Lernen im Team: Zusammenhänge zwischen Personen- und Teammerkmalen und der Leistung von Multiplikatoren [Learning in teams: Relationships between individual and team characteristics and the performance of multipliers]

The continuous mutual transfer of knowledge and skills within work teams is increasingly important for organizational practice. According to the situational and experience-based approaches of applied learning research, certain individual and social prerequisites have to be met for successful learning in teams. In a field study at an automobile production site, it was investigated which personal characteristics of multipliers and which characteristics of teams are related to the performance of multipliers in 31 teams with 291 coworkers. Using multi-level analyses (HLM), the amount of variance explained by the predictor variables in teaching success of multipliers and learning success of coworkers was examined. Results showed that multipliers' conscientiousness and team cohesion were related to teaching success of multipliers; extraversion and team cohesion were related to the learning success of coworkers. In closing, the scientific and practical implications for the investigation and promotion of work-based learning processes in teams are discussed.

Acceptability-based QoE management for user-centric mobile video delivery : a field study evaluation

Effective Quality of Experience (QoE) management for mobile video delivery – to optimize overall user experience while adapting to heterogeneous use contexts – is still a big challenge to date. This paper proposes a mobile video delivery system to emphasize the use of acceptability as the main indicator of QoE to manage the end-to-end factors in delivering mobile video services. The first contribution is a novel framework for user-centric mobile video system that is based on acceptability-based QoE (A-QoE) prediction models, which were derived from comprehensive subjective studies. The second contribution is results from a field study that evaluates the user experience of the proposed system during realistic usage circumstances, addressing the impacts of perceived video quality, loading speed, interest in content, viewing locations, network bandwidth, display devices, and different video coding approaches, including region-of-interest (ROI) enhancement and center zooming

Symposium on participatory approaches to reservoir fisheries management: issues, challenges and policies. Dambulla, Sri Lanka, 04-06 Oct. 2004. Session I: Community-based fisheries management: experience in other countries

This paper advocates strategies, processes and practices that enable: livelihoods approaches rather than resource-based approaches, ‘direct’ institutional and policy development, rather than ‘project demonstrations’, and support for regional, national and local communications. (Pdf contains 12 pages).

User-driven quality of experience modelling for mobile video optimisation

The increasing demand for mobile video has attracted much attention from both industry and researchers. To satisfy users and to facilitate the usage of mobile video, providing optimal quality to the users is necessary. As a result, quality of experience (QoE) becomes an important focus in measuring the overall quality perceived by the end-users, from the aspects of both objective system performance and subjective experience. However, due to the complexity of user experience and diversity of resources (such as videos, networks and mobile devices), it is still challenging to develop QoE models for mobile video that can represent how user-perceived value varies with changing conditions. Previous QoE modelling research has two main limitations: aspects influencing QoE are insufficiently considered; and acceptability as the user value is seldom studied. Focusing on the QoE modelling issues, two aims are defined in this thesis: (i) investigating the key influencing factors of mobile video QoE; and (ii) establishing QoE prediction models based on the relationships between user acceptability and the influencing factors, in order to help provide optimal mobile video quality. To achieve the first goal, a comprehensive user study was conducted. It investigated the main impacts on user acceptance: video encoding parameters such as quantization parameter, spatial resolution, frame rate, and encoding bitrate; video content type; mobile device display resolution; and user profiles including gender, preference for video content, and prior viewing experience. Results from both quantitative and qualitative analysis revealed the significance of these factors, as well as how and why they influenced user acceptance of mobile video quality. Based on the results of the user study, statistical techniques were used to generate a set of QoE models that predict the subjective acceptability of mobile video quality by using a group of the measurable influencing factors, including encoding parameters and bitrate, content type, and mobile device display resolution. Applying the proposed QoE models into a mobile video delivery system, optimal decisions can be made for determining proper video coding parameters and for delivering most suitable quality to users. This would lead to consistent user experience on different mobile video content and efficient resource allocation. The findings in this research enhance the understanding of user experience in the field of mobile video, which will benefit mobile video design and research. This thesis presents a way of modelling QoE by emphasising user acceptability of mobile video quality, which provides a strong connection between technical parameters and user-desired quality. Managing QoE based on acceptability promises the potential for adapting to the resource limitations and achieving an optimal QoE in the provision of mobile video content.

Information Experience : Approaches to Theory and Practice

"The German word for experience - Erlebnis - the experience of the life, to live through something - underpins this book: making visible scholarly opportunities for richer and deeper contextualizations and examinations of the lived-world experiences of people in everyday contexts as they be, do and become." (Ross Todd, Preface). Information experience is a burgeoning area of research and still unfolding as an explicit research and practice theme. This book is therefore very timely as it distils the reflections of researchers and practitioners from various disciplines, with interests ranging across information, knowledge, user experience, design and education. They cast a fresh analytical eye on information experience, whilst approaching the idea from diverse perspectives. Information Experience brings together current thinking about the idea of information experience to help form discourse around it and establish a conceptual foundation for taking the idea forward. It therefore "provides a number of theoretical lenses for examining people's information worlds in more holistic and dynamic ways." (Todd, Preface)

Abstract concepts made real: A pilot study examining pedagogical approaches in thermodynamics tutorials

BACKGROUND OR CONTEXT Thermodynamics is a core concept for mechanical engineers yet notoriously difficult. Evidence suggests students struggle to understand and apply the core fundamental concepts of thermodynamics with analysis indicating a problem with student learning/engagement. A contributing factor is that thermodynamics is a ‘science involving concepts based on experiments’ (Mayhew 1990) with subject matter that cannot be completely defined a priori. To succeed, students must engage in a deep-holistic approach while taking ownership of their learning. The difficulty in achieving this often manifests itself in students ‘not getting’ the principles and declaring thermodynamics ‘hard’. PURPOSE OR GOAL Traditionally, students practice and “learn” the application of thermodynamics in their tutorials, however these do not consider prior conceptions (Holman & Pilling 2004). As ‘hands on’ learning is the desired outcome of tutorials it is pertinent to study methods of improving their efficacy. Within the Australian context, the format of thermodynamics tutorials has remained relatively unchanged over the decades, relying anecdotally on a primarily didactic pedagogical approach. Such approaches are not conducive to deep learning (Ramsden 2003) with students often disengaged from the learning process. Evidence suggests (Haglund & Jeppsson 2012), however, that a deeper level and ownership of learning can be achieved using a more constructivist approach for example through self generated analogies. This pilot study aimed to collect data to support the hypothesis that the ‘difficulty’ of thermodynamics is associated with the pedagogical approach of tutorials rather than actual difficulty in subject content or deficiency in students. APPROACH Successful application of thermodynamic principles requires solid knowledge of the core concepts. Typically, tutorial sessions guide students in this application. However, a lack of deep and comprehensive understanding can lead to student confusion in the applications resulting in the learning of the ‘process’ of application without understanding ‘why’. The aim of this study was to gain empirical data on student learning of both concepts and application, within thermodynamic tutorials. The approach taken for data collection and analysis was: - 1 Four concurrent tutorial streams were timetabled to examine student engagement/learning in traditional ‘didactic’ (3 weeks) and non-traditional (3 weeks). In each week, two of the selected four sessions were traditional and two non-traditional. This provided a control group for each week. - 2 The non-traditional tutorials involved activities designed to promote student-centered deep learning. Specific pedagogies employed were: self-generated analogies, constructivist, peer-to-peer learning, inquiry based learning, ownership of learning and active learning. - 3 After a three-week period, teaching styles of the selected groups was switched, to allow each group to experience both approaches with the same tutor. This also acted to mimimise any influence of tutor personality / style on the data. - 4 At the conclusion of the trial participants completed a ‘5 minute essay’ on how they liked the sessions, a small questionnaire, modelled on the modified (Christo & Hoang, 2013)SPQ designed by Biggs (1987) and a small formative quiz to gauge the level of learning achieved. DISCUSSION Preliminary results indicate that overall students respond positively to in class demonstrations (inquiry based learning), and active learning activities. Within the active learning exercises, the current data suggests students preferred individual rather than group or peer-to-peer activities. Preliminary results from the open-ended questions such as “What did you like most/least about this tutorial” and “do you have other comments on how this tutorial could better facilitate your learning”, however, indicated polarising views on the nontraditional tutorial. Some student’s responded that they really like the format and emphasis on understanding the concepts, while others were very vocal that that ‘hated’ the style and just wanted the solutions to be presented by the tutor. RECOMMENDATIONS/IMPLICATIONS/CONCLUSION Preliminary results indicated a mixed, but overall positive response by students with more collaborative tutorials employing tasks promoting inquiry based, peer-to-peer, active, and ownership of learning activities. Preliminary results from student feedback supports evidence that students learn differently, and running tutorials focusing on only one pedagogical approached (typically didactic) may not be beneficial to all students. Further, preliminary data suggests that the learning / teaching style of both students and tutor are important to promoting deep learning in students. Data collection is still ongoing and scheduled for completion at the end of First Semester (Australian academic calendar). The final paper will examine in more detail the results and analysis of this project.

A practice-based culminating experience: Writing a HRSA grant application with Texas Organ Sharing Alliance for public education efforts to increase solid organ donation

In November 2010, nearly 110,000 people in the United States were waiting for organs for transplantation. Despite the fact that the organ donor registration rate has doubled in the last year, Texas has the lowest registration rate in the nation. Due to the need for improved registration rates in Texas, this practice-based culminating experience was to write an application for federal funding for the central Texas organ procurement organization, Texas Organ Sharing Alliance. The culminating experience has two levels of significance for public health – (1) to engage in an activity to promote organ donation registration, and (2) to provide professional experience in grant writing. ^ The process began with a literature review. The review was to identify successful intervention activities in motivating organ donation registration that could be used in intervention design for the grant application. Conclusions derived from the literature review included (1) the need to specifically encourage family discussions, (2) religious and community leaders can be leveraged to facilitate organ donation conversations in families, (3) communication content must be culturally sensitive and (4) ethnic disparities in transplantation must be acknowledged and discussed.^ Post the literature review; the experience followed a five step process of developing the grant application. The steps included securing permission to proceed, assembling a project team, creation of a project plan and timeline, writing each element of the grant application including the design of proposed intervention activities, and completion of the federal grant application. ^ After the grant application was written, an evaluation of the grant writing process was conducted. Opportunities for improvement were identified. The first opportunity was the need for better timeline management to allow for review of the application by an independent party, iterative development of the budget proposal, and development of collaborative partnerships. Another improvement opportunity was the management of conflict regarding the design of the intervention that stemmed from marketing versus evidence-based approaches. The most important improvement opportunity was the need to develop a more exhaustive evaluation plan.^ Eight supplementary files are attached to appendices: Feasibility Discussion in Appendix 1, Grant Guidance and Workshop Notes in Appendix 2, Presentation to Texas Organ Sharing Alliance in Appendix 3, Team Recruitment Presentation in Appendix 5, Grant Project Narrative in Appendix 7, Federal Application Form in Appendix 8, and Budget Workbook with Budget Narrative in Appendix 9.^

Agent-based modeling and simulation for the design of the future european Air Traffic Management system: the experience of CASSIOPEIA

The SESAR (Single European Sky ATM Research) program is an ambitious re-search and development initiative to design the future European air traffic man-agement (ATM) system. The study of the behavior of ATM systems using agent-based modeling and simulation tools can help the development of new methods to improve their performance. This paper presents an overview of existing agent-based approaches in air transportation (paying special attention to the challenges that exist for the design of future ATM systems) and, subsequently, describes a new agent-based approach that we proposed in the CASSIOPEIA project, which was developed according to the goals of the SESAR program. In our approach, we use agent models for different ATM stakeholders, and, in contrast to previous work, our solution models new collaborative decision processes for flow traffic management, it uses an intermediate level of abstraction (useful for simulations at larger scales), and was designed to be a practical tool (open and reusable) for the development of different ATM studies. It was successfully applied in three stud-ies related to the design of future ATM systems in Europe.

Quality of experience evaluation of voice communication: an affect-based approach

Voice communication systems such as Voice-over IP (VoIP), Public Switched Telephone Networks, and Mobile Telephone Networks, are an integral means of human tele-interaction. These systems pose distinctive challenges due to their unique characteristics such as low volume, burstiness and stringent delay/loss requirements across heterogeneous underlying network technologies. Effective quality evaluation methodologies are important for system development and refinement, particularly by adopting user feedback based measurement. Presently, most of the evaluation models are system-centric (Quality of Service or QoS-based), which questioned us to explore a user-centric (Quality of Experience or QoE-based) approach as a step towards the human-centric paradigm of system design. We research an affect-based QoE evaluation framework which attempts to capture users' perception while they are engaged in voice communication. Our modular approach consists of feature extraction from multiple information sources including various affective cues and different classification procedures such as Support Vector Machines (SVM) and k-Nearest Neighbor (kNN). The experimental study is illustrated in depth with detailed analysis of results. The evidences collected provide the potential feasibility of our approach for QoE evaluation and suggest the consideration of human affective attributes in modeling user experience.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Collaborative reflective experience and practice in education explored through self-study and arts-based research

Eight teacher educators used self-study methodology to engage in reflective practice to overcome their isolation as individual teachers and researchers, and to facilitate professional development. Their research question asked: How can we continue to develop our teaching practice to ensure we are high quality, contemporary teacher educators? They contributed collaboratively in one overarching research project as well as through several focussed projects that explored issues in their individual teaching practices including: sustainability, creativity, curriculum design, pedagogy, assessment, and the learning experiences for students. This paper explores the outcomes from collaborative inquiry that five of the eight educator/researchers engaged in during a research-writing retreat. It documents their experience using arts-based strategies in which drawings were created about their experiences of engaging in a collaborative project and smaller focussed self-study projects. Analysis involved inquiring into each other’s drawings through recorded conversation. The metaphoric representations found through analysing the drawings provided insight into participants’ teaching practices and identities as teacher educators. Six months later when the participants had developed their projects further and used other artsbased methods to understand these experiences, they reflected on the key issues for their teaching practices that had arisen from undertaking this Collaborative Reflective Experience and Practice in Education research. Arts-based inquiries and reflective analysis over six months, constitute this paper. The experiences and analyses are shared to show how creating and sharing metaphoric meaning of visual representations is useful in self-study research to drill down into the real issues. Importantly, this in-depth sharing provides authentic interdisciplinary links when individual educators share their own approaches to teaching in their disciplined area. Findings suggest that gaining new insights into each other’s discipline-based approaches to teacher education through these methods, revealed different responses to pedagogical challenges and allowed for new possibilities for understanding the landscape of teacher education.

Collaborative recovery: An integrative model for working with individuals that experience chronic or recurring mental illness.

Objectives: Recovery is an emerging movement in mental health. Evidence for recovery-based approaches is not well developed and approaches to implement recovery-oriented services are not well articulated. The collaborative recovery model (CRM) is presented as a model that assists clinicians to use evidence-based skills with consumers, in a manner consistent with the recovery movement. A current 5 year multisite Australian study to evaluate the effectiveness of CRM is briefly described. Conclusion: The collaborative recovery model puts into practice several aspects of policy regarding recovery-oriented services, using evidence-based practices to assist individuals who have chronic or recurring mental disorders (CRMD). It is argued that this model provides an integrative framework combining (i) evidence-based practice; (ii) manageable and modularized competencies relevant to case management and psychosocial rehabilitation contexts; and (iii) recognition of the subjective experiences of consumers.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Street computing workshop 2009

The Street Computing workshop, held in conjunction with OZCHI 2009, solicits papers discussing new research directions, early research results, works-in-progress and critical surveys of prior research work in the areas of ubiquitous computing and interaction design for urban environments. Urban spaces have unique characteristics. Typically, they are densely populated, buzzing with life twenty-four hours a day, seven days a week. These traits afford many opportunities, but they also present many challenges: traffic jams, smog and pollution, stress placed on public services, and more. Computing technology, particularly the kind that can be placed in the hands of citizens, holds much promise in combating some of these challenges. Yet, computation is not merely a tool for overcoming challenges; rather, when embedded appropriately in our everyday lives, it becomes a tool of opportunity, for shaping how our cities evolve, for enabling us to interact with our city and its people in new ways, and for uncovering useful, but hidden relationships and correlations between elements of the city. The increasing availability of an urban computing infrastructure has lead to new and exciting ways inhabitants can interact with their city. This includes interaction with a wide range of services (e.g. public transport, public services), conceptual representations of the city (e.g. local weather and traffic conditions), the availability of a variety of shared and personal displays (e.g. public, ambient, mobile) and the use of different interaction modes (e.g. tangible, gesture-based, token-based). This workshop solicits papers that address the above themes in some way. We encourage researchers to submit work that deals with challenges and possibilities that the availability of urban computing infrastructure such as sensors and middleware for sensor networks pose. This includes new and innovative ways of interacting with and within urban environments; user experience design and participatory design approaches for urban environments; social aspects of urban computing; and other related areas.

Enhancing the experience of public transport users with urban screens and mobile applications

Public transportation is an environment with great potential for applying location-based services through mobile devices. This paper provides the underpinning rationale for research that will be looking at how the real-time passenger information system deployed by the Translink Transit Authority across all of South East Queensland in Australia can provide a core platform to improve commuters’ user experiences. This system relies on mobile computing and GPS technology to provide accurate information on transport vehicle locations. The proposal builds on this platform to inform the design and development of innovative social media, mobile computing and geospatial information applications. The core aim is to digitally augment the public transport environment to enhance the user experience of commuters for a more enjoyable journey.