There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. (Advanced Information and Knowledge Processing)

Lunds Civila Ryttarforening, LCR, is one of Sweden’s largest equestrian clubs with its facilities located in between Norra Faladen to the north and LTH to the south. To the west of the horse facilities is “Smorlyckans Idrottsplats” with football pitches, tennis courts, a Jujutsu club and a Home Guard’s building. The club has approximately 500 weekly riders and offers a wide range of activities within the the riding school, as well as stalls for private horses. Discussions on whether the equestrian centre should be relocated or not have reached a standstill as it has been going on for about 50 years. I believe that if LCR is to stay on its current site it can not continue to be an island. Therefore this project is an investigation into how the centre could be developed meeting and integrating with its surroundings. As much as the horse is the centre of the stable and the equestrian sport it’s also the centre of this project. “When Species Meet” is a what-if scenario on what could happen if we plan for the horse and who else that could benefit from that. In addition to the architectural proposal, one major question with the project has been to develop my own method and investigate how it’s possible to keep a high rate of complexity when working with a project. This is something I have done by taking the position of the horse instead of the architect. This change of position has provided me with a possibility to see the site, with all its opportunities, from a perspective that I couldn’t have without the horse. Therefore, this project is also a try on how it could be possible to take on other projects by relocating my investigation to several other positions relevant for those projects. (Less)

When Species Meet

Simulacra and Simulation

One of the key interests for learning analytics is how it can be used to improve retention. This paper focuses on work conducted at the Open University (OU) into predicting students who are at risk of failing their module. The Open University is one of the worlds largest distance learning institutions. Since tutors do not interact face to face with students, it can be difficult for tutors to identify and respond to students who are struggling in time to try to resolve the difficulty. Predictive models have been developed and tested using historic Virtual Learning Environment (VLE) activity data combined with other data sources, for three OU modules. This has revealed that it is possible to predict student failure by looking for changes in user's activity in the VLE, when compared against their own previous behaviour, or that of students who can be categorised as having similar learning behaviour. More focused analysis of these modules applying the GUHA (General Unary Hypothesis Automaton) method of data analysis has also yielded some early promising results for creating accurate hypothesis about students who fail.

/pdf/improving-retention-predicting-at-risk-students-by-analysing-2562bkqqj4.pdf

Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment

Society is become increasingly reliant on data, making it necessary to ensure that all citizens are equipped with the skills needed to be data literate. We argue that the foundations for a data literate society begin by acquiring key data literacy competences in school. However, as yet there is no clear definition of what these should be. This paper explores the different perspectives currently offered on both data and statistical literacy and then critically examines to what extent these address the data literacy needs of citizens in today’s society. We survey existing approaches to teaching data literacy in schools, to identify how data literacy is interpreted in practice. Based on these analyses, we propose a definition of data literacy that is focused on using data to understand real world phenomena. The contribution of this paper is the creation of a common foundation for teaching and learning data literacy skills.

https://openjournals.uwaterloo.ca/index.php/JoCI/article/download/3275/4298

Creating an Understanding of Data Literacy for a Data-driven Society

The OU Analyse project aims at providing early prediction of ‘at-risk’ students based on their demographic data and their interaction with Virtual Learning Environment. Four predictive models have been constructed from legacy data using machine learning methods. In Spring 2014 the approach was piloted and evaluated on two introductory university courses with about 1500 and 3000 students, respectively. Since October 2014 the predictions have been extended to include 10+ courses of different level. The OU Analyse dashboard has been implemented, for presenting predictions and providing a course overview and a view of individual students.

OU Analyse: analysing at-risk students at The Open University

Not all students who fail or drop out would have done so if they had been offered help at the right time. This is particularly true on distance learning modules where there is no direct tutor/student contact, but where it has been shown that making contact at the right time can improve a student’s chances. This paper explores the latest work conducted at the Open University, one of Europe’s largest distance learning institutions, to identify when is the optimum time to make student interventions and to develop models to identify the at-risk students in this time frame. This work in progress is taking real time data and feeding it back to module teams as the module is running. Module teams will be indicating which of the predicted at-risk students have received an intervention, and the nature of the intervention.

/pdf/developing-predictive-models-for-early-detection-of-at-risk-4mo8h55yus.pdf

Developing predictive models for early detection of at-risk students on distance learning modules.

The technological focus of many Smart City projects relies on top-down innovations, ignoring the role that citizens can play in improving their local communities. In this paper we outline our approach to supporting citizens in playing an active role in urban innovation, from the crowdsourcing of initial ideas through to facilitating citizen involvement in the realization of community projects. This extends previous work in the field by exploring how to go beyond identifying issues and ideas to securing a commitment from citizens to assisting a project intended to address an identified issue.

/pdf/reimagining-the-role-of-citizens-in-smart-city-projects-2cq2tm3cfr.pdf

Annika Wolff

Papers

Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment

Creating an Understanding of Data Literacy for a Data-driven Society

OU Analyse: analysing at-risk students at The Open University

Developing predictive models for early detection of at-risk students on distance learning modules.

Reimagining the role of citizens in smart city projects