574 research outputs found
Innovative Learning Environments in STEM Higher Education
As explored in this open access book, higher education in STEM fields is influenced by many factors, including education research, government and school policies, financial considerations, technology limitations, and acceptance of innovations by faculty and students. In 2018, Drs. Ryoo and Winkelmann explored the opportunities, challenges, and future research initiatives of innovative learning environments (ILEs) in higher education STEM disciplines in their pioneering project: eXploring the Future of Innovative Learning Environments (X-FILEs). Workshop participants evaluated four main ILE categories: personalized and adaptive learning, multimodal learning formats, cross/extended reality (XR), and artificial intelligence (AI) and machine learning (ML). This open access book gathers the perspectives expressed during the X-FILEs workshop and its follow-up activities. It is designed to help inform education policy makers, researchers, developers, and practitioners about the adoption and implementation of ILEs in higher education
AI in Learning: Designing the Future
AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers
Collaborative trails in e-learning environments
This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future
AI in Learning: Designing the Future
AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers
Enhancing Free-text Interactions in a Communication Skills Learning Environment
Learning environments frequently use gamification to enhance user interactions.Virtual characters with whom players engage in simulated conversations often employ prescripted dialogues; however, free user inputs enable deeper immersion and higher-order cognition. In our learning environment, experts developed a scripted scenario as a sequence of potential actions, and we explore possibilities for enhancing interactions by enabling users to type free inputs that are matched to the pre-scripted statements using Natural Language Processing techniques. In this paper, we introduce a clustering mechanism that provides recommendations for fine-tuning the pre-scripted answers in order to better match user inputs
An Actor-Centric Approach to Facial Animation Control by Neural Networks For Non-Player Characters in Video Games
Game developers increasingly consider the degree to which character animation emulates facial expressions found in cinema. Employing animators and actors to produce cinematic facial animation by mixing motion capture and hand-crafted animation is labor intensive and therefore expensive. Emotion corpora and neural network controllers have shown promise toward developing autonomous animation that does not rely on motion capture. Previous research and practice in disciplines of Computer Science, Psychology and the Performing Arts have provided frameworks on which to build a workflow toward creating an emotion AI system that can animate the facial mesh of a 3d non-player character deploying a combination of related theories and methods. However, past investigations and their resulting production methods largely ignore the emotion generation systems that have evolved in the performing arts for more than a century. We find very little research that embraces the intellectual process of trained actors as complex collaborators from which to understand and model the training of a neural network for character animation. This investigation demonstrates a workflow design that integrates knowledge from the performing arts and the affective branches of the social and biological sciences. Our workflow begins at the stage of developing and annotating a fictional scenario with actors, to producing a video emotion corpus, to designing training and validating a neural network, to analyzing the emotion data annotation of the corpus and neural network, and finally to determining resemblant behavior of its autonomous animation control of a 3d character facial mesh. The resulting workflow includes a method for the development of a neural network architecture whose initial efficacy as a facial emotion expression simulator has been tested and validated as substantially resemblant to the character behavior developed by a human actor
A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness
People increasingly use videos on the Web as a source for learning. To
support this way of learning, researchers and developers are continuously
developing tools, proposing guidelines, analyzing data, and conducting
experiments. However, it is still not clear what characteristics a video should
have to be an effective learning medium. In this paper, we present a
comprehensive review of 257 articles on video-based learning for the period
from 2016 to 2021. One of the aims of the review is to identify the video
characteristics that have been explored by previous work. Based on our
analysis, we suggest a taxonomy which organizes the video characteristics and
contextual aspects into eight categories: (1) audio features, (2) visual
features, (3) textual features, (4) instructor behavior, (5) learners
activities, (6) interactive features (quizzes, etc.), (7) production style, and
(8) instructional design. Also, we identify four representative research
directions: (1) proposals of tools to support video-based learning, (2) studies
with controlled experiments, (3) data analysis studies, and (4) proposals of
design guidelines for learning videos. We find that the most explored
characteristics are textual features followed by visual features, learner
activities, and interactive features. Text of transcripts, video frames, and
images (figures and illustrations) are most frequently used by tools that
support learning through videos. The learner activity is heavily explored
through log files in data analysis studies, and interactive features have been
frequently scrutinized in controlled experiments. We complement our review by
contrasting research findings that investigate the impact of video
characteristics on the learning effectiveness, report on tasks and technologies
used to develop tools that support learning, and summarize trends of design
guidelines to produce learning video
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