Personalization via collaboration in web retrieval systems: a context based approach

World Wide Web is a source of information, and searches on the Web can be analyzed to detect patterns in Web users' search behaviors and information needs to effectively handle the users' subsequent needs. The rationale is that the information need of a user at a particular time point occurs in a particular context, and queries are derived from that need. In this paper, we discuss an extension of our personalization approach that was originally developed for a traditional bibliographic retrieval system but has been adapted and extended with a collaborative model for the Web retrieval environment. We start with a brief introduction of our personalization approach in a traditional information retrieval system. Then, based on the differences in the nature of documents, users and search tasks between traditional and Web retrieval environments, we describe our extensions of integrating collaboration in personalization in the Web retrieval environment. The architecture for the extension integrates machine learning techniques for the purpose of better modeling users' search tasks. Finally, a user-oriented evaluation of Web-based adaptive retrieval systems is presented as an important aspect of the overall strategy for personalization

How may designers create furniture that allows meaningful place-making in a modern office?: Case study in the Malaysian office environment

There have been many discussions on office environments, but there have been few studies on the designer relationship with the workplace that they are designing. Given the benefit of the findings of this research, designers might create opportunities for the user to express their emotions through their workplace personalization. In my early field work, I used a participatory design approach and used mock-ups to investigate the main problems and to explore design opportunities in developing new office environments. The findings revealed that meaningful workplaces can be achieved in different way and for different reason according to different needs

Explaining smart heating systems to discourage fiddling with optimized behavior

Our work focuses on textual and graphical explanations for smart heating systems. We have started to investigate the opportunities for when to provide explanations, how we can design these explanations, and we have started to evaluate these explanations. We argue that explanations need to be carefully crafted to fit with their desired aim, in our case to encourage users' trust and reliance while minimizing user interactions

Online banking customization via tag-based interaction

In this paper, we describe ongoing work on online banking customization with a particular focus on interaction. The scope of the study is confined to the Australian banking context where the lack of customization is evident. This paper puts forward the notion of using tags to facilitate personalized interactions in online banking. We argue that tags can afford simple and intuitive interactions unique to every individual in both online and mobile environments. Firstly, through a review of related literature, we frame our work in the customization domain. Secondly, we define a range of taggable resources in online banking. Thirdly, we describe our preliminary prototype implementation with respect to interaction customization types. Lastly, we conclude with a discussion on future work

APERFEIÇOANDO A APRENDIZAGEM PERSONALIZADA COM UM SISTEMA DE RECOMENDAÇÃO EM CURSOS ONLINE PRIVADOS

This paper proposes the integration of a recommendation system into private online courses as a means to enhance personalized learning. By leveraging the power of data analysis and algorithms, this paper argues that the recommendation system can tailor course content, study materials, and learning resources to meet the unique needs and preferences of individual students. The recommendation system, as detailed in this paper, operates by analyzing various factors such as students' learning patterns, performance data, and personal interests. Based on this analysis, the system dynamically adapts the course curriculum to provide additional resources and support for topics that students find challenging, while also offering advanced materials for those who are progressing rapidly. This adaptive approach, as presented in this paper, ensures that each student receives personalized guidance and support, enabling them to navigate the course at their own pace. As outlined, the recommendation system assists in creating customized study paths for students. By considering their learning goals and interests, this paper argues that the system suggests the optimal order of modules or topics within the course. In addition to personalized course content, as discussed in this paper, the recommendation system also suggests relevant learning resources to complement the core materials. These supplementary resources, as highlighted in this paper, such as articles, videos, interactive exercises, or recommended readings, are tailored to each student's specific needs. By providing diverse and targeted resources, the system, as detailed in this paper, ensures that students have access to a rich and varied learning experience, thereby promoting a deeper understanding of the subject matter. Moreover, as emphasized in this paper, the recommendation system fosters peer collaboration by suggesting study groups, discussion forums, or project teams based on shared interests, learning styles, or complementary skill sets. By connecting students with like-minded peers, as proposed in this paper, the system encourages active participation, knowledge sharing, and collaborative learning, creating a supportive and engaging learning community. For courses that focus on skill development, as argued in this paper, the recommendation system helps students identify their strengths and weaknesses. By analyzing their performance data, this paper suggests that the system can recommend targeted exercises, projects, or practice materials to improve specific skills. It can also suggest related courses or modules that build upon students' existing knowledge, as detailed in this paper, allowing them to develop a comprehensive skill set. The recommendation system, as presented in this paper, incorporates personalized assessments and feedback mechanisms to evaluate students' progress. It recommends practice quizzes, mock exams, or interactive assessments to help students gauge their understanding and identify areas for improvement. The system also provides tailored feedback, as discussed in this paper, highlighting strengths and offering specific strategies for enhancement, thereby fostering a growth mindset and supporting continuous learning.  Este artigo propõe a integração de um sistema de recomendação em cursos online privados como meio de aprimorar a aprendizagem personalizada. Ao aproveitar o poder da análise de dados e algoritmos, este artigo argumenta que o sistema de recomendação pode personalizar o conteúdo do curso, materiais de estudo e recursos de aprendizagem para atender às necessidades e preferências únicas de cada aluno. O sistema de recomendação, conforme detalhado neste artigo, opera analisando vários fatores, como padrões de aprendizagem dos alunos, dados de desempenho e interesses pessoais. Com base nessa análise, o sistema adapta dinamicamente o currículo do curso para fornecer recursos adicionais e apoio para tópicos que os alunos consideram desafiadores, ao mesmo tempo em que oferece materiais avançados para aqueles que estão progredindo rapidamente. Essa abordagem adaptativa, conforme apresentada neste artigo, garante que cada aluno receba orientação e suporte personalizados, permitindo que eles naveguem pelo curso em seu próprio ritmo. Como delineado, o sistema de recomendação auxilia na criação de trajetórias de estudo personalizadas para os alunos. Ao considerar seus objetivos de aprendizagem e interesses, este artigo argumenta que o sistema sugere a ordem ideal de módulos ou tópicos dentro do curso. Além do conteúdo do curso personalizado, conforme discutido neste artigo, o sistema de recomendação também sugere recursos de aprendizagem relevantes para complementar os materiais principais. Esses recursos complementares, conforme destacado neste artigo, como artigos, vídeos, exercícios interativos ou leituras recomendadas, são adaptados às necessidades específicas de cada aluno. Ao fornecer recursos diversos e direcionados, o sistema, conforme detalhado neste artigo, garante que os alunos tenham acesso a uma experiência de aprendizagem rica e variada, promovendo, assim, uma compreensão mais profunda do assunto. Além disso, conforme enfatizado neste artigo, o sistema de recomendação promove a colaboração entre pares, sugerindo grupos de estudo, fóruns de discussão ou equipes de projeto com base em interesses compartilhados, estilos de aprendizagem ou conjuntos de habilidades complementares. Ao conectar os alunos a colegas com mentalidades semelhantes, conforme proposto neste artigo, o sistema incentiva a participação ativa, a troca de conhecimento e a aprendizagem colaborativa, criando uma comunidade de aprendizado solidária e envolvente. Para cursos que se concentram no desenvolvimento de habilidades, como argumentado neste artigo, o sistema de recomendação ajuda os alunos a identificar seus pontos fortes e fracos. Ao analisar seus dados de desempenho, este artigo sugere que o sistema pode recomendar exercícios direcionados, projetos ou materiais de prática para aprimorar habilidades específicas. Ele também pode sugerir cursos ou módulos relacionados que se baseiam no conhecimento existente dos alunos, conforme detalhado neste artigo, permitindo que eles desenvolvam um conjunto abrangente de habilidades. O sistema de recomendação, conforme apresentado neste artigo, incorpora avaliações personalizadas e mecanismos de feedback para avaliar o progresso dos alunos. Ele recomenda questionários de prática, exames simulados ou avaliações interativas para ajudar os alunos a avaliar seu entendimento e identificar áreas para melhoria. O sistema também fornece feedback personalizado, conforme discutido neste artigo, destacando pontos fortes e oferecendo estratégias específicas para aprimoramento, promovendo assim uma mentalidade de crescimento e apoiando a aprendizagem contínua. &nbsp

A complementing approach for identifying ethical issues in care robotics – grounding ethics in practical use

We use a long-term study of a robotic eating-aid for disabled users to illustrate how empirical use give rise to a set of ethical issues that might be overlooked in ethic discussions based on theoretical extrapolation of the current state-of-the-art in robotics. This approach provides an important complement to the existing robot ethics by revealing new issues as well as providing actionable guidance for current and future robot design. We discuss our material in relation to the literature on robot ethics, specifically the risk of robots performing care taking tasks and thus causing increased isolation for care recipients. Our data identifies a different set of ethical issues such as independence, privacy, and identity where robotics, if carefully designed and developed, can make positive contributions

Consumer attitudes toward personalization features and intention to purchase online

In this study, a model of attitude toward personalization and purchase intention is developed to investigate how consumer attitudes and intention to purchase using personalization features are influenced by privacy and security concerns and by previous online purchase experiences. The behavioral intention model (Fishbein, 197 5) has been adopted for theoretical model building. To collect data, an e-mail survey was distributed to 7,000 online consumers who had at least online shopping experience and a sample of 1140 usable responses were used for data analysis. The results indicated that 1) attitudes toward personalization features were important determinants of consumer intentions to purchase online, 2) consumer concerns about privacy and security had a significant influence on consumer attitudes toward personalization features, and 3) previous online purchase experience had no influence on consumer attitudes toward personalization features. Implications and directions for future research are discussed

Defining Engagement and Characterizing Engaged-Behaviors in Digital Gaming

International audienceBackground. Although the analysis of engagement is crucial for digital entertainment or learning games, the concept of players' or learners' engagement is still confusing. Indeed, in digital games research, several concepts referring to the idea of engagement such as immersion, involvement, presence, and flow are used. Also, while the characterization of engaged-behaviors may be useful for designers or teachers in assessing players' or learners' engagement, the nature and the scope of these behaviors are still unclear. Aim. In this article, based on a multidisciplinary state of the art on the concept of engagement, we define and delineate the concepts related to digital gaming engagement. Results. We characterize engaged-behaviors by identifying four types of engagement: environmental, social, self, and action. We thus refine, disambiguate, and characterize the concepts of engagement and engaged-behaviors. This work therefore constitutes an effective support for analyzing, designing, assessing, and personalizing engaging activities in digital games

Chapter 11: Instructional Message Design in E-Learning

Instructional message design is an essential aspect of an effective e-learning experience for learners. E-learning is the use of online and mobile technologies to develop learning material, content, and applications for online learners. As an instructional designer, it is important to analyze and know your audience, engage the user, use relevant information, and provide a good user experience. Other critical aspects of message design for e-learning include the reduction of extraneous cognitive load, the aesthetic look and feel of the design, designing for learner motivation, and a focused simplicity of design. User engagement can also be encouraged by relevant content and context, and the instructor’s or facilitator’s active participation in discussion, chat, projects, and group activities

New Media and the Quality of Life

We are currently in the middle of a revolution. This revolution, sometimes called the digital revolution, is the revolutionary transformation brought about in the information and communication structure of society by the advent of the digital computer, with most of the major transformations having taken place in the past thirty years. Digital computing technology has generated the mainframe and personal computer, the multimedia computer, and computer networks. It has also transformed the telephone system and the monetary system, it is transforming all kinds of conventional products ranging from washing machines to automobiles, and it is on its way to change television as well. More than ever, contemporary society is an Information Society, in which the importance of information and communication is much greater than in past societies, and of which technologies that facilitate information and communication processes are a central societal feature. In this paper, I want to evaluate the implications of contemporary information and communication media for the quality of life, including both the new media from the digital revolution and the older media that still remain in use. My evaluation of contemporary media will proceed in three parts. In the section to follow, the benefits of contemporary media will be discussed, with special emphasis given to their immediate functional benefits. The section thereafter is devoted to a discussion of four potential threats posed by contemporary media. In a final major section, I look at the future of digital media and the possibilities available to us in shaping that future. A short concluding section ends the paper