Assessment of Driver Behavior Based on Machine Learning Approaches in a Social Gaming Scenario

The estimation of user performance analytics in the area of car driver performance was carried out in this paper. The main focus relies on the descriptive analysis with our approaches emphasizing on educational serious games, in order to improvise the driver\u2019s behavior (specifically green driving) in a pleasant and challenging way. We also propose a general Internet of the Things (IoT) social gaming platform (SGP) concept that could be adaptable and deployable to any kind of application domain. The social gaming scenario in this application enables the users to compete with peers based on their physical location. The efficient drivers will be awarded with virtual coins and gained virtual coins can be used in real world applications (such as purchasing travel tickets, reservation of parking lots, etc.). This research work is part of TEAM project co-funded within the EU FP7 ICT research program

The design and implementation of serious games for driving and mobility

The automotive and transportation sectors are showing consistent improvements in trends and standards concerning the safe and convenient travel of the road users. In this growing community of road users, the driver performance is a notable factor as many on-road mishaps emerge out of poor driver performance. In this research work, a case-study and experimental analysis were conducted to improve driver performance through the deployment of serious games. The primary motive of this work is to stimulate the on-road user performance through immediate feedback, driver coaching, and real-time gamification methods. The games exploit the cloud-based architecture to retrieve the driver performance scores based on real-time evaluation of vehicle signals and display the outcomes on game scene by reflecting the game parameters based on real-world user performance (in the context of driving and mobility). The deployment of games in cars is the topic of interest in current state-of-the-art, as there are more factors associated with it, such as safety, usability, and willingness of the users. These aspects were taken into careful consideration while designing the paradigm of gamification model. The user feedback for the real-time games was extracted through pilot tests and field tests in Genova. The gamification and driver coaching aspects were tested on various occasions (plug-in and field tests conducted at 5 European test sites), and the inputs from these field tests enabled to tune the parameters concerning the evaluation and gamification models. The improvement of user behavior was performed through a virtuous cycle with the integration of virtual sensors to the serious gaming framework. As the culmination, the usability tests for the real-time games were conducted with 18 test users to understand the user acceptance criteria and the parameters (ease of use and safety) that would contribute to the deployment of games. Other salient factors such as the impact of games, large-scale deployment, collaborative gaming and exploitation of gaming framework for 3rd party applications were also investigated in this research activity. The analysis of the usability tests states that the user acceptance of the implemented games is good. The report from usability study has addressed the user preferences in games such as duration, strategy and gameplay mechanism; these factors contribute a foundation for future research in implementing the games for mobility

A gamification framework demonstrating a complete cycle of vehicle driver performance evaluation

Training through a gamified environment motivates the users in achieving optimal outcome and reduces the complexity of learning by adding factor of entertainment in it. The deployment of serious games in automotive industry is a major leap in technological grounds, as it\u2019s a best way to inculcate safe driving patterns to reduce the fatalities and enhance resource usage which includes car accessories and fuel. The Ph.D. thesis represents Gamification platform aimed to Green Mobility and Safe Driving

Informaticology: combining Computer Science, Data Science, and Fiction Science

Motivated by an intention to remedy current complications with Dutch terminology concerning informatics, the term informaticology is positioned to denote an academic counterpart of informatics where informatics is conceived of as a container for a coherent family of practical disciplines ranging from computer engineering and software engineering to network technology, data center management, information technology, and information management in a broad sense. Informaticology escapes from the limitations of instrumental objectives and the perspective of usage that both restrict the scope of informatics. That is achieved by including fiction science in informaticology and by ranking fiction science on equal terms with computer science and data science, and framing (the study of) game design, evelopment, assessment and distribution, ranging from serious gaming to entertainment gaming, as a chapter of fiction science. A suggestion for the scope of fiction science is specified in some detail. In order to illustrate the coherence of informaticology thus conceived, a potential application of fiction to the ontology of instruction sequences and to software quality assessment is sketched, thereby highlighting a possible role of fiction (science) within informaticology but outside gaming

ProsocialLearn: D2.3 - 1st system requirements and architecture

This document present the first version of the ProsocialLearn architecture covering the principle definition, the requirement collection, the “business”, “information system”, “technology” architecture as defined in the TOGAF methodology

Computer-Mediated Communication

This book is an anthology of present research trends in Computer-mediated Communications (CMC) from the point of view of different application scenarios. Four different scenarios are considered: telecommunication networks, smart health, education, and human-computer interaction. The possibilities of interaction introduced by CMC provide a powerful environment for collaborative human-to-human, computer-mediated interaction across the globe

Characterizing Productive Perseverance Using Sensor-Free Detectors of Student Knowledge, Behavior, and Affect

Failure is a necessary step in the process of learning. For this reason, there has been a myriad of research dedicated to the study of student perseverance in the presence of failure, leading to several commonly-cited theories and frameworks to characterize productive and unproductive representations of the construct of persistence. While researchers are in agreement that it is important for students to persist when struggling to learn new material, there can be both positive and negative aspects of persistence. What is it, then, that separates productive from unproductive persistence? The purpose of this work is to address this question through the development, extension, and study of data-driven models of student affect, behavior, and knowledge. The increased adoption of computer-based learning platforms in real classrooms has led to unique opportunities to study student learning at both fine levels of granularity and longitudinally at scale. Prior work has leveraged machine learning methods, existing learning theory, and previous education research to explore various aspects of student learning. These include the development of sensor-free detectors that utilize only the student interaction data collected through such learning platforms. Building off of the considerable amount of prior research, this work employs state-of-the-art machine learning methods in conjunction with the large scale granular data collected by computer-based learning platforms in alignment with three goals. First, this work focuses on the development of student models that study learning through the use of advancements in student modeling and deep learning methodologies. Second, this dissertation explores the development of tools that incorporate such models to support teachers in taking action in real classrooms to promote productive approaches to learning. Finally, this work aims to complete the loop in utilizing these detector models to better understand the underlying constructs that are being measured through their application and their connection to productive perseverance and commonly-observed learning outcomes

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