The use of tagging to support the authoring of personalisable learning content

This research project is interested in the area of personalised and adaptable learning and in particular within an e-learning context. Brusilovsky (1996) and Santally (2005) stress the importance of adaptive systems within e-learning. Karagiannikis and Sampson et al. (2004) argue that personalised learning systems can be defined by their capability to adapt automatically to the changing attitudes of the “learning experience” which can, in turn, be defined by the individual learner characteristics, for example the type of learning material. The project evolved to cover areas including personalised learning, e-learning environments, authoring tools, tagging, learning objects, learning theories and learning styles. The main focus at the start of the project was to provide a personalised and adaptable learning environment for students based on their learning style. During the research, this led to a specific interest about how an academic can create, tag and author learning objects to provide the capability of personalised adaptable e-learning for a learner. Research undertaken was designed to gain an understanding of personalised and adaptive learning techniques, e-learning tools and learning styles. Important findings of this research showed that e-learning platforms do not offer much in the way of a personalised learning experience for a learner. Additionally, the research showed that general adaptive systems and adaptive systems incorporating learning styles are not commonly used or available due to issues with flexibility, reuse and integration. The concept of tagging was investigated during the research and it was found that tagging is underused within e-learning, although the research shows that it could be a good ‘fit’ within e-learning. This therefore led to the decision to create a general purpose discriminatory tagging methodology to allow authors to tag learning objects for personalisation and reuse. The main focus for the evaluation of this tagging methodology was the authoring side of the tagging. It was found that other research projects have evaluated the personalisation of learning content based on a learner’s learning style (see Graf and Kinshuk (2007)). It was therefore felt that there was a sufficient body of existing evidence in this area whereas there was limited research available on the authoring side. The evaluation of the discriminatory tagging methodology demonstrated that the methodology could allow for any discrimination between learners to be used. The example demonstrated within this thesis includes discriminating according to a learner’s learning style and accessibility type. This type of platform independent flexible discriminatory methodology does not exist within current e-learning platforms or other e-learning systems. Therefore, the main contribution of this thesis is therefore a platform independent general-purpose discriminatory tagging methodology

A context aware recommender system for tourism with ambient intelligence

Recommender system (RS) holds a significant place in the area of the tourism sector. The major factor of trip planning is selecting relevant Points of Interest (PoI) from tourism domain. The RS system supposed to collect information from user behaviors, personality, preferences and other contextual information. This work is mainly focused on user’s personality, preferences and analyzing user psychological traits. The work is intended to improve the user profile modeling, exposing relationship between user personality and PoI categories and find the solution in constraint satisfaction programming (CSP). It is proposed the architecture according to ambient intelligence perspective to allow the best possible tourist place to the end-user. The key development of this RS is representing the model in CSP and optimizing the problem. We implemented our system in Minizinc solver with domain restrictions represented by user preferences. The CSP allowed user preferences to guide the system toward finding the optimal solutions; RESUMO O sistema de recomendação (RS) detém um lugar significativo na área do sector do turismo. O principal fator do planeamento de viagens é selecionar pontos de interesse relevantes (PoI) do domínio do turismo. O sistema de recomendação (SR) deve recolher informações de comportamentos, personalidade, preferências e outras informações contextuais do utilizador. Este trabalho centra-se principalmente na personalidade, preferências do utilizador e na análise de traços fisiológicos do utilizador. O trabalho tem como objetivo melhorar a modelação do perfil do utilizador, expondo a relação entre a personalidade deste e as categorias dos POI, assim como encontrar uma solução com programação por restrições (CSP). Propõe-se a arquitetura de acordo com a perspetiva do ambiente inteligente para conseguir o melhor lugar turístico possível para o utilizador final. A principal contribuição deste SR é representar o modelo como CSP e tratá-lo como problema de otimização. Implementámos o nosso sistema com o solucionador em Minizinc com restrições de domínio representadas pelas preferências dos utilizadores. O CSP permitiu que as preferências dos utilizadores guiassem o sistema para encontrar as soluções ideais

Machine learning prediction of mortality in acute myocardial infarction

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Acute Myocardial Infarction (AMI) is the leading cause of death in Portugal and globally. The present investigation created a model based on machine learning for predictive analysis of mortality in patients with AMI upon admission, using different variables to analyse their impact on predictive models. Methods: Three experiments were built for mortality in AMI in a Portuguese hospital between 2013 and 2015 using various machine learning techniques. The three experiments differed in the number and type of variables used. We used a discharged patients' episodes database, including administrative data, laboratory data, and cardiac and physiologic test results, whose primary diagnosis was AMI. Results: Results show that for Experiment 1, Stochastic Gradient Descent was more suitable than the other classification models, with a classification accuracy of 80%, a recall of 77%, and a discriminatory capacity with an AUC of 79%. Adding new variables to the models increased AUC in Experiment 2 to 81% for the Support Vector Machine method. In Experiment 3, we obtained an AUC, in Stochastic Gradient Descent, of 88% and a recall of 80%. These results were obtained when applying feature selection and the SMOTE technique to overcome imbalanced data. Conclusions: Our results show that the introduction of new variables, namely laboratory data, impacts the performance of the methods, reinforcing the premise that no single approach is adapted to all situations regarding AMI mortality prediction. Instead, they must be selected, considering the context and the information available. Integrating Artificial Intelligence (AI) and machine learning with clinical decision-making can transform care, making clinical practice more efficient, faster, personalised, and effective. AI emerges as an alternative to traditional models since it has the potential to explore large amounts of information automatically and systematically.The present publication was funded by Fundação Ciência e Tecnologia, IP national support through CHRC (UIDP/04923/2020).info:eu-repo/semantics/publishedVersio

JISC Preservation of Web Resources (PoWR) Handbook

Handbook of Web Preservation produced by the JISC-PoWR project which ran from April to November 2008. The handbook specifically addresses digital preservation issues that are relevant to the UK HE/FE web management community”. The project was undertaken jointly by UKOLN at the University of Bath and ULCC Digital Archives department

Third international workshop on Authoring of adaptive and adaptable educational hypermedia (A3EH), Amsterdam, 18-22 July, 2005

The A3EH follows a successful series of workshops on Adaptive and Adaptable Educational Hypermedia. This workshop focuses on models, design and authoring of AEH, on assessment of AEH, conversion between AEH and evaluation of AEH. The workshop has paper presentations, poster session and panel discussions

Semantic technologies: from niche to the mainstream of Web 3? A comprehensive framework for web Information modelling and semantic annotation

Context: Web information technologies developed and applied in the last decade have considerably changed the way web applications operate and have revolutionised information management and knowledge discovery. Social technologies, user-generated classification schemes and formal semantics have a far-reaching sphere of influence. They promote collective intelligence, support interoperability, enhance sustainability and instigate innovation. Contribution: The research carried out and consequent publications follow the various paradigms of semantic technologies, assess each approach, evaluate its efficiency, identify the challenges involved and propose a comprehensive framework for web information modelling and semantic annotation, which is the thesis’ original contribution to knowledge. The proposed framework assists web information modelling, facilitates semantic annotation and information retrieval, enables system interoperability and enhances information quality. Implications: Semantic technologies coupled with social media and end-user involvement can instigate innovative influence with wide organisational implications that can benefit a considerable range of industries. The scalable and sustainable business models of social computing and the collective intelligence of organisational social media can be resourcefully paired with internal research and knowledge from interoperable information repositories, back-end databases and legacy systems. Semantified information assets can free human resources so that they can be used to better serve business development, support innovation and increase productivity

Implementing frailty assessment and management in oncology services

Frailty is defined as “A medical syndrome with multiple causes and contributors that is characterised by diminished strength, endurance, and reduced physiologic function that increases an individual’s vulnerability….” Frailty is common in patients with cancer. Patients with frailty are vulnerable to higher rates of treatment toxicity and surgical complications, and worse quality of life and survival outcomes. However, these can and should be improved with targeted assessment, support and management of frailty. Each step in the cancer pathway is an opportunity for assessing and managing frailty. Guidance published by the Joint Collegiate Council for Oncology, in association with the British Geriatrics Society, the International Society of Geriatric Oncology and Macmillan Cancer Support, is intended to encourage and support the implementation of frailty assessment and management in oncology services in the UK. It provides practical advice and recommendations to help ensure this becomes a routine part of clinical care. Frailty is everyone’s business and although aimed primarily at oncologists, this guidance is relevant to everyone involved in the care of adult patients with cancer across the wider multi-disciplinary team.<br/