577 research outputs found
Designing adaptivity in educational games to improve learning
The study of pedagogy has shown that students have different ways of learning and processing information. Students in a classroom learn best when being taught by a teacher who is able to adapt and/or change the pedagogical model being used, to better suit said students and/or the subject being taught. When considering other teaching mediums such as computer-assisted learning systems or educational video games, research also identified the benefits of adapting educational features to better teach players. However, effective methods for adaptation in educational video games are less well researched.This study addresses four points regarding adaptivity within educational games. Firstly, a framework for making any game adaptive was extracted from the literature. Secondly, an algorithm capable of monitoring, modelling and executing adaptations was developed and explained using the framework. Thirdly, the algorithm's effect on learning gains in players was evaluated using a customised version of Minecraft as the educational game and topics from critical thinking as the educational content. Lastly, a methodology explaining the process of utilising the algorithm with any educational game and the evaluation of said methodology were detailed
Investigating Trade-offs For Fair Machine Learning Systems
Fairness in software systems aims to provide algorithms that operate in a nondiscriminatory manner, with respect to protected attributes such as gender, race,
or age. Ensuring fairness is a crucial non-functional property of data-driven Machine Learning systems. Several approaches (i.e., bias mitigation methods) have
been proposed in the literature to reduce bias of Machine Learning systems. However, this often comes hand in hand with performance deterioration. Therefore, this
thesis addresses trade-offs that practitioners face when debiasing Machine Learning
systems.
At first, we perform a literature review to investigate the current state of the
art for debiasing Machine Learning systems. This includes an overview of existing
debiasing techniques and how they are evaluated (e.g., how is bias measured).
As a second contribution, we propose a benchmarking approach that allows for
an evaluation and comparison of bias mitigation methods and their trade-offs (i.e.,
how much performance is sacrificed for improving fairness).
Afterwards, we propose a debiasing method ourselves, which modifies already
trained Machine Learning models, with the goal to improve both, their fairness and
accuracy.
Moreover, this thesis addresses the challenge of how to deal with fairness with
regards to age. This question is answered with an empirical evaluation on real-world
datasets
Northeastern Illinois University, Academic Catalog 2023-2024
https://neiudc.neiu.edu/catalogs/1064/thumbnail.jp
Towards A Modular IT-Landscape For Manufacturing Companies: Framework For Holistic Software Modularization
Companies in the manufacturing sector are confronted with an increasingly dynamic environment. Thus, corporate processes and, consequently, the supporting IT landscape must change. This need is not yet fully met in the development of information systems. While best-of-breed approaches are available, monolithic systems that no longer meet the manufacturing industry's requirements are still prevalent in practical use. A modular structure of IT landscapes could combine the advantages of individual and standard information systems and meet the need for adaptability. At present, however, there is no established standard for the modular design of IT landscapes in the field of manufacturing companies' information systems. This paper presents different ways of the modular design of IT landscapes and information systems and analyzes their objects of modularization. For this purpose, a systematic literature research is carried out in the subject area of software and modularization. Starting from the V-model as a reference model, a framework for different levels of modularization was developed by identifying that most scientific approaches carry out modularization at the data structure-based and source code-based levels. Only a few sources address the consideration of modularization at the level of the software environment-based and software function-based level. In particular, no domain-specific application of these levels of modularization, e.g., for manufacturing, was identified
Doing the heavy lifting: the experiences of working-class professional services and administrative staff in Russell Group universities
In recent years, UK higher education has pursued more inclusive practices, adopting widening participation metrics, removing historically problematic statues, reviewing research culture environments, and renaming university buildings (Chigudu, 2021; Heath et al, 2013). Research has sought to understand how people from different 'non-traditional' backgrounds experience these institutions (Reay, 2017b). At present, studies of social class focus on the experiences of working-class academics and working-class students (Crew, 2020; Crozier et al., 2019). Academic research has not yet addressed the experiences of working-class professional services and administrative staff, who form a critical part of the political economy of knowledge production. This study used an interpretative approach, combining narrative inquiry and semi-structured interview questions to elucidate the narratives of 13 working-class professional service staff working in Russell Group universities. This thesis makes contributions from conceptual, empirical, theoretical and practical perspectives. Conceptually, a working-class identity, for the participants in this study, is formed from a multitude of varying characteristics, rather than a traditional association with employment and labour. Participants refer to their working-class identity through family history, occupations, deprivation and taste. Empirically, participants felt supported by their immediate networks but often at the price of uncomfortable relationships with academics. Here, a lack of value was made visceral by toxic behaviour, substandard remuneration, poor career progression, isolation and not being listened to in meetings. Concerning theory, I find a ubiquity with the use of Bourdieu in working-class studies. Yet, there is a disparity between theory and participant identification and a dislocation between temporalities of space, time and experience that the theories of Bourdieu fail to account for. I find that there is a lamination of field which working-class participants carry through their lives. I question social mobility, a rhetoric accepted as the way disadvantaged people are accepted into elite institutions. This assimilation accepts that middle-class space is normative in juxtaposition with working-class attributes which are seen to be undesirable. Inclusion, not representation, should be the goal of all Higher Education Institutions (HEIs) if they want to embed equity in their workforce. This study works at frontiers of research on social class, developing a space where the experiences of professional services staff might be fully integrated in the cultural fabric of universities. For too long these voices have been ignored and pushed to the margins, I hope this will be the first of many studies to address this injustice
The co-evolution of networked terrorism and information technology
This thesis describes for the first time the mechanism by which high-performing terrorist networks leverage new iterations of information technology and the two interact in a mutually propulsive manner. Using process tracing as its methodology and complexity theory as its ontology, it identifies both terrorism and information technology as complex adaptive systems, a key characteristic of whose make-up is that they co-evolve in pursuit of augmented performance. It identifies this co-evolutionary mechanism as a classic information system that computes the additional scale with which the new technology imbues its terrorist partner, in other words, the force multiplier effect it enables. The thesis tests the mechanismâs theoretical application rigorously in three case studies spanning a period of more than a quarter of a century: Hezbollah and its migration from terrestrial to satellite broadcasting, Al Qaeda and its leveraging of the internet, and Islamic State and its rapid adoption of social media. It employs the NATO Allied Joint Doctrine for Intelligence Procedures estimative probability standard to link its assessment of causal inference directly to the data. Following the logic of complexity theory, it contends that a more twenty-first century interpretation of
the key insight of RAND researchers in 1972 would be not that âterrorism evolvesâ but that it co-evolves, and that co-evolution too is arguably the first logical explanation of the much-vaunted âsymbiotic relationshipâ between terrorists and the media that has been at the heart of the sub-discipline of terrorism studies for 50 years. It maintains that an understanding of terrorism based on co-evolution belatedly explains the newness of much-debated ânew terrorismâ. Looking forward, it follows the trajectory of terrorism driven by information technology and examines the degree to which the gradual symbiosis between biological and digital information, and the acknowledgment of human beings as reprogrammable information systems, is transforming the threat landscape
Machine Learning and Its Application to Reacting Flows
This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the worldâs total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and âgreenerâ combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation
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