Learning through playing for or against each other? Promoting collaborative learning in digital game based learning

The process of learning through Game Based Learning (GBL) presents both positive aspects and challenges to be faced in order to support the achievement of learning goals and knowledge creation. This study aims to characterise game dynamics in the adoption of multi-player GBL. In particular, we examine the multi-player GBL dynamics may enhance collaborative learning through a relation of positive interdependence while at the same time maintaining a certain level of competition for ensuring multi-player GBL gameplay. The first section of the paper introduces collaborative GBL and describes the combination of intragroup dynamics of cooperation and positive interdependence and an intergroup dynamic of competition to maintain gameplay. The second part of the paper describes two multi-player GBL scenarios: the multi-player game with interpersonal competition and the multiplayer game with intergroup competition. For each scenario a case analysis of existing collaborative games is provided, which may help instructional and game designers when defining the collaborative GBL dynamics. Technological requirements and best practices in the use of collaborative GBL are described in the last sections

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Information-rich user embodiment in groupware

Embodiments are virtual personifications of the user in real-time distributed groupware. Many embodiments in groupware are simple abstract 2D representations such as avatars and telepointers. Although current user embodiment techniques can reveal information related to position and orientation, they show far less than what is available in a face-to-face situation, and as a result, collaboration can become more difficult. The problem addressed in this research is that it is difficult for groupware users to recognize and characterize other participants using only their embodiments. The solution explored in this thesis is to provide more information about groupware users by enriching their embodiment. This scheme encodes state and context variables as visual augmentations on the embodiment. Providing information about characteristics such as skill, expertise, and experience can be valuable for collaboration; increasing the information in visual embodiments makes it easier and more natural for collaborators to recognize and characterize others, and thus coordinate activity, simplify communication, and find collaborators. Rich embodiments were tested in three separate experiments. The first experiment showed that users are able to recall a large number of variables displayed on embodiments, and are able to accurately determine the values of those variables. The second study showed that rich embodiments are useful in terms of collaboration and interaction in an actual groupware context – a multiplayer game. The final study further examined information-rich embodiment in a shared drawing task, and further revealed the potential of increasing awareness using embodiment

The Tracer Method: Don\u27t Blink or You Might Miss it. A Novel Methodology Combining Cognitive Task Analysis and Eye Tracking

This thesis describes the development and first demonstration of a new Human Factors method, The Tracer Method, which is a combination of Cognitive Task Analysis (CTA) and Eye Tracking. The study evaluated whether the two methods together produce new and different information than either method alone could provide. The method was tested using a video game, Overwatch, a dynamic, complex, and multiplayer game. The evaluation included: 1. Examining both in the same context (game), 2. Establishing unique contributions of each method alone, and 3. Evaluating overlapping information. Results identified some overlap between the two methods that provided some cross-validation of the data. Cognitive Task Analysis provided higher level strategies and course of actions that players implement during their games, while eye tracking provided visual patterns of search (order of eye movements). However, when combined, the two methods provide strategy information in context that neither method alone can provide. CTA elicits insight into how individuals make decisions and apply previous knowledge, experience, and environmental information. Eye tracking can support this through predictive models of individual’s eye tracking, to understand which elements are utilized in making predictions and situational assessments. We provide a tutorial and insight into best practices for implementation of The Tracer Method. This is the initial development of the new method, and on-going research is validating it in different environments. The Tracer Method is the first combined and documented systematic methodology that utilizes a changing and complicated environment and tests the interaction and output of Critical Decision Method and Eye Tracking

Structuring AI Teammate Communication: An Exploration of AI\u27s Communication Strategies in Human-AI Teams

In the past decades, artificial intelligence (AI) has been implemented in various domains to facilitate humans in their work, such as healthcare and the automotive industry. Such application of AI has led to increasing attention on human-AI teaming, where AI closely collaborates with humans as a teammate. AI as a teammate is expected to have the ability to coordinate with humans by sharing task-related information, predicting other teammates’ behaviors, and progressing team tasks accordingly. To complete these team activities effectively, AI teammates must communicate with humans, such as sharing updates and checking team progress. Even though communication is a core element of teamwork that helps to achieve effective coordination, how to design and structure human-AI communication in teaming environments still remains unclear. Given the context-dependent characteristics of communication, research on human-AI teaming communication needs to narrow down and focus on specific communication elements/components, such as the proactivity of communication and communication content. In doing so, this dissertation explores how AI teammates’ communication should be structured by modifying communication components through three studies, each of which details a critical component of effective AI communication: (1) communication proactivity, (2) communication content (explanation), and (3) communication approach (verbal vs. non-verbal). These studies provide insights into how AI teammates’ communication ii can be integrated into teamwork and how to design AI teammate communication in human-AI teaming. Study 1 explores an important communication element, communication proactivity, and its impact on team processes and team performance. Specifically, communication proactivity in this dissertation refers to whether an AI teammate proactively communicates with human teammates, i.e., proactively pushing information to human teammates. Experimental analysis shows that AI teammates’ proactive communication plays a crucial role in impacting human perceptions, such as perceived teammate performance and satisfaction with the teammate. Importantly, teams with a non-proactive communication AI teammate increase team performance more than teams with a proactive communication AI as the human and the AI collaborate more. This study identifies the positive impact of AI being proactive in communication at the initial stage of task coordination, as well as the potential need for AI’s flexibility in their communication proactivity (i.e., once human and AI teammates’ coordination pattern forms, AI can be non-proactive in communication). Study 2 examines communication content by focusing on AI’s explanation and its impact on human perceptions in teaming environments. Results indicate that AI’s explanation, as part of communication content, does not always positively impact human trust in human-AI teaming. Instead, the impact of AI’s explanations on human perceptions depends on specific collaboration scenarios. Specifically, AI’s explanations facilitate trust in the AI teammate when explaining why AI disobeys humans’ orders, but hinder trust when explaining why AI lies to humans. In addition, AI giving an explanation of why they ignored the human teammate’s injury was perceived to be more effective than AI not providing such an explanation. The findings emphasize the context-dependent characteristic of AI’s communication content with a focus on AI’s explanation of their actions. iii Study 3 investigates AI’s communication approach, which was manipulated as verbal vs. non-verbal communication. Results indicate that AI teammates’ verbal/nonverbal communication does not impact human trust in the AI teammate, but facilitates the maintenance of humans’ situation awareness in task coordination. In addition, AI with non-verbal communication is perceived as having lower communication quality and lower performance. Importantly, AI with non-verbal communication has better team performance in human-human-AI teams than human-AI-AI teams, whereas AI with verbal communication has better team performance in human-AI-AI teams than human-human-AI teams. These three studies together address multiple research gaps in human-AI team communication and provide a holistic view of the design and structure of AI’s communication by examining three specific aspects of communication in human-AI teaming. In addition, each study in this dissertation proposes practical design implications on AI’s communication in human-AI teams, which will assist AI designers and developers to create better AI teammates that facilitate humans in teaming environments