Lurking and learning: Making learning visible in a Virtual Design Studio

This paper explores certain types of student behaviour in design courses presented through an online distance learning environment and using a virtual design studio. It demonstrates that types of behaviour often considered to be passive, and therefore negative or less valuable than obviously active behaviours, can be significant evidence of student learning. Specifically, viewing other students’ work is demonstrated to be a stronger (or equal) correlation of student success compared to any other behaviour measured in the virtual design studios studied. It is hypothesised that this activity is part of a larger set of social learning behaviours that contribute to a general social press or ‘ecology’ of studio learning. This finding has important implications for the design and implementation of virtual studios (technically and in learning design) and these are reported specifically for the interest and use of learning designers

Building a virtual classroom : an education environment for the internet generation

This thesis examines the provision of learning environments that enable people to participate in high-quality learning experiences without physically travelling to classrooms and classes. New technologies enable the asynchronous web currently based on text, images, and video, to be extended to facilitate multi-channel synchronous communications. There is significant potential to enhance learning using the 3D worlds used for interactive gaming, populated by avatars representing the participants, and chat systems using text and audio channels. The purpose of this study was to investigate the development and use of 3D web-based learning environments. Staff and students from an Information Technology degree programme at one New Zealand Polytechnic participated in the study. The design and use of 3D web-based learning environments were integrated into one paper over six years. Data were collected from the teachers of this paper and the programme in which it was embedded.A survey instrument was used to collect data, along with artefacts from the software design and development plus the web-based environments created. Computer logs, and records of chat sessions were collected to enable analysis of the activities that took place in the new learning environments. Follow-up interviews were conducted with a sample of students after the completion of their study. Analysis of these data included collations of statistically significant relationships between environmental factors and the design features of the 3D web-based environments created. Results indicate that the 3D web-based environments were well received by the students and show significant potential for the future provision of learning environments. The technology has no negative impact on students’ perception of their learning environment; however, it did not have the expected positive impact on their communications with peers or teaching staff. This research suggests directions for the future development and application of 3D webbased technologies to fully enable their potential to be achieved in educational learning environments

Combining Sources of Preference Data: The Case of the Lurking l‘s

This paper brings together several research streams and concepts that have been evolving in random utility choice theory: first, it reviews the literature on stated preference (SP) elicitation methods and introduces the concept of testing data generation process invariance across SP and revealed preference (RP) choice data sources; second, it proposes a general data generation process an useful framework for viewing this data combination process; third, it describes the evolution of discrete choice models within the random utility family, where progressively more behavioural realism is being achieved by relaxing strong assumptions on the role of the variance structure (specifically heteroscedasticity) of the unobserved effects. This latter topic is central to the issue of combining multiple data sources. Particular choice model formulations incorporating heteroscedastic effects are presented, discussed and applied to data. The rich insights possible from modeling heteroscedasticity in choice processes is illustrated in each of the empirical applications, which examine its relevance to issues of data combination and taste heterogeneity

Beautiful and damned. Combined effect of content quality and social ties on user engagement

User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flickr, we investigate how the production of quality impacts the dynamics of online social systems. We develop a deep learning computer vision model to score images according to their aesthetic value and we validate its output through crowdsourcing. By applying it to over 15B Flickr photos, we study for the first time how image beauty is distributed over a large-scale social system. Beautiful images are evenly distributed in the network, although only a small core of people get social recognition for them. To study the impact of exposure to quality on user engagement, we set up matching experiments aimed at detecting causality from observational data. Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement. Our analysis has practical implications for improving link recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on Knowledge and Data Engineering (Volume: PP, Issue: 99

The Use of Social Media in Enterprises for Communication, Collaboration, and Knowledge Management

Der Erfolg von Social Media im Internet hat dazu geführt, dass diese Technologie zunehmend auch in Unternehmen eingesetzt, oder über deren Implementierung nachgedacht wird. Durch die erwartete Verbesserung der Kommunikation und Interaktion zwischen Mitarbeitern auf der einen Seite und des Wissensmanagements auf der anderen Seite er-hoffen sich Entscheidungsträger in Unternehmen einen erheblichen betriebswirtschaftlichen Nutzen. Obwohl es einige Beispiele erfolgreicher Enterprise-Social-Media(ESM)-Implementierungen gibt und mehr als 90% der Fortune 500 Unternehmen ESM eingeführt haben oder dies planen, verfehlen 80% der ESM-Projekte die eingangs definierten Ziele. Während die Entscheidung, die Software einzukaufen, zentral getroffen wird, hängt deren Erfolg von der aktiven Partizipation der Mitarbeiter ab – wie sich anhand der genannten Statistiken zeigt, ist beides nicht zwangsläufig korreliert. Im Gegensatz zu organischem Wachstum, wie es in Social-Media-Anwendungen im Internet in den vergangenen Jahren beobachtet werden konnte (z.B. bei Facebook), ist die Nutzungsrate von internen ESM oft zu gering, um den Fortbestand der Community zu sichern. Es zeigt sich dabei verstärkt, dass passive Roll-Out-Strategien, die darauf vertrauen, dass es ein vergleichbares organisches Wachstum auch bei ESM gibt, zum Scheitern verurteilt sind. Viel-mehr müssen Analysen im Vorhinein das für einen spezifischen Anwendungsbereich geeignete Tool identifizieren, und Strategien entwickelt werden, wie Mitarbeiter für die Interaktion über die neuen Anwendungen gewonnen werden können. Da Ausgaben für Informationstechnologien bei einem geringen Nutzungsgrad nicht zu-rechtfertigen sind, trägt die vorliegende Dissertation in acht Essays dazu bei, verschiedene Facetten der ESM-Nutzung näher beleuchten und so zu einem besseren Verständnis des Themas und damit einhergehend einer effektiveren und effizienteren Implementierung von ESM beitragen. Sowohl die Analyse von Einflussfaktoren auf verschiedene Nutzungstypen von ESM, die Optimierung von Enterprise-Suchalgorithmen als auch die Neuinterpretation von Online-Produkt-Ratings können dabei helfen, die Veränderungen der internen und externen Kommunikation, Kollaboration und des Wissensmanagements, die sich durch den Einsatz von ESM ergeben, besser zu erklären und bedarfs-gerechter einzusetzen. Die theoretischen und praktischen Implikationen, welche sich konkret aus den einzelnen Essays ergeben, werden in den entsprechenden Abschnitten der jeweiligen Papiere erläutert

How to Host a Data Competition: Statistical Advice for Design and Analysis of a Data Competition

Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised learning competition is vulnerable to overfitting, where the winning solutions are so closely tuned to the particular set of provided data that they cannot generalize to the underlying problem of interest to the host. This paper outlines some important considerations for strategically designing relevant and informative data sets to maximize the learning outcome from hosting a competition based on our experience. It also describes a post-competition analysis that enables robust and efficient assessment of the strengths and weaknesses of solutions from different competitors, as well as greater understanding of the regions of the input space that are well-solved. The post-competition analysis, which complements the leaderboard, uses exploratory data analysis and generalized linear models (GLMs). The GLMs not only expand the range of results we can explore, they also provide more detailed analysis of individual sub-questions including similarities and differences between algorithms across different types of scenarios, universally easy or hard regions of the input space, and different learning objectives. When coupled with a strategically planned data generation approach, the methods provide richer and more informative summaries to enhance the interpretation of results beyond just the rankings on the leaderboard. The methods are illustrated with a recently completed competition to evaluate algorithms capable of detecting, identifying, and locating radioactive materials in an urban environment.Comment: 36 page

Advancing Alternative Analysis: Integration of Decision Science.

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.Assess whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings.We conclude the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients, and would also advance the science of decision analysis.We advance four recommendations: (1) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts