What is it like learning with an eportfolio for online distance learners?

This paper reports on a doctoral research project which examines the nature of the learning experience of using an eportfolio and whether it enhances the development of critical thinking among online distance learners. It aims to interrogate the process of the development of critical thinking rather than the product. The project adopts a case study approach, following 24 online distance learners over the course of one academic year in a Dublin based third level institution. The research question for the study is: How can eportfolios enhance the nature of the learning experience and the development of critical thinking among online distance learners? This study is using an exploratory holistic single-case design where the “object of the study” is the of the learner experience of using an eportfolio and the process of developing critical thinking are investigated. The participants are intermediate online distance sociology learners studying a module called Soc3A- Power, Social Order, Crime, Work and Employment as part of the BA (Hons) in Humanities which is a modular humanities programme whereby learners can study a combination of history, sociology, literature, psychology and philosophy. Participants have used their eportfolios to create a critical commentary of their learning and completed five eportfolio entries over the course of one academic year at key points in their learning journey. Eportfolio entries follow a prescribed structured template of critical questions intended to encourage reflection about their learning. Within this case study 37 interviews were conducted for an in-depth exploration of the learner experience of using an eportfolio and the development of criticality. The participants were interviewed with their eportfolios, written, visual and physical artefacts from the participant’s eportfolios were used as stimulus during the interviews using the technique of “photo elicitation”

What Is Urban Environmental Stewardship? Constructing a Practitioner-Derived Framework

Agencies and organizations deploy various strategies in response to environmental challenges, including the formulation of policy, programs, and regulations. Citizen-based environmental stewardship is increasingly seen as an innovative and important approach to improving and conserving landscape health. A new research focus on the stewardship of urban natural resources is being launched by the U.S. Forest Service in the Pacific Northwest region. Early scoping efforts are addressing various scales of human systems ranging from individuals to organizations to the entire positive “footprint” of stewardship on the land. This report addresses a fundamental need—to understand and describe civic environmental stewardship in urban settings. Stewardship has been described and defined in diverse ways within a variety of contexts, including the philosophical literature of environmentalism, agency program descriptions, and outreach by sponsoring organizations. Constructing a framework to convey the layered meanings of stewardship will help to focus and guide future research. A cognitive mapping technique was used to elicit responses to the question “What is environmental stewardship?” Semistructured interviews were conducted with representatives of nine Seattle environmental organizations, a group of practitioners who collectively represent over 100 years of experience in the field. Program planners and managers have particularly direct experiences of stewardship. Cognitive mapping enables participants to explore, then display, their particular knowledge and perceptions about an idea or activity. Analysis generated thematic, structural representations of shared concepts. Results show that the practitioners have multilayered perceptions of stewardship, from environmental improvement to community building, and from actions to outcomes. The resulting conceptual framework demonstrates the full extent of stewardship activity and meaning, which can aid stewardship sponsors to improve stewardship programs, leading to better experiences for participants and higher quality outcomes for projects and environments

What is the Avatar?

What are the characteristic features of avatar-based singleplayer videogames, from Super Mario Bros. to Grand Theft Auto? Rune Klevjer examines this question with a particular focus on issues of fictionality and realism, and their relation to cinema and Virtual Reality. Through close-up analysis and philosophical discussion, Klevjer argues that avatar-based gaming is a distinctive and dominant form of virtual self-embodiment in digital culture. This book is a revised edition of Rune Klevjer's pioneering work from 2007, featuring a new introduction by the author and afterword by Stephan Günzel, Jörg Sternagel, and Dieter Mersch

What Good is Positive Business?

Current economic forces, combined with powerful social forces, have created a shifting paradigm of value for markets and organizations around the world. Simply making money – regardless of the social costs – will no longer suffice as a definition of success for a country, a business or even for most individuals, as the human and environmental price becomes increasingly expensive and unsustainable. Now more than ever we need a revolution of new theories and ideologies to help us define and discover the “good society” of the future. Humbly the authors of this paper propose a new theory of business – Theory P – and a means to test its applications – positive business – to advance this revolution by developing more positive leaders, employees and institutions. Grounded in the learnings from the great business thinkers and the leading “people” scientists of our time, we have blended organizational scholarship, positive psychology and positive organizational scholarship together to ask “What good is positive business?” We hope you find our thinking enjoyable, challenging and provocative. And we welcome feedback and ideas on how we might shape our ideas further

What Good Is Positive Business?

Current economic forces, combined with powerful social forces, have created a shifting paradigm of value for markets and organizations around the world. Simply making money – regardless of the social costs – will no longer suffice as a definition of success for a country, a business or even for most individuals, as the human and environmental price becomes increasingly expensive and unsustainable. Now more than ever we need a revolution of new theories and ideologies to help us define and discover the “good society” of the future. Humbly the authors of this paper propose a new theory of business – Theory P – and a means to test its applications – positive business – to advance this revolution by developing more positive leaders, employees and institutions. Grounded in the learnings from the great business thinkers and the leading “people” scientists of our time, we have blended organizational scholarship, positive psychology and positive organizational scholarship together to ask “What good is positive business?” We hope you find our thinking enjoyable, challenging and provocative. And we welcome feedback and ideas on how we might shape our ideas further

If interpretability is the answer, what is the question?

Due to the ability to model even complex dependencies, machine learning (ML) can be used to tackle a broad range of (high-stakes) prediction problems. The complexity of the resulting models comes at the cost of transparency, meaning that it is difficult to understand the model by inspecting its parameters. This opacity is considered problematic since it hampers the transfer of knowledge from the model, undermines the agency of individuals affected by algorithmic decisions, and makes it more challenging to expose non-robust or unethical behaviour. To tackle the opacity of ML models, the field of interpretable machine learning (IML) has emerged. The field is motivated by the idea that if we could understand the model's behaviour -- either by making the model itself interpretable or by inspecting post-hoc explanations -- we could also expose unethical and non-robust behaviour, learn about the data generating process, and restore the agency of affected individuals. IML is not only a highly active area of research, but the developed techniques are also widely applied in both industry and the sciences. Despite the popularity of IML, the field faces fundamental criticism, questioning whether IML actually helps in tackling the aforementioned problems of ML and even whether it should be a field of research in the first place: First and foremost, IML is criticised for lacking a clear goal and, thus, a clear definition of what it means for a model to be interpretable. On a similar note, the meaning of existing methods is often unclear, and thus they may be misunderstood or even misused to hide unethical behaviour. Moreover, estimating conditional-sampling-based techniques poses a significant computational challenge. With the contributions included in this thesis, we tackle these three challenges for IML. We join a range of work by arguing that the field struggles to define and evaluate "interpretability" because incoherent interpretation goals are conflated. However, the different goals can be disentangled such that coherent requirements can inform the derivation of the respective target estimands. We demonstrate this with the examples of two interpretation contexts: recourse and scientific inference. To tackle the misinterpretation of IML methods, we suggest deriving formal interpretation rules that link explanations to aspects of the model and data. In our work, we specifically focus on interpreting feature importance. Furthermore, we collect interpretation pitfalls and communicate them to a broader audience. To efficiently estimate conditional-sampling-based interpretation techniques, we propose two methods that leverage the dependence structure in the data to simplify the estimation problems for Conditional Feature Importance (CFI) and SAGE. A causal perspective proved to be vital in tackling the challenges: First, since IML problems such as algorithmic recourse are inherently causal; Second, since causality helps to disentangle the different aspects of model and data and, therefore, to distinguish the insights that different methods provide; And third, algorithms developed for causal structure learning can be leveraged for the efficient estimation of conditional-sampling based IML methods.Aufgrund der Fähigkeit, selbst komplexe Abhängigkeiten zu modellieren, kann maschinelles Lernen (ML) zur Lösung eines breiten Spektrums von anspruchsvollen Vorhersageproblemen eingesetzt werden. Die Komplexität der resultierenden Modelle geht auf Kosten der Interpretierbarkeit, d. h. es ist schwierig, das Modell durch die Untersuchung seiner Parameter zu verstehen. Diese Undurchsichtigkeit wird als problematisch angesehen, da sie den Wissenstransfer aus dem Modell behindert, sie die Handlungsfähigkeit von Personen, die von algorithmischen Entscheidungen betroffen sind, untergräbt und sie es schwieriger macht, nicht robustes oder unethisches Verhalten aufzudecken. Um die Undurchsichtigkeit von ML-Modellen anzugehen, hat sich das Feld des interpretierbaren maschinellen Lernens (IML) entwickelt. Dieses Feld ist von der Idee motiviert, dass wir, wenn wir das Verhalten des Modells verstehen könnten - entweder indem wir das Modell selbst interpretierbar machen oder anhand von post-hoc Erklärungen - auch unethisches und nicht robustes Verhalten aufdecken, über den datengenerierenden Prozess lernen und die Handlungsfähigkeit betroffener Personen wiederherstellen könnten. IML ist nicht nur ein sehr aktiver Forschungsbereich, sondern die entwickelten Techniken werden auch weitgehend in der Industrie und den Wissenschaften angewendet. Trotz der Popularität von IML ist das Feld mit fundamentaler Kritik konfrontiert, die in Frage stellt, ob IML tatsächlich dabei hilft, die oben genannten Probleme von ML anzugehen, und ob es überhaupt ein Forschungsgebiet sein sollte: In erster Linie wird an IML kritisiert, dass es an einem klaren Ziel und damit an einer klaren Definition dessen fehlt, was es für ein Modell bedeutet, interpretierbar zu sein. Weiterhin ist die Bedeutung bestehender Methoden oft unklar, so dass sie missverstanden oder sogar missbraucht werden können, um unethisches Verhalten zu verbergen. Letztlich stellt die Schätzung von auf bedingten Stichproben basierenden Verfahren eine erhebliche rechnerische Herausforderung dar. In dieser Arbeit befassen wir uns mit diesen drei grundlegenden Herausforderungen von IML. Wir schließen uns der Argumentation an, dass es schwierig ist, "Interpretierbarkeit" zu definieren und zu bewerten, weil inkohärente Interpretationsziele miteinander vermengt werden. Die verschiedenen Ziele lassen sich jedoch entflechten, sodass kohärente Anforderungen die Ableitung der jeweiligen Zielgrößen informieren. Wir demonstrieren dies am Beispiel von zwei Interpretationskontexten: algorithmischer Regress und wissenschaftliche Inferenz. Um der Fehlinterpretation von IML-Methoden zu begegnen, schlagen wir vor, formale Interpretationsregeln abzuleiten, die Erklärungen mit Aspekten des Modells und der Daten verknüpfen. In unserer Arbeit konzentrieren wir uns speziell auf die Interpretation von sogenannten Feature Importance Methoden. Darüber hinaus tragen wir wichtige Interpretationsfallen zusammen und kommunizieren sie an ein breiteres Publikum. Zur effizienten Schätzung auf bedingten Stichproben basierender Interpretationstechniken schlagen wir zwei Methoden vor, die die Abhängigkeitsstruktur in den Daten nutzen, um die Schätzprobleme für Conditional Feature Importance (CFI) und SAGE zu vereinfachen. Eine kausale Perspektive erwies sich als entscheidend für die Bewältigung der Herausforderungen: Erstens, weil IML-Probleme wie der algorithmische Regress inhärent kausal sind; zweitens, weil Kausalität hilft, die verschiedenen Aspekte von Modell und Daten zu entflechten und somit die Erkenntnisse, die verschiedene Methoden liefern, zu unterscheiden; und drittens können wir Algorithmen, die für das Lernen kausaler Struktur entwickelt wurden, für die effiziente Schätzung von auf bindingten Verteilungen basierenden IML-Methoden verwenden

Bayesianism for Non-ideal Agents

Orthodox Bayesianism is a highly idealized theory of how we ought to live our epistemic lives. One of the most widely discussed idealizations is that of logical omniscience: the assumption that an agent’s degrees of belief must be probabilistically coherent to be rational. It is widely agreed that this assumption is problematic if we want to reason about bounded rationality, logical learning, or other aspects of non-ideal epistemic agency. Yet, we still lack a satisfying way to avoid logical omniscience within a Bayesian framework. Some proposals merely replace logical omniscience with a different logical idealization; others sacrifice all traits of logical competence on the altar of logical non-omniscience. We think a better strategy is available: by enriching the Bayesian framework with tools that allow us to capture what agents can and cannot infer given their limited cognitive resources, we can avoid logical omniscience while retaining the idea that rational degrees of belief are in an important way constrained by the laws of probability. In this paper, we offer a formal implementation of this strategy, show how the resulting framework solves the problem of logical omniscience, and compare it to orthodox Bayesianism as we know it