Mutual Information as a Measure of Coordination in Collaborative Interaction

Dari Trendafilov, Alexander Maye, D. Polani, Roderick Murray-Smith, Andreas Engel, ‘Mutual Information as a Measure of Coordination in Collaborative Interaction’, paper presented at the British HCI 2015 Workshop on Ubiquitous and Collaborative Computing (iUBICOM), Lincoln, UK, 13 July, 2005.We present an information-theoretic approach for quantifying the level of coordination between cooperating parties engaged in a computer-mediated collaborative interaction. The approach builds on Shannon’s mutual information, as a task-independent objective measure, which captures the level of corelation between the actions of interacting agents. We introduce the approach through two characteristic examples and discuss the challenges in designing a reliable measure and the amount of modelling effort required. Our initial results suggest the potential of this measure in supporting designers of collaborative systems and in providing more solid theoretical foundations for the science of Human-Computer Interaction.Peer reviewedFinal Accepted Versio

Construction and abstraction: contrasting methods of supporting model building in learning science

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Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Developing professional recognition of systems thinking in practice: an interim report

The interim report on developing a competency framework for systems thinking in practice (STiP) provides a step towards possibly developing professional recognition of STiP. The report provides feedback to initial co-respondents involved with phase 1 of this wider inquiry, and provides a platform to a wider audience for initiating a second phase of the inquiry. The phase 1 study had the following objectives: 1. To scope relevant examples of work aimed at giving professional recognition to systems thinking 2. To capture some perspectives on the challenges and opportunities facing the task of giving profession recognition to systems thinking. Phase 2 of the wider inquiry aims to firstly consolidate the findings from phase 1 but also to focus more on moves towards collaborative modelling of a STiP competency framework. The research is carried out by members of the Applied Systems Thinking in Practice (ASTiP) Group at The Open University (UK) with funding from OU eSTEeM (OU Centre for STEM Pedagogy). The research team for phase 1 comprised of Rupesh Shah (Associate Lecturer), who carried out the core research activities, in collaboration with Martin Reynolds (Senior Lecturer) who is overseeing both phases of the wider inquiry, including support for reporting on research outcomes. The findings reported in sections 4, 5 and 6 remain largely unrefined and in sketch (bullet) form at this interim stage of reporting. The interim report comprises a brief background to the wider inquiry before outlining the approach taken to the phase 1 study. The findings are reported in relation to each of the two study objectives. Three themes arising from the study as identified by Rupesh are then discussed. Finally, some concluding ideas are presented for taking forward the outcomes from this study towards a second phase of the inquiry

Using the Internet to improve university education

Up to this point, university education has largely remained unaffected by the developments of novel approaches to web-based learning. The paper presents a principled approach to the design of problem-oriented, web-based learning at the university level. The principles include providing authentic contexts with multimedia, supporting collaborative knowledge construction, making thinking visible with dynamic visualisation, quick access to content resources via information and communication technologies, and flexible support by tele-tutoring. These principles are used in the MUNICS learning environment, which is designed to support students of computer science to apply their factual knowledge from the lectures to complex real-world problems. For example, students may model the knowledge management in an educational organisation with a graphical simulation tool. Some more general findings from a formative evaluation study with the MUNICS prototype are reported and discussed. For example, the students' ignorance of the additional content resources is discussed in the light of the well-known finding of insufficient use of help systems in software applications