More Like this

1. Toward Understanding the Effects of Cognitive Styles on Collaboration in Multiplayer Games

   https://doi.org/10.1145/3272973.3274047  

   Alharthi, Sultan A. ; Raptis, George E. ; Katsini, Christina ; Dolgov, Igor ; Nacke, Lennart E. ; Toups, Zachary O. ( November 2018 , Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW '18))

   In multiplayer games, players need to coordinate action to succeed. This paper investigates the effect of cognitive styles on performance of dyads engaged in collaborative gaming activities. 24 individuals took part in a mixed methods user-study; they were classified as field dependent (FD) or independent (FI) based on a cognitive style elicitation instrument. Three groups of teams were formed, based on the cognitive style of each team member: FD-FD, FD-FI, FI-FI. We examined performance in terms of game completion time, cognitive load, and player experience. The analysis revealed that FD-FI cognitive style had an effect on the performance and the mental load. We expect the findings to provide useful insight for practitioners and researchers on improving team collaboration in different contexts, such as learning, eSports, and disaster response. 

   more » « less
2. A new measure for team performance: Measuring inter-brain synchrony of engineering students when designing and building

   Aruon, A. ; Shealy, T. ; Gero, J. ( June 2023 , Engineering project organization journal)

   This research paper examines the patterns of inter-brain synchrony among engineering student teams and the relationship between inter-brain synchrony and team cooperation and performance. A pilot study was conducted with eight two-person teams of fourth-year undergraduate civil engineering students. Three collaborative design and build tasks were assigned to each team. Two independent raters carried out the behavioral analysis, scoring team cooperation. Each team member wore a functional near-infrared spectroscopy (fNIRS) device to measure inter-brain synchrony during the tasks. The results showed that inter-brain synchrony occurred during the team task, but the patterns varied between groups and tasks. Elevated levels of inter-brain synchrony were observed in the left ventrolateral prefrontal cortex (VLPFC) and left dorsolateral prefrontal cortex (DLPFC). The left VLPFC and left DLPFC are often associated with cognitive processes such as problem-solving, working memory, decision-making, and coordinated verbal exchange. Inter-brain synchrony was positively correlated with task performance and cooperation when teams were asked to design and build a structure given limited time and money but negatively correlated with cooperation and performance on other more open-ended design sketching tasks. The study’s findings suggest that inter-brain synchrony exists when engineering students work together as a team, but the results are inconsistent between task types. Inter-brain synchrony could be a useful metric for measuring team cooperation and performance, particularly in tasks that require coordinated verbal exchange, problemsolving, and decision-making. However, the study’s small sample size limits the generalizability of the results. Future studies with a larger sample size and more diverse groups of engineers are needed to validate the findings and explore their implications further. 

   more » « less
3. UNDERSTANDING SPEAKER ROLES ON A NATURALISTIC CORPUS: FEARLESS STEPS APOLLO 11

   Chandra-Shekar, M.M. ; Hansen, J.H.L. ( February 2023 , NASA Human Research Program Investigators Conference)

   INTRODUCTION: Apollo-11 (A-11) was the first manned space mission to successfully bring astronauts to the moon and return them safely. Effective team based communications is required for mission specialists to work collaboratively to learn, engage, and solve complex problems. As part of NASA’s goal in assessing team and mission success, all vital speech communications between these personnel were recorded using the multi-track SoundScriber system onto analog tapes, preserving their contribution in the success of one of the greatest achievements in human history. More than +400 personnel served as mission specialists/support who communicated across 30 audio loops, resulting in +9k hours of data for A-11. To ensure success of this mission, it was necessary for teams to communicate, learn, and address problems in a timely manner. Previous research has found that compatibility of individual personalities within teams is important for effective team collaboration of those individuals. Hence, it is essential to identify each speaker’s role during an Apollo mission and analyze group communications for knowledge exchange and problem solving to achieve a common goal. Assessing and analyzing speaker roles during the mission can allow for exploring engagement analysis for multi-party speaker situations. METHOD: The UTDallas Fearless steps Apollo data is comprised of 19,000 hours (A-11,A-13,A-1) possessing unique and multiple challenges as it is characterized by severe noise and degradation as well as overlap instances over the 30 channels. For our study, we have selected a subset of 100 hours manually transcribed by professional annotators for speaker labels. The 100 hours are obtained from three mission critical events: 1. Lift-Off (25 hours) 2. Lunar-Landing (50 hours) 3. Lunar-Walking (25 hours). Five channels of interest, out of 30 channels were selected with the most speech activity, the primary speakers operating these five channels are command/owners of these channels. For our analysis, we select five speaker roles: Flight Director (FD), Capsule Communicator (CAPCOM), Guidance, Navigation and, Control (GNC), Electrical, environmental, and consumables manager (EECOM), and Network (NTWK). To track and tag individual speakers across our Fearless Steps audio dataset, we use the concept of ‘where’s Waldo’ to identify all instances of our speakers-of-interest across a cluster of other speakers. Also, to understand speaker roles of our speaker-of-interests, we use speaker duration of primary speaker vs secondary speaker and speaker turns as our metrics to determine the role of the speaker and to understand their responsibility during the three critical phases of the mission. This enables a content linking capability as well as provide a pathway to analyzing group engagement, group dynamics of people working together in an enclosed space, psychological effects, and cognitive analysis in such individuals. IMPACT: NASA’s Apollo Program stands as one of the most significant contributions to humankind. This collection opens new research options for recognizing team communication, group dynamics, and human engagement/psychology for future deep space missions. Analyzing team communications to achieve such goals would allow for the formulation of educational and training technologies for assessment of STEM knowledge, task learning, and educational feedback. Also, identifying these personnel can help pay tribute and yield personal recognition to the hundreds of notable engineers and scientist who made this feat possible. ILLUSTRATION: In this work, we propose to illustrate how a pre-trained speech/language network can be used to obtain powerful speaker embeddings needed for speaker diarization. This framework is used to build these learned embeddings to label unique speakers over sustained audio streams. To train and test our system, we will make use of Fearless Steps Apollo corpus, allowing us to effectively leverage a limited label information resource (100 hours of labeled data out of +9000 hours). Furthermore, we use the concept of 'Finding Waldo' to identify key speakers of interest (SOI) throughout the Apollo-11 mission audio across multiple channel audio streams. 

   more » « less
4. Exploring the dynamic interactions and cognitive characteristics of NSF Innovation Corps (I-Corps™) teams

   Jablokow, K. ( June 2018 , Proc. of the ASEE 2018 Annual Conference & Exposition)

   In this pilot study, we used the Interaction Dynamics Notation (IDN), originally designed for use with engineering design teams, to explore the dynamic interactions of five NSF I-Corps™ teams engaged in a simple design activity. Our aim was to relate these interaction data to selected cognitive characteristics of the team members, as well as team design outcomes and individual perceptions related to the experience. The individual cognitive characteristics we assessed focused on cognitive style, as measured by the Kirton Adaption-Innovation inventory (KAI), while team outcomes included the novelty, usefulness, and feasibility of each team’s design solutions, as well as their success within and beyond the NSF I-Corps™ program. Our findings show that the Interaction Dynamics Notation (IDN) can be readily extended to the study of entrepreneurial teams, with important insights gained from the combined study of interaction dynamics, individual cognitive characteristics as measured by KAI, and team outcomes. The results of this study demonstrate the feasibility and value of this approach for investigating the dynamic interactions of NSF I-Corps™ teams, as well as product-focused design teams in general. 

   more » « less
5. Intelligence Augmentation for Collaborative Learning

   Roschelle, Jeremy ( January 2021 , HCI International)

   null (Ed.)

   Today’s classrooms are remarkably different from those of yesteryear. In place of individual students responding to the teacher from neat rows of desks, one more typically finds students working in groups on projects, with a teacher circulating among groups. AI applications in learning have been slow to catch up, with most available technologies focusing on personalizing or adapting instruction to learners as isolated individuals. Meanwhile, an established science of Computer Supported Collaborative Learning has come to prominence, with clear implications for how collaborative learning could best be supported. In this contribution, I will consider how intelligence augmentation could evolve to support collaborative learning as well as three signature challenges of this work that could drive AI forward. In conceptualizing collaborative learning, Kirschner and Erkens (2013) provide a useful 3x3 framework in which there are three aspects of learning (cognitive, social and motivational), three levels (community, group/team, and individual) and three kinds of pedagogical supports (discourse-oriented, representation-oriented, and process-oriented). As they engage in this multiply complex space, teachers and learners are both learning to collaborate and collaborating to learn. Further, questions of equity arise as we consider who is able to participate and in which ways. Overall, this analysis helps us see the complexity of today’s classrooms and within this complexity, the opportunities for augmentation or “assistance to become important and even essential. An overarching design concept has emerged in the past 5 years in response to this complexity, the idea of intelligent augmentation for “orchestrating” classrooms (Dillenbourg, et al, 2013). As a metaphor, orchestration can suggest the need for a coordinated performance among many agents who are each playing different roles or voicing different ideas. Practically speaking, orchestration suggests that “intelligence augmentation” could help many smaller things go well, and in doing so, could enable the overall intention of the learning experience to succeed. Those smaller things could include helping the teacher stay aware of students or groups who need attention, supporting formation of groups or transitions from one activity to the next, facilitating productive social interactions in groups, suggesting learning resources that would support teamwork, and more. A recent panel of AI experts identified orchestration as an overarching concept that is an important focus for near-term research and development for intelligence augmentation (Roschelle, Lester & Fusco, 2020). Tackling this challenging area of collaborative learning could also be beneficial for advancing AI technologies overall. Building AI agents that better understand the social context of human activities has broad importance, as does designing AI agents that can appropriately interact within teamwork. Collaborative learning has trajectory over time, and designing AI systems that support teams not just with a short term recommendation or suggestion but in long-term developmental processes is important. Further, classrooms that are engaged in collaborative learning could become very interesting hybrid environments, with multiple human and AI agents present at once and addressing dual outcome goals of learning to collaborate and collaborating to learn; addressing a hybrid environment like this could lead to developing AI systems that more robustly help many types of realistic human activity. In conclusion, the opportunity to make a societal impact by attending to collaborative learning, the availability of growing science of computer-supported collaborative learning and the need to push new boundaries in AI together suggest collaborative learning as a challenge worth tackling in coming years. 

   more » « less