Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

Improving a tutor’s feedback assessment tool: transforming Open Mentor following two recent deployments

Evidence shows the vital role that the quality of feedback plays on students’ performance and on the overall increase of learning opportunities that good feedback creates for students. Based on this evidence, the Open University developed Open Mentor (OM), a system to support tutors enhance their feedback practice. Open Mentor Technology transfer (OMTetra), a JISC funded project, took OM and deployed it in two Higher Education institutions with the purpose of evaluating the process of transferability and continue the development of the tools available to tutors within the system. This paper describes the original OM and the enhancements identified after use and evaluations from tutors of the institutions involved

Transformation for an open education repository: issues associated with IT and computing distance learning course materials

The number of Open Educational Resource Repositories available worldwide continues to grow and many contain course materials from campus-based institutions. The Open University in the United Kingdom (UK) has influenced the OER movement by releasing traditional distance learning course materials as OERs. This paper discusses issues associated with the transformation of Open University distance learning course materials in the IT and Computing subject area into OERs

Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking

Machine-learned models are often described as "black boxes". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial task, which requires significant and time-consuming human effort. Whilst some features are inherently static, representing properties that cannot be influenced (e.g., the age of an individual), others capture characteristics that could be adjusted (e.g., the daily amount of carbohydrates taken). Nonetheless, once a model is learned from the data, each prediction it makes on new instances is irreversible - assuming every instance to be a static point located in the chosen feature space. There are many circumstances however where it is important to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter such a prediction when the mutated instance is input back to the model. In this paper, we present a technique that exploits the internals of a tree-based ensemble classifier to offer recommendations for transforming true negative instances into positively predicted ones. We demonstrate the validity of our approach using an online advertising application. First, we design a Random Forest classifier that effectively separates between two types of ads: low (negative) and high (positive) quality ads (instances). Then, we introduce an algorithm that provides recommendations that aim to transform a low quality ad (negative instance) into a high quality one (positive instance). Finally, we evaluate our approach on a subset of the active inventory of a large ad network, Yahoo Gemini.Comment: 10 pages, KDD 201

Doctrina perpetua: brokering change, promoting innovation and transforming marginalisation in university learning and teaching [Editors introduction]

Doctrina perpetua—translated variously as “forever learning” (Cryle, 1992, p. 27), “lifelong learning” and “lifelong education”—is the Latin motto of Central Queensland University (CQU), an Australian regional university with campuses in Central Queensland and the metropolitan and provincial cities of Brisbane, the Gold Coast, Melbourne and Sydney and with centres in China, Fiji, Hong Kong and Singapore. During its early development the institution was small and regional; in many ways it was an institution at the margins of higher education. For only a third of its 40-year life has it been recognised as a university. However, the vision of both its founders and its continuing staff has been that of an institution that actively brokers change, promotes innovation and seeks to transform marginalisation— for students, for its community and for itself. Its short life on the edge of the universe of higher education has promoted a culture of innovation and an acceptance that change is a necessary and positive aspect of life on the edge. Embracing change, CQU has become a complex institution, a notion well expressed in a speech in August 1999 by former Vice-Chancellor Lauchlan Chipman on Visioning Our Future: I have often remarked that I do not see CQU as “the last university of the old millennium” but rather as “the first university of the new millennium”. One of our greatest strengths in making the transition is our relative immaturity as a university. The more mature a university, especially if it is successful, the less agile it is when it comes to the need to change. So far as the future of universities and change is concerned, my position is unequivocally Heraclitean: change is the only thing that is permanent. Applying to itself the motto “doctrina perpetua” over its short life, the agile University has become a “complex and diverse organisation” (Danaher, Harreveld, Luck & Nouwens, 2004, p. 13). This overview of CQU seeks to provide readers with a short description of the current state of the institution and the story of its development to provide a context for understanding the chapters that follow, and to assist readers to reflect on how these developments at CQU relate to higher education generally, and to the universities with which they are more familiar

Developing an Online Learning Pedagogy for Conflict Resolution Training

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Heritage Quay: What Will You Discover? Transforming the Archives of the University of Huddersfield, Yorkshire, UK

The Heritage Quay project is changing how archive services at the University of Huddersfield are delivered. This article examines how the Staff/Space/Collections dependency model and Customer Service Excellence framework have been used, and what lessons can be drawn for other archives

Multi-Instance Multi-Label Learning

In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we propose the D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly.Comment: 64 pages, 10 figures; Artificial Intelligence, 201

Transforming pre-service teacher curriculum: observation through a TPACK lens

This paper will discuss an international online collaborative learning experience through the lens of the Technological Pedagogical Content Knowledge (TPACK) framework. The teacher knowledge required to effectively provide transformative learning experiences for 21st century learners in a digital world is complex, situated and changing. The discussion looks beyond the opportunity for knowledge development of content, pedagogy and technology as components of TPACK towards the interaction between those three components. Implications for practice are also discussed. In today’s technology infused classrooms it is within the realms of teacher educators, practising teaching and pre-service teachers explore and address effective practices using technology to enhance learning

Teaching and learning in virtual worlds: is it worth the effort?

Educators have been quick to spot the enormous potential afforded by virtual worlds for situated and authentic learning, practising tasks with potentially serious consequences in the real world and for bringing geographically dispersed faculty and students together in the same space (Gee, 2007; Johnson and Levine, 2008). Though this potential has largely been realised, it generally isn’t without cost in terms of lack of institutional buy-in, steep learning curves for all participants, and lack of a sound theoretical framework to support learning activities (Campbell, 2009; Cheal, 2007; Kluge & Riley, 2008). This symposium will explore the affordances and issues associated with teaching and learning in virtual worlds, all the time considering the question: is it worth the effort