Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding

In this study, we present a data-driven generative design approach that can augment human creativity in product shape design with the objective of improving system performance. The approach consists of two modules: 1) a 3D mesh generative design module that can generate part-aware 3D objects using variational auto-encoder (VAE), and 2) a low-fidelity evaluation module that can rapidly assess the engineering performance of 3D objects based on locally linear embedding (LLE). This approach has two unique features. First, it generates 3D meshes that can better capture surface details (e.g., smoothness and curvature) given individual parts’ interconnection and constraints (i.e., part-aware), as opposed to generating holistic 3D shapes. Second, the LLE-based solver can assess the engineering performance of the generated 3D shapes to realize real-time evaluation. Our approach is applied to car design to reduce air drag for optimal aerodynamic performance

Military Team Training Utilizing GIFT

In 2015, the U.S. Army identified intelligent tutoring as a crucial resource for effective training of soldiers. Specifically, team training is essential as military missions are usually team-based and require extensive coordination. Intelligent Tutoring Systems (ITS) review actions taken by the user and provide dynamic instructions to teach subject matter to an individual. A team ITS assesses the performance of the teams’ individuals, their overall performance as a team, and the interactions of that team to provide dynamic instructions. While extensive work has been conducted regarding single person ITSs, work regarding team-based ITS is limited. A team ITS is difficult to design as the tutor must account for the actions of multiple individuals and their team interactions. The tutor must teach task skills for completing the objective, and team skills for how a team works to meet the objective. This paper describes the implementation, development and evaluation of a Team Intelligent Tutoring System for military teams. We faced challenges such as defining the appropriate levels of cognitive load and team communication required to be successful. The goal of the work was to evaluate an ITS’s effectiveness in a simple team training scenario, a two-person surveillance task in which participants signaled each other using keystrokes. The scenario was constructed using Virtual Battle Space 2.0 (VBS2), and the tutor was built using the Generalized Framework for Tutoring (GIFT). Sixteen two-person teams were run through the study in one of three feedback conditions (individual feedback, team feedback, or no feedback). Their individual and team performance within the task were assessed. We found that participants in the feedback conditions had fewer extraneous keystrokes in the task than those without feedback

Creating a Team Tutor Using GIFT

With the movement in education towards collaborative learning, it is becoming more important that learners be able to work together in groups and teams. Intelligent tutoring systems (ITSs) have been used successfully to teach individuals, but so far only a few ITSs have been used for the purpose of training teams. This is due to the difficulty of creating such systems. An ITS for teams must be able to assess complex interactions between team members (team skills) as well as the way they interact with the system itself (task skills). Assessing team skills can be difficult because they contain social components such as communication and coordination that are not readily quantifiable. This article addresses these difficulties by developing a framework to guide the authoring process for team tutors. The framework is demonstrated using a case study about a particular team tutor that was developed using a military surveillance scenario for teams of two. The Generalized Intelligent Framework for Tutoring (GIFT) software provided the team tutoring infrastructure for this task. A new software architecture required to support the team tutor is described. This theoretical framework and the lessons learned from its implementation offer conceptual scaffolding for future authors of ITSs

Designing Adaptive Instruction for Teams: a Meta-Analysis

The goal of this research was the development of a practical architecture for the computer-based tutoring of teams. This article examines the relationship of team behaviors as antecedents to successful team performance and learning during adaptive instruction guided by Intelligent Tutoring Systems (ITSs). Adaptive instruction is a training or educational experience tailored by artificially-intelligent, computer-based tutors with the goal of optimizing learner outcomes (e.g., knowledge and skill acquisition, performance, enhanced retention, accelerated learning, or transfer of skills from instructional environments to work environments). The core contribution of this research was the identification of behavioral markers associated with the antecedents of team performance and learning thus enabling the development and refinement of teamwork models in ITS architectures. Teamwork focuses on the coordination, cooperation, and communication among individuals to achieve a shared goal. For ITSs to optimally tailor team instruction, tutors must have key insights about both the team and the learners on that team. To aid the modeling of teams, we examined the literature to evaluate the relationship of teamwork behaviors (e.g., communication, cooperation, coordination, cognition, leadership/coaching, and conflict) with team outcomes (learning, performance, satisfaction, and viability) as part of a large-scale meta-analysis of the ITS, team training, and team performance literature. While ITSs have been used infrequently to instruct teams, the goal of this meta-analysis make team tutoring more ubiquitous by: identifying significant relationships between team behaviors and effective performance and learning outcomes; developing instructional guidelines for team tutoring based on these relationships; and applying these team tutoring guidelines to the Generalized Intelligent Framework for Tutoring (GIFT), an open source architecture for authoring, delivering, managing, and evaluating adaptive instructional tools and methods. In doing this, we have designed a domain-independent framework for the adaptive instruction of teams

On Predicting Learning Styles in Conversational Intelligent Tutoring Systems using Fuzzy Decision Trees

Intelligent Tutoring Systems personalise learning for students with different backgrounds, abilities, behaviours and knowledge. One way to personalise learning is through consideration of individual differences in preferred learning style. OSCAR is the name of a Conversational Intelligent Tutoring System that models a person's learning style using natural language dialogue during tutoring in order to dynamically predict, and personalise, their tutoring session. Prediction of learning style is undertaken by capturing independent behaviour variables during the tutoring conversation with the highest value variable determining the student's learning style. A weakness of this approach is that it does not take into consideration the interactions between behaviour variables and, due to the uncertainty inherently present in modelling learning styles, small differences in behaviour can lead to incorrect predictions. Consequently, the learner is presented with tutoring material not suited to their learning style. This paper proposes a new method that uses fuzzy decision trees to build a series of fuzzy predictive models combining these variables for all dimensions of the Felder Silverman Learning Styles model. Results using live data show the fuzzy models have increased the predictive accuracy of OSCAR-CITS across four learning style dimensions and facilitated the discovery of some interesting relationships amongst behaviour variables

Analysis of Communication, Team Situational Awareness, and Feedback in a Three-Person Intelligent Team Tutoring System

This research assessed how the performance and team skills of three-person teams working with an Intelligent Team Tutoring System (ITTS) on a virtual military surveillance task were affected by feedback privacy, participant role, task experience, prior team experience, and teammate familiarity. Previous work in Intelligent Tutoring Systems (ITSs) has focused on outcomes for task skill training for individual learners. As research extends into intelligent tutoring for teams, both task skills and team skills are necessary for good team performance. This work includes a brief review of previous research on ITTSs, feedback, teams, and teamwork, including the recounting of two categories of a framework of teamwork performance, Communication and Cognition, which are relevant to the present study. This research examines the effects of an intelligent agent, as well as features of the team, its members, and the task being undertaken, on team communication (measured by relevant key-presses) and team situation awareness (as measured by scores on a quiz). Thirty-seven teams of three participants, each at their own computer running a multiplayer surveillance simulation, were given just-in-time private (individually delivered) or public (team-delivered) performance feedback during four 5-min trials. In the fourth trial, two of the three participants switched roles. Feedback type, teamwork experience, and teammate familiarity had no statistically significant effect on communication or team situation awareness. However, higher levels of role experience and task experience showed significant and medium-sized effects on communication performance. Results, based on performance data and structured interview responses, also revealed areas of improvement in future feedback design and a potential benchmark for feedback frequency in an action-oriented serious game-based ITTS. Among the conclusions are six design objectives for future ITTSs, establishing a foundation for future research on designing effective ITTSs that train interpersonal skills to nascent teams

Producing Acoustic-Prosodic Entrainment in a Robotic Learning Companion to Build Learner Rapport

abstract: With advances in automatic speech recognition, spoken dialogue systems are assuming increasingly social roles. There is a growing need for these systems to be socially responsive, capable of building rapport with users. In human-human interactions, rapport is critical to patient-doctor communication, conflict resolution, educational interactions, and social engagement. Rapport between people promotes successful collaboration, motivation, and task success. Dialogue systems which can build rapport with their user may produce similar effects, personalizing interactions to create better outcomes. This dissertation focuses on how dialogue systems can build rapport utilizing acoustic-prosodic entrainment. Acoustic-prosodic entrainment occurs when individuals adapt their acoustic-prosodic features of speech, such as tone of voice or loudness, to one another over the course of a conversation. Correlated with liking and task success, a dialogue system which entrains may enhance rapport. Entrainment, however, is very challenging to model. People entrain on different features in many ways and how to design entrainment to build rapport is unclear. The first goal of this dissertation is to explore how acoustic-prosodic entrainment can be modeled to build rapport. Towards this goal, this work presents a series of studies comparing, evaluating, and iterating on the design of entrainment, motivated and informed by human-human dialogue. These models of entrainment are implemented in the dialogue system of a robotic learning companion. Learning companions are educational agents that engage students socially to increase motivation and facilitate learning. As a learning companion’s ability to be socially responsive increases, so do vital learning outcomes. A second goal of this dissertation is to explore the effects of entrainment on concrete outcomes such as learning in interactions with robotic learning companions. This dissertation results in contributions both technical and theoretical. Technical contributions include a robust and modular dialogue system capable of producing prosodic entrainment and other socially-responsive behavior. One of the first systems of its kind, the results demonstrate that an entraining, social learning companion can positively build rapport and increase learning. This dissertation provides support for exploring phenomena like entrainment to enhance factors such as rapport and learning and provides a platform with which to explore these phenomena in future work.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Intelligent Team Tutoring: An analysis of communication, cognition, cooperation, and coordination

This thesis describes the evaluation of an Intelligent Team Tutoring System (ITTS) designed to teach team and task skills to improve team and individual performance. Previous work has revealed how team communication, shared situational awareness and mental models, and collective efficacy contribute to the success of a team and how these phenomena are molded by the team members’ interactions. However, less research has explored the impacts of an ITTS on these dimensions of teamwork. The present study was conducted on 37 teams of three who took on one of two roles – spotter (two people) or sniper – in a military-style task. The teams completed three trials in their original roles, then one spotter and the sniper switched roles in the fourth trial. Additionally, individuals either received public or private automated feedback from the ITTS on their performance in the task. Results were mixed. Role experience contributed to the mental model or shared situational awareness of that role as it was defined in training, but not to increased similarity of mental models among teammates. Public feedback positively influenced, although only marginally, the percentage of accurately timed communications and was significantly related to lower overall missed communication actions. Individuals’ performance was also influenced by the frequency of video game play and the amount of team experience, but only for certain actions. Collective efficacy was impacted by an interaction between experience with cooperative gameplay and frequency of video gaming, where individuals with low gaming frequency but high cooperative gameplay experience had significantly lower collective efficacy than low gamers with no or low co-op experience. Lastly, performance errors were related to individuals’ self-reported use of the feedback, in that ignoring the feedback negatively impacted performance, but selectively following the feedback improved performance. Given previous literature on team dynamics and ITSs, these results are largely unexpected but suggest the feedback style had less impact than was predicted. One team dynamic, collective efficacy, was also shown to be impacted by video game team experience in unanticipated ways, indicating that video game experience and team game experience are indirectly influential to team performance. This research enables the designers of future ITTSs to consider the effects of feedback on coordination and communication tasks more carefully and highlights the importance of the design principle of ensuring a transparent mapping between the feedback and the behavioral triggers that led to it