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Industrial systems can be complex and not intuitive to perceive. Therefore, students in technology and engineering programs can benefit from developing mental models of industrial systems during their journey in college. However, more often than not, these students do not have access to industrial facilities; thus, developing mental models for systems is a challenge. This paper examines the merit of an Immersive Virtual Reality (IVR) framework application in creating proper mental models for industrial systems in technology and engineering students. Two IVR applications were developed. One IVR application afforded interaction with components of a prefabricated industrial cooling water system (CWVR). In the other application, students designed and built industrial systems with IVR (system designer VR [SDVR]). SDVR facilitated constructive-generative engagement. A group of 33 students was divided into two; one group (the Design, experimental group) was tasked with building a system with SDVR and interacting with the cooling water system in CWVR. The other group was tasked with directly interacting with the CWVR without building a system with SDVR (the Interaction, comparison group). Students' mental models of the cooling water system in CWVR were evaluated following completing the interaction experience with CWVR. The results demonstrate that the causal model notion of the mental model of the cooling water system was significantly higher in the Design, experimental group. The results suggest that designing a rich IVR application that facilitates constructive-generative engagements may carry merit in informing student mental models of complex technical concepts.

Students learn when they connect new information to existing understanding or when they modify existing understanding to accept new information. Most current teaching methods focus on trying to get students to solve problems in a manner identical to that of an expert. This study investigates the effectiveness of assessing student understanding related to context specific problem solving decisions, prescribing feedback based on the assessment, and improving student understanding to the point where they can make correct decisions. Students were given a refrigeration problem unlike their prior problems and were asked to draw the cycle on a T-v diagram using a tutor system. Every group tested (a total of 373 students) showed a significant improvement in their understanding (p<0.0001, Cohen's d>0.8) using a single 40 minute tutor activity.

User interface design patterns that document Human-Agent Team (HAT) interface best practices were developed by analyzing team video games with different types of agents, ranging from virtual players in sports games to advisors contributing to strategy and planning in combat games. One factor that may contribute to the enormous success of some games is the cohesive team interactions between humans and agents. A design pattern collection was developed by identifying 25 design patterns under interface themes most relevant to HATs. The design patterns describe how multiple successful game interfaces handle challenging aspects of HAT interaction, addressing contexts including planning, spatial orientation, task management, communication, and more. Based on the complex and well-tested interface solutions in video games, these design patterns can provide HAT user interface developers practical guidance for interface development and a starting point for developing a HAT design pattern language. [ABSTRACT FROM AUTHOR]

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.

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.

The Extensible Problem Specific Tutor (xPST) allows authors who are not cognitive scientists and not programmers to quickly create an intelligent tutoring system that provides instruction akin to a model-tracing tutor. Furthermore, this instruction is overlaid on existing software, so that the learner's interface does not have to be made from scratch. The xPST architecture allows for extending its capabilities by the addition of plug-ins that communicate with additional third-party software. After reviewing this general architecture, we describe three major implementations that we have created using the xPST system, each using different third-party software as the learner's interface. We have conducted three evaluations of authors using xPST to create tutoring content, and these are considered in turn. These evaluations show that xPST authors can quickly learn the system, and can efficiently produce successful embedded instruction.

Intelligent Tutoring Systems (ITSs) that employ a model-tracing methodology have consistently shown their effectiveness. However, what evidently makes these tutors effective, the cognitive model embedded within them, has traditionally been difficult to create, requiring great expertise and time, both of which come at a cost. Furthermore, an interface has to be constructed that communicates with the cognitive model. Together these constitute a high bar that needs to be crossed in order to create such a tutor. We outline a system that lowers this bar on both accounts and that has been used to produce commercial-quality tutors. First, we discuss and evaluate a tool that allows authors who are not cognitive scientists or programmers to create a cognitive model. Second, we detail a way for this cognitive model to communicate with third-party interfaces. (Contains 5 figures and 4 tables.)

Virtual environments (VEs) can be infinitely large, but movement of the virtual reality (VR) user is constrained by the surrounding real environment. Teleporting has become a popular locomotion interface to allow complete exploration of the VE. To teleport, the user selects the intended position (and sometimes orientation) before being instantly transported to that location. However, locomotion interfaces such as teleporting can cause disorientation. This experiment explored whether practice and feedback when using the teleporting interface can reduce disorientation. VR headset owners participated remotely. On each trial of a triangle completion task, the participant traveled along two path legs through a VE before attempting to point to the path origin. Travel was completed with one of two teleporting interfaces that differed in the availability of rotational self-motion cues. Participants in the feedback condition received feedback about their pointing accuracy. For both teleporting interfaces tested, feedback caused significant improvement in pointing performance, and practice alone caused only marginal improvement. These results suggest that disorientation in VR can be reduced through feedback-based training. [ABSTRACT FROM AUTHOR]