1,723 research outputs found
Inclusive Intelligent Learning Management System Framework
Machado, D. S-M., & Santos, V. (2023). Inclusive Intelligent Learning Management System Framework. International Journal of Automation and Smart Technology, 13(1), [2423]. https://doi.org/10.5875/ausmt.v13i1.2423The article finds context and the current state of the art in a systematic literature review on intelligent systems employing PRISMA Methodology which is complemented with narrative literature review on disabilities, digital accessibility and legal and standards context. The main conclusion from this review was the existing gap between the available knowledge, standards, and law and what is put into practice in higher education institutions in Portugal. Design Science Research Methodology was applied to output an Inclusive Intelligent Learning Management System Framework aiming to help higher education professors to share accessible pedagogic content and deliver on-line and presential classes with a high level of accessibility for students with different types of disabilities, assessing the uploaded content with Web content Accessibility Guidelines 3.0, clustering students according to their profile, conscient feedback and emotional assessment during content consumption, applying predictive models and signaling students at risk of failing classes according to study habits and finally applying a recommender system. The framework was validated by a focus group to which experts in digital accessibility, information systems and a disabled PhD graduate.publishersversionpublishe
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
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
Deep Learning for Fatigue Estimation on the Basis of Multimodal Human-Machine Interactions
The new method is proposed to monitor the level of current physical load and
accumulated fatigue by several objective and subjective characteristics. It was
applied to the dataset targeted to estimate the physical load and fatigue by
several statistical and machine learning methods. The data from peripheral
sensors (accelerometer, GPS, gyroscope, magnetometer) and brain-computing
interface (electroencephalography) were collected, integrated, and analyzed by
several statistical and machine learning methods (moment analysis, cluster
analysis, principal component analysis, etc.). The hypothesis 1 was presented
and proved that physical activity can be classified not only by objective
parameters, but by subjective parameters also. The hypothesis 2 (experienced
physical load and subsequent restoration as fatigue level can be estimated
quantitatively and distinctive patterns can be recognized) was presented and
some ways to prove it were demonstrated. Several "physical load" and "fatigue"
metrics were proposed. The results presented allow to extend application of the
machine learning methods for characterization of complex human activity
patterns (for example, to estimate their actual physical load and fatigue, and
give cautions and advice).Comment: 12 pages, 10 figures, 1 table; presented at XXIX IUPAP Conference in
Computational Physics (CCP2017) July 9-13, 2017, Paris, University Pierre et
Marie Curie - Sorbonne (https://ccp2017.sciencesconf.org/program
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