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

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Student Modeling in Intelligent Tutoring Systems

After decades of development, Intelligent Tutoring Systems (ITSs) have become a common learning environment for learners of various domains and academic levels. ITSs are computer systems designed to provide instruction and immediate feedback, which is customized to individual students, but without requiring the intervention of human instructors. All ITSs share the same goal: to provide tutorial services that support learning. Since learning is a very complex process, it is not surprising that a range of technologies and methodologies from different fields is employed. Student modeling is a pivotal technique used in ITSs. The model observes student behaviors in the tutor and creates a quantitative representation of student properties of interest necessary to customize instruction, to respond effectively, to engage students¡¯ interest and to promote learning. In this dissertation work, I focus on the following aspects of student modeling. Part I: Student Knowledge: Parameter Interpretation. Student modeling is widely used to obtain scientific insights about how people learn. Student models typically produce semantically meaningful parameter estimates, such as how quickly students learn a skill on average. Therefore, parameter estimates being interpretable and plausible is fundamental. My work includes automatically generating data-suggested Dirichlet priors for the Bayesian Knowledge Tracing model, in order to obtain more plausible parameter estimates. I also proposed, implemented, and evaluated an approach to generate multiple Dirichlet priors to improve parameter plausibility, accommodating the assumption that there are subsets of skills which students learn similarly. Part II: Student Performance: Student Performance Prediction. Accurately predicting student performance is one of the most desired features common evaluations for student modeling. for an ITS. The task, however, is very challenging, particularly in predicting a student¡¯s response on an individual problem in the tutor. I analyzed the components of two common student models to determine which aspects provide predictive power in classifying student performance. I found that modeling the student¡¯s overall knowledge led to improved predictive accuracy. I also presented an approach, which, rather than assuming students are drawn from a single distribution, modeled multiple distributions of student performances to improve the model¡¯s accuracy. Part III: Wheel-spinning: Student Future Failure in Mastery Learning. One drawback of the mastery learning framework is its possibility to leave a student stuck attempting to learn a skill he is unable to master. We refer to this phenomenon of students being given practice with no improvement as wheel-spinning. I analyzed student wheel-spinning across different tutoring systems and estimated the scope of the problem. To investigate the negative consequences of see what wheel-spinning could have done to students, I investigated the relationships between wheel-spinning and two other constructs of interest about students: efficiency of learning and ¡°gaming the system¡±. In addition, I designed a generic model of wheel-spinning, which uses features easily obtained by most ITSs. The model can be well generalized to unknown students with high accuracy classifying mastery and wheel-spinning problems. When used as a detector, the model can detect wheel-spinning in its early stage with satisfying satisfactory precision and recall

Machinima And Video-based Soft Skills Training

Multimedia training methods have traditionally relied heavily on video based technologies and significant research has shown these to be very effective training tools. However production of video is time and resource intensive. Machinima (pronounced \u27muh-sheen-eh-mah\u27) technologies are based on video gaming technology. Machinima technology allows video game technology to be manipulated into unique scenarios based on entertainment or training and practice applications. Machinima is the converting of these unique scenarios into video vignettes that tell a story. These vignettes can be interconnected with branching points in much the same way that education videos are interconnected as vignettes between decision points. This study addressed the effectiveness of machinima based soft-skills education using avatar actors versus the traditional video teaching application using human actors. This research also investigated the difference between presence reactions when using avatar actor produced video vignettes as compared to human actor produced video vignettes. Results indicated that the difference in training and/or practice effectiveness is statistically insignificant for presence, interactivity, quality and the skill of assertiveness. The skill of active listening presented a mixed result indicating the need for careful attention to detail in situations where body language and facial expressions are critical to communication. This study demonstrates that a significant opportunity exists for the exploitation of avatar actors in video based instruction

Fidelity and Game-based Technology in Management Education

This study explores educational technology and management education by analyzing fidelity in game-basedmanagement education interventions. A sample of 31 MBA students was selected to help answer the researchquestion: To what extent do MBA students tend to recognize specific game-based academic experiences, interms of fidelity, as relevant to their managerial performance? Two distinct game-based interventions (BG1 andBG2) with key differences in fidelity levels were explored: BG1 presented higher physical and functional fidelitylevels and lower psychological fidelity levels. Hypotheses were tested with data from the participants, collectedshortly after their experiences, related to the overall perceived quality of game-based interventions. The findingsreveal a higher overall perception of quality towards BG1: (a) better for testing strategies, (b) offering betterbusiness and market models, (c) based on a pace that better stimulates learning, and (d) presenting a fidelity levelthat better supports real world performance. This study fosters the conclusion that MBA students tend torecognize, to a large extent, that specific game-based academic experiences are relevant and meaningful to theirmanagerial development, mostly with heightened fidelity levels of adopted artifacts. Agents must be ready andmotivated to explore the new, to try and err, and to learn collaboratively in order to perform