presents the results of the questionnaires in more detail.

AI-Assisted Human Teamwork

Effective teamwork translates to fewer preventable errors and higher task performance in collaborative tasks. However, in time-critical tasks, successful teamwork becomes highly challenging to attain. In such settings, often, team members have partial observability of their surroundings, incur high cost of communication, and have trouble estimating the state and intent of their teammates. To assist a team in improving teamwork at task time, my doctoral research proposes an automated task-time team intervention system. Grounded in the notion of shared mental models, the system first detects whether the team is on the same page or not. It then generates effective interventions to improve teamwork. Additionally, by leveraging past demonstrations to learn a model of team behavior, this system minimizes the need for domain experts to specify teamwork models and rules.

The impact of rubrics and scripts on self-regulation, self-efficacy and performance in collaborative problem-solving tasks

Rubrics and scripts are tools that can promote self-assessment. Although similar, rubrics and scripts differ in the metacognitive processes they support, their cognitive demands, how students use them and their focus. While there is ample evidence of the positive effects of rubrics, there is less research on the impact of scripts and their use in collaborative tasks. We aimed to compare the effects of rubrics and scripts on self-regulation, self-efficacy, academic performance and students’ perceptions when used in group tasks on econometrics. A total of 134 university students participated in a quasi-experiment under three conditions (rubrics, scripts, control). Positive effects on self-regulation were found for the experimental groups, and a positive impact on self-efficacy only for the rubric group. Additionally, as previous academic performance differed among conditions, difference-in-differences analysis was employed. In the rubric condition, the students that started from very high levels of academic achievement benefited more from using the rubric. In contrast, in the script condition, it was the students with intermediate or high levels of academic performance who experienced the greatest benefits from employing the script.

Distributed Absolute and Relative Estimation of Spacecraft Formations

For optimal decision making and collaborative task performance in multispacecraft missions, it is essential for each spacecraft to maintain a state estimate of all the spacecraft in the formation. However, especially in the case of large formation sizes, each spacecraft may not be able to track or communicate with all other spacecraft. Further, for formations deployed in deep space, the unavailability of the Global Navigation Satellite System makes inertial state estimation challenging. We propose the Distributed Absolute and Relative Estimation (DARE) algorithm for autonomous inertial estimation of spacecraft formations. The algorithm enables each spacecraft to maintain an accurate inertial estimate of the entire formation even in the presence of observability and communication constraints. Each spacecraft utilizes measurements of feature points (landmarks) for inertial localization and measurements of neighboring spacecraft for relative localization. The algorithm is distributed and scalable to any number of spacecraft in the formation. A modified version of the algorithm called the Sparse Distributed Absolute and Relative Estimation (SDARE) algorithm is also derived. This algorithm is computationally more efficient at the expense of estimation accuracy, making it suitable for implementation on nanosatellites where resources are limited. Numerical simulation results demonstrating the effectiveness and comparing the performance of both algorithms are provided.

Physiological Synchrony Predict Task Performance and Negative Emotional State during a Three-Member Collaborative Task.

Evaluation of team performance in naturalistic contexts has gained popularity during the last two decades. Among other human factors, physiological synchrony has been adopted to investigate team performance and emotional state when engaged in collaborative team tasks. A variety of methods have been reported to quantify physiological synchrony with a varying degree of correlation with the collaborative team task performance and emotional state, reflected in the inconclusive nature of findings. Little is known about the effect of the choice of synchrony calculation methods and the level of analysis on these findings. In this research work, we investigate the relationship between outcomes of different methods to quantify physiological synchrony, emotional state, and team performance of three-member teams performing a collaborative team task. The proposed research work employs dyadic-level linear (cross-correlation) and team-level non-linear (multidimensional recurrence quantification analysis) synchrony calculation measures to quantify task performance and the emotional state of the team. Our investigation indicates that the physiological synchrony estimated using multidimensional recurrence quantification analysis revealed a significant negative relationship between the subjectively reported frustration levels and overall task performance. However, no relationship was found between cross-correlation-based physiological synchrony and task performance. The proposed research highlights that the method of choice for physiological synchrony calculation has direct impact on the derived relationship of team task performance and emotional states.

Using Agent Features to Influence User Trust, Decision Making and Task Outcome during Human-Agent Collaboration

Optimal performance of collaborative tasks requires consideration of the interactions between intelligent agents and their human counterparts. The functionality and success of these agents lie in their ability to maintain user trust; with too much or too little trust leading to over-reliance and under-utilisation, respectively. This problem highlights the need for an appropriate trust calibration methodology with an ability to vary user trust and decision making in-task. An online experiment was run to investigate whether stimulus difficulty and the implementation of agent features by a collaborative recommender system interact to influence user perception, trust and decision making. Agent features are changes to the Human-Agent interface and interaction style, and include presentation of a disclaimer message, a request for more information from the user and no additional feature. Signal detection theory is utilised to interpret decision making, with this applied to assess decision making on the task, as well as with the collaborative agent. The results demonstrate that decision change occurs more for hard stimuli, with participants choosing to change their initial decision across all features to follow the agent recommendation. Furthermore, agent features can be utilised to mediate user decision making and trust in-task, though the direction and extent of this influence is dependent on the implemented feature and difficulty of the task. The results emphasise the complexity of user trust in Human-Agent collaboration, highlighting the importance of considering task context in the wider perspective of trust calibration.

Quantitative analysis of team communication for maritime collaborative task performance improvement

Poor communication may cause serious maritime accidents. In order to improve crews' collaborative task performance through team communication optimization, the present study implements a combination of social network analysis and speech-act coding analysis to quantitatively characterizes team communication. Twenty-one teams of eighty-four participants were recruited for an experiment in which they were instructed to perform three kinds of simulated collaborative tasks that are typical for maritime crews. Team communication was recorded using cameras and expert observation. The participants were divided into two groups according to task performance, and an independent sample t-test was used for group verification. The experimental results show that the high task performance group had a significantly shorter task completion time than that of the low task performance group. Besides, there existed significant differences in the communication mode adopted by the two groups. Compared with the low task performance group, the high task performance group exhibited a denser and more decentralised communication structure. They also had a stronger perception, deeper understanding, and greater prediction of communication content, which indicates better situation awareness. This study provides a theoretical basis and supporting data for improving crews’ collaborative task performance according to the quantitative analysis of team communication.

Hand-Eye-Object Tracking for Human Intention Inference

Optimizing human-robot interaction in the performance of collaborative tasks is a challenging problem. An important aspect of this problem relates to the robot's understanding of human intentions. Empowering robots with accurate intention inference capabilities sets the stage for more natural, safe, and effcient interactions and greater confidence in the Human-Robot Interaction domain. Intentions can be deduced by observing human cues as they interact with the environment, but currently, there is no clear-cut method for achieving this in an effective way. Here, we present a novel method for intention inference based on the integration of three visual cues, namely, hand movement, eye fixation, and object interaction, coupled with a bidirectional LSTM neural network for the classification of human intention. Experimental studies evaluating our approach against two-visual cue alternatives confirm the utility of our approach.

Task-Oriented Data Compression for Multi-Agent Communications Over Bit-Budgeted Channels

Various applications for inter-machine communications are on the rise. Whether it is for autonomous driving vehicles or the internet of everything, machines are more connected than ever to improve their performance in fulfilling a given task. While in traditional communications the goal has often been to reconstruct the underlying message, under the emerging task-oriented paradigm, the goal of communication is to enable the receiving end to make more informed decisions or more precise estimates/computations. Motivated by these recent developments, in this paper, we perform an indirect design of the communications in a multi-agent system (MAS) in which agents cooperate to maximize the averaged sum of discounted one-stage rewards of a collaborative task. Due to the bit-budgeted communications between the agents, each agent should efficiently represent its local observation and communicate an abstracted version of the observations to improve the collaborative task performance. We first show that this problem can be approximated as a form of data-quantization problem which we call task-oriented data compression (TODC). We then introduce the state-aggregation for information compression algorithm (SAIC) to solve the formulated TODC problem. It is shown that SAIC is able to achieve near-optimal performance in terms of the achieved sum of discounted rewards. The proposed algorithm is applied to a geometric consensus problem and its performance is compared with several benchmarks. Numerical experiments confirm the promise of this indirect design approach for task-oriented multi-agent communications.

The Underlying neural mechanisms of interpersonal situations on collaborative ability: A hyperscanning study using functional near-infrared spectroscopy

ABSTRACT The collaborative ability to coordinate an individual with others is critical to performance of joint actions. Prior studies found that different types of interpersonal situations have more or less impact on the collaborative ability of joint actions, but the results are controversial. To clarify the influence of interpersonal situations on collaborative ability, we adopted the joint Simon task, a choice-reaction task that two people perform together. Functional near-infrared spectroscopy (fNIRS) was used to study the neural mechanisms of interpersonal situations on collaborative ability and task performance under payoffs that fostered competition or cooperation. The fNIRS results showed that significant inter-brain neural synchronization (INS) occurred in the bilateral inferior parietal lobule (IPL) for both situations. Moreover, for the competition situation, the pairs also shown a significant INS in the right IPL. These results imply that the bilateral IPL is involved in cooperation and competition due to involvement of common concern and understanding of intention. The right IPL may be more crucial for competition because of the psychological resources involved in distinguishing self and others. Eventually, the INS in competition was better than in the other situations, correlating with higher performance of the joint task as well.

Effect of Cognitive Fatigue, Operator Sex, and Robot Assistance on Task Performance Metrics, Workload, and Situation Awareness in Human-Robot Collaboration

Advancements in robot technology are allowing for increasing integration of humans and robots in shared space manufacturing processes. While individual task performance of the robotic assistance and human operator can be separately optimized, the interaction between humans and robots can lead to emergent effects on collaborative performance. Thus, the performance benefits of increased automation in robotic assistance and its impact by human factors need to be considered. As such, this letter examines the interplay of operator sex, their cognitive fatigue states, and varying levels of automation on collaborative task performance, operator situation awareness and perceived workload, and physiological responses (heart rate variability; HRV). Sixteen participants, balanced by sex, performed metal polishing tasks directly with a UR10 collaborative robot under different fatigued states and with varying levels of robotic assistance. Perceived fatigue, situation awareness, and workload were measured periodically, in addition to continuous physiological monitoring and three task performance metrics: task efficiency, accuracy, and precision, were obtained. Higher robotic assistance demonstrated direct task performance benefits. However, unlike females, males did not perceive the performance benefits as better with higher automation. A relationship between situation awareness and automation was observed in both the HRV signals and subjective measures, where increased robot assistance reduced the attentional supply and task engagement of participants. The consideration of the interplay between human factors, such as operator sex and their cognitive states, and robot factors on collaborative performance can lead to improved human-robot collaborative system designs.

Examining the social influence of reputation for partner productivity level on the collaborative task performance of young children

AbstractHumans adjust behaviour in the presence of others in a phenomenon called social influence, which can be categorized into social facilitation (promotional effects) and social loafing (inhibitory effects). The study examined whether the productivity level of the partner in individual and collaborative tasks produced a social influence on children's task performance. The participants were aged 4‐ to 6‐years‐old and grouped as high‐productivity, low‐productivity, and control according to the productivity level of the partners. Experiments 1 and 2 assigned adults and young peers as partners, respectively. Children's performance in the collaborative task decreased compared with the individual task. This effect was unobserved when the adult partner was highly productive but occurred regardless of productivity level when the partner was a peer. Positive correlations were also observed between children's and partners' performance in both experiments. The results thus suggested that young children adjust their behaviour based on the partner's actions.Highlights The social influence of partner's productivity level on young children's task performance was examined during a collaborative task. Children performed individual and collaborative tasks with an adult (Experiment 1) or child partner (Experiment 2). Positive correlations were observed between children's and partners' performance in both experiments.

Are Two Voices Better Than One? Comparing Aspects of Text Quality and Authorial Voice in Paired and Independent L2 Writing

Research has shown that collaboratively produced texts are better in quality compared with individually written texts. However, no study has considered the role of collaboration in authorial voice, which is an essential element in current writing curricula. This study analyzes the effects of collaborative task performance in the quality of L2 learners’ argumentative texts and in their authorial voice strength. A total of 306 upper-intermediate L2 learners were selected and divided into independent ( N = 130) and paired ( N = 176) groups. Each learner/pair was asked to write one argumentative text. The quality of writings was determined by a quantitative analysis that included three measures of complexity, accuracy, and fluency (CAF). Participants’ authorial voice strength was assessed by two raters using an analytic voice rubric. Comparison of means revealed that pairs outperformed independent writers in all CAF measures. However, the results for the role of collaboration in authorial voice were mixed: While pairs were more successful than independent writers in manifesting their ideational voice, independent writers outperformed pairs with regard to affective and presence voice dimensions and holistic voice scores. The article concludes that, despite its positive implications for L2 writing, collaborative writing may pose challenges for learners’ authorial stance taking.

Students’ use of social media in collaborative design: a case study of an advanced interior design studio

Social media, essential in everyday activity, have been frequently used in the performance of collaborative tasks as well as in collaborative learning in academia due to their interactive and open nature. Thus, understanding design students’ selective use and evaluation of social media in collaborative design is worthwhile. This paper reports a case study of design students’ use of social media in the collaborative design process in an advanced interior design studio class. A total of 27 junior interior design students at a university in Korea participated in the study, engaging in a semester-long team project in pairs or trios. At the end of the semester, each team participated in (a) an open-ended survey questionnaire about the types of social media they used and the pros and cons of each and (b) a design charrette to suggest an ideal platform supportive of collaboration. The results show that students (a) used social media in design collaboration primarily for the purposes of searching, sharing, and communicating; (b) frequently used social media that supported more than one purpose; and (c) engaged in three design processes, comprising identification of issues, concept development, and design development. In addition, the features students needed most were (a) ease of management and classification of resources and data, (b) ease of sharing design task files, and (c) seamless integration of the social media in searching, sharing, and communicating. The results of the study will help design educators understand students’ behaviors with social media in a collaborative design studio and needs for more effective design collaboration.

ConTaCT: Deciding to Communicate during Time-Critical Collaborative Tasks in Unknown, Deterministic Domains

Communication between agents has the potential to improve team performance of collaborative tasks. However, communication is not free in most domains, requiring agents to reason about the costs and benefits of sharing information. In this work, we develop an online, decentralized communication policy, ConTaCT, that enables agents to decide whether or not to communicate during time-critical collaborative tasks in unknown, deterministic environments. Our approach is motivated by real-world applications, including the coordination of disaster response and search and rescue teams. These settings motivate a model structure that explicitly represents the world model as initially unknown but deterministic in nature, and that de-emphasizes uncertainty about action outcomes. Simulated experiments are conducted in which ConTaCT is compared to other multi-agent communication policies, and results indicate that ConTaCT achieves comparable task performance while substantially reducing communication overhead.

Dual Reality for Production Verification Workshops: A Comprehensive Set of Virtual Methods

In automotive industry, planning of manual assembly is getting ever-increasing complex, diverse and variant-rich due to ever-increasing market demands for more models and derivatives with shorter life-cycles. In order to reduce the costs for building physical prototypes before ramp-up processes, we present a comprehensive set of virtual and augmented reality methods for real-time assessments of manual assembly tasks used in interdisciplinary production planning workshops. This novel mixed reality assessment system unifies innovative interaction concepts with display technologies from a variety of domains. True-to-scale floor projections, interactive tangible tabletops, powerwalls and head mounted displays are used in combination with markerless full body motion capture and motion controller interfaces. Therewith, production planners in workshop situations are enabled to collaboratively plan and optimize station layouts, author 3D scenes and assess product and process related topics, such as buildability, reachability, assembly and disassembly routines. An in-depth evaluation on collaborative task performance using differing visualization scenarios is presented and discussed.

Collaborative L2 Interactivity in Diverse ZPD-Based Proximal Contexts and Interactional Competence Development

Central to Vygotsky-inspired sociocultural theory (SCT) is the notion of ZPD seen by SLA researchers as a useful framework within which L2 teaching and learning can take place. Van Lier (2004) argues that the ZPD could be activated in diverse proximal contexts (PCs) and is not limited to the expert-novice scenario. . This study probed whether Iranian EFL learners' collaborative task performance within different ZPD-based PCs results in their development of interactional competence (IC). Young's (2011) IC model was used for developing an IC test (ICT) employed at the pretest and posttest times. Three intact EFL listening-speaking classes at a university at southwest of Iran were randomly assigned to the expert-novice, equal-peers, and control (non-ZPD) conditions. The co-constructed interactions of the groups in (a)symmetrical ZPD-based conditions were audio-recorded for further analysis of participants' L2 IC development. A triangulation (quantitative and microgenetic) data-analysis approach was adopted. The results showed that whereas both ZPD groups (equal-peers and expert-novice) outperformed the non-ZPD group on IC posttests, no statistically significant difference was found between participants' IC development in symmetrical and asymmetrical PCs. Further, microgenetic analysis of the ZPD groups' interactions demonstrated how participation and activity in different PCs can effectively trigger awareness of mechanisms and norms of L2 spoken interaction and, in turn, result in IC development. The findings point to the applicability of diverse ZPD-adjusted PCs, composed of equal or unequal participants, in EFL classrooms and also the efficacy of ZPD-based interactive scenarios for students' development of L2 IC.

The Effects of Audio and Haptic Feedback on Collaborative Scanning and Placing

This paper presents a study aimed at exploring the effects of different modality combinations on collaborative task performance and employed joint task-solving strategies in a shared interface. The ...

Collaborative Versus Individual Learning Experiences in Virtual Education: The Effects of a Time Variable

In this study we analyzed a time effect variable on the performance of collaborative versus individual tasks. Thirty-one postgraduate students carried out two online collaborative learning tasks and two online individual tasks as part of a university course. The results of an ANOVA did not show significant effects of time or task type variables, although the effect of interaction was significant. At the beginning of the course (T1) the students’ performance was significantly higher in the collaborative tasks, while at the end of the course (T2) this trend was reversed, with the students’ performance in individual tasks being significantly higher than in the collaborative tasks. The results suggest implications for when to incorporate collaborative learning tasks in virtual learning environments.