A Framework for Delivering Contextually Appropriate Opportunities for Warfighter Practice

Computer-based modeling and simulation has been a training staple in the military domain since the first aircraft simulators were adopted. More recently, virtual environments based on modeling, simulation and serious games, have introduced relatively low-cost, yet high value additions to the learning environment. As these virtual environments have proliferated, many researchers have investigated the relationship between theoretical foundations of learning, learner development and content delivery, and applied their findings in an attempt to bolster learning, yet performance deficiencies continue to exist. This study asserts that performance deficiencies exist in part because of insufficient contextually appropriate opportunities to practice. This work is multi-disciplinary in nature. Its foundation is modeling and simulation engineering; the use of technology to deliver training. Educational psychology and human factors concepts explain the theoretical basis for modeling and simulation as an effective training delivery agent. The study\u27s thesis is that a framework for delivering contextually appropriate opportunities for warfighter practice can be applied to discover whether modeling, simulation and game-based virtual environments have the potential to improve individual performance for learners beyond the Novice Stage (e.g., Competent Stage) of skills acquisition. Furthermore, this conceptually appropriate practice (CAP) framework can be used to assess the potential of low fidelity virtual environments to provide targeted practice and to improve individual performance, not only during training in high-fidelity virtual environments (near transfer) but also in the live environment (far transfer). To evaluate the thesis, this study investigates the relationship of technology and learning science, and features an empirical evaluation of training effectiveness afforded by delivering additional training repetitions using both low-fidelity virtual environment simulator systems and high-fidelity aircraft simulators

Assessment-driven Learning through Serious Games: Guidance and Effective Outcomes

Evaluation in serious games is an important aspect; it aims to evaluate the good transmission of pedagogical objectives, the performance of student in relation to these objectives defined in the pedagogical scenario, the content of the course and the predefined criteria. However, the effectiveness of learning is under-studied due to the complexity involved to gamify the assessment concept, particularly when it comes to intangible measures related to the progression of learning outcomes, which is among the most important aspects of evaluation in serious games. This paper reviews the literature regarding assessment due to their importance in the learning process with a detailed assessment plan applied on serious game. Then, it presents a framework used to facilitate the assessment design integrated in serious games. Finally, a significant example of how the proposed framework proved successful with corresponding results will conclude the paper

Design Knowledge for Virtual Learning Companions from a Value-centered Perspective

The increasing popularity of conversational agents such as ChatGPT has sparked interest in their potential use in educational contexts but undermines the role of companionship in learning with these tools. Our study targets the design of virtual learning companions (VLCs), focusing on bonding relationships for collaborative learning while facilitating students’ time management and motivation. We draw upon design science research (DSR) to derive prescriptive design knowledge for VLCs as the core of our contribution. Through three DSR cycles, we conducted interviews with working students and experts, held interdisciplinary workshops with the target group, designed and evaluated two conceptual prototypes, and fully coded a VLC instantiation, which we tested with students in class. Our approach has yielded 9 design principles, 28 meta-requirements, and 33 design features centered around the value-in-interaction. These encompass Human-likeness and Dialogue Management, Proactive and Reactive Behavior, and Relationship Building on the Relationship Layer (DP1,3,4), Adaptation (DP2) on the Matching Layer, as well as Provision of Supportive Content, Fostering Learning Competencies, Motivational Environment, and Ethical Responsibility (DP5-8) on the Service Layer

Understanding the embodied teacher : nonverbal cues for sociable robot learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.Includes bibliographical references (p. 103-107).As robots enter the social environments of our workplaces and homes, it will be important for them to be able to learn from natural human teaching behavior. My research seeks to identify simple, non-verbal cues that human teachers naturally provide that are useful for directing the attention of robot learners. I conducted two novel studies that examined the use of embodied cues in human task learning and teaching behavior. These studies motivated the creation of a novel data-gathering system for capturing teaching and learning interactions at very high spatial and temporal resolutions. Through the studies, I observed a number of salient attention-direction cues, the most promising of which were visual perspective, action timing, and spatial scaffolding. In particular, this thesis argues that spatial scaffolding, in which teachers use their bodies to spatially structure the learning environment to direct the attention of the learner, is a highly valuable cue for robotic learning systems. I constructed a number of learning algorithms to evaluate the utility of the identified cues. I situated these learning algorithms within a large architecture for robot cognition, augmented with novel mechanisms for social attention and visual perspective taking. Finally, I evaluated the performance of these learning algorithms in comparison to human learning data, providing quantitative evidence for the utility of the identified cues. As a secondary contribution, this evaluation process supported the construction of a number of demonstrations of the humanoid robot Leonardo learning in novel ways from natural human teaching behavior.by Matthew Roberts Berlin.Ph.D

Effective learning in a virtual classroom for computer studies in a high school : a case study

M.Ed

Inclusive Intelligent Learning Management System Framework - Application of Data Science in Inclusive Education

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceBeing a disabled student the author faced higher education with a handicap which as experience studying during COVID 19 confinement periods matched the findings in recent research about the importance of digital accessibility through more e-learning intensive academic experiences. Narrative and systematic literature reviews enabled providing context in World Health Organization’s International Classification of Functioning, Disability and Health, legal and standards framework and information technology and communication state-of-the art. Assessing Portuguese higher education institutions’ web sites alerted to the fact that only outlying institutions implemented near perfect, accessibility-wise, websites. Therefore a gap was identified in how accessible the Portuguese higher education websites are, the needs of all students, including those with disabilities, and even the accessibility minimum legal requirements for digital products and the services provided by public or publicly funded organizations. Having identified a problem in society and exploring the scientific base of knowledge for context and state of the art was a first stage in the Design Science Research methodology, to which followed development and validation cycles of an Inclusive Intelligent Learning Management System Framework. The framework blends various Data Science study fields contributions with accessibility guidelines compliant interface design and content upload accessibility compliance assessment. Validation was provided by a focus group whose inputs were considered for the version presented in this dissertation. Not being the purpose of the research to deliver a complete implementation of the framework and lacking consistent data to put all the modules interacting with each other, the most relevant modules were tested with open data as proof of concept. The rigor cycle of DSR started with the inclusion of the previous thesis on Atlântica University Institute Scientific Repository and is to be completed with the publication of this thesis and the already started PhD’s findings in relevant journals and conferences

MASELTOV Deliverable D7.1.1: Incidental Learning Framework

This is the first steps towards creating an ‘Incidental learning framework’, and it is a work in progress. It is intended that the Incidental learning framework will facilitate the creation of technology rich learning opportunities for immigrants within cities. The framework will be a descriptive mechanism that permits analysis, a generative tool to support software system design, and it will facilitate the communication of learning design ideas both visually and textually. The framework focuses on incidental learning i.e. learning that is spontaneous and unplanned, in the knowledge domains of interest to the MASELTOV project including health care, culture, and language and information access. However, it provides links and triggers to structured and reflective learning to back up and deepen learning that happens incidentally. This document describes the initial version of the Incidental Learning Framework, presents a few examples of its use, and describes some conclusions and recommendations for work towards the next version of the framework including • Using the framework to develop it, • Extending the literature review, • Gathering evidence about immigrants day-to-day lives, • Running design workshop(s) using the framework

An Overview of Self-Adaptive Technologies Within Virtual Reality Training

This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Automated haptic interaction can enable tele-presence and virtual artefact tactile interaction from either remote or simulated environments. Automation, machine learning and data driven features play an important role in providing trainee-specific individual adaptive training content. Data from trainee assessment can form an input to autonomous systems for customised training and automated difficulty levels to match individual requirements. Self-adaptive technology has been developed previously within individual technologies of VR training. One of the conclusions of this research is that while it does not exist, an enhanced portable framework is needed and it would be beneficial to combine automation of core technologies, producing a reusable automation framework for VR training

Effects of Static and Dynamic Visuals on the Learning of Science Concepts in the Secondary-School Classroom

The current study is grounded in the cognitive theory of multimedia learning. The investigator considered how embedding science text with visuals could affect secondary-school students\u27 ability to retain the information they learn (rote learning) and transfer the new knowledge to an unfamiliar problem (meaningful learning). Furthermore, this study explored how the type of visuals (static versus vs. dynamic visual) and text (audio vs. print) affect science learning. The data generated was sourced from student participants in a secondary-school biology classroom. The purpose of this study was to investigate how prior knowledge and the integration of information modalities (i.e., text, audio, static visual, dynamic visuals) promotes rote learning (information retention) and meaningful learning (knowledge transfer) in science. The study was used also to investigate how the interaction of prior knowledge, which was coded as expertise level, with information modality effects learning and cognitive load. The study was based on a quasi-experiment that included a pretest, intervention, and posttest phase. The pretest assessed prior knowledge of the subject matter and established a baseline knowledge score. 117 participants were assigned to one of four treatment groups. For Group 1, the learning material was exclusively text. Group 2 had textual information with embedded pictures that corresponded with the concepts in the text. Group 3 had animation with the text subscripted in a video. And, Group 4 was provided with a fully animated version of the video that included audio narration instead of subscripted text. Three sets of response variables were generated from the collected data: (a) rote learning scores, (b) meaningful learning scores, and (c) cognitive load scores. The between-group differences in the response variables were evaluated via analysis of variance (ANOVA) using the SPSS Statistics software package. The ANOVA results revealed statistically significant effects only for rote learning and the cognitive load associated with rote learning. No statistically significant effect was detected for meaningful learning, the learning intervention, and their associated cognitive loads. Furthermore, the interaction of prior knowledge (i.e., learner expertise) with information modality did not have statistically significant effects on any of the responding variables

A Multi-Agent Architecture for An Intelligent Web-Based Educational System

An intelligent educational system must constitute an adaptive system built on multi-agent system architecture. The multi-agent architecture component provides self-organization, self-direction, and other control functionalities that are crucially important for an educational system. On the other hand, the adaptiveness of the system is necessary to provide customization, diversification, and interactional functionalities. Therefore, an educational system architecture that integrates multi-agent functionality [50] with adaptiveness can offer the learner the required independent learning experience. An educational system architecture is a complex structure with an intricate hierarchal organization where the functional components of the system undergo sophisticated and unpredictable internal interactions to perform its function. Hence, the system architecture must constitute adaptive and autonomous agents differentiated according to their functions, called multi-agent systems (MASs). The research paper proposes an adaptive hierarchal multi-agent educational system (AHMAES) [51] as an alternative to the traditional education delivery method. The document explains the various architectural characteristics of an adaptive multi-agent educational system and critically analyzes the system’s factors for software quality attributes