Using Open Data as a Material for Introductory Programming Assignments

This case study explores why and how open data can be used as a material with which to produce engaging challenges for students as they are introduced to programming. Through describing the process of producing the assignments, and learner responses to them, we suggest that open data is a powerful material for designing learning activities because of its qualities of ease of access and authenticity. We conclude by outlining steps to take in devising and implementing open data-based assignments

Open Data as Open Educational Resources: Case studies of emerging practice

This collection presents the stories of our contributors’ experiences and insights, in order to demonstrate the enormous potential for openly-licensed and accessible datasets (Open Data) to be used as Open Educational Resources (OER). Open Data is an umbrella term describing openly-licensed, interoperable, and reusable datasets which have been created and made available to the public by national or local governments, academic researchers, or other organisations. These datasets can be accessed, used and shared without restrictions other than attribution of the intellectual property of their creators1.While there are various definitions of OER, these are generally understood as openly-licensed digital resources that can be used in teaching and learning. On the basis of these definitions, it is reasonable to assert that while Open Data is not always OER, it certainly becomes OER when used within pedagogical contexts. Yet while the question may appear already settled at the level of definition, the potential and actual pedagogical uses of Open Data appear to have been under-discussed. As open education researchers who take a wider interest in the various open ‘movements’, we have observed that linkages between them are not always strong, in spite of shared and interconnecting values. So, Open Data tends to be discussed primarily in relation to its production, storage, licensing and accessibility, but less often in relation to its practical subsequent uses. And, in spite of widespread understanding that use of the term ‘OER’ is actually context-dependent, and, therefore, could be almost all-encompassing, the focus of OER practice and research has tended to be on educator-produced learning materials. The search for relevant research literature in the early stages of this project turned up sources which discuss the benefits of opening data, and others advocating improving student engagement with data3, but on the topic of Open Data as an educational resource specifically, there appeared to be something of a gap

Creating human digital memories with the aid of pervasive mobile devices

The abundance of mobile and sensing devices, within our environment, has led to a society in which any object, embedded with sensors, is capable of providing us with information. A human digital memory, created with the data from these pervasive devices, produces a more dynamic and data rich memory. Information such as how you felt, where you were and the context of the environment can be established. This paper presents the DigMem system, which utilizes distributed mobile services, linked data and machine learning to create such memories. Along with the design of the system, a prototype has also been developed, and two case studies have been undertaken, which successfully create memories. As well as demonstrating how memories are created, a key concern in human digital memory research relates to the amount of data that is generated and stored. In particular, searching this set of big data is a key challenge. In response to this, the paper evaluates the use of machine learning algorithms, as an alternative to SPARQL, and treats searching as a classification problem. In particular, supervised machine learning algorithms are used to find information in semantic annotations, based on probabilistic reasoning. Our approach produces good results with 100% sensitivity, 93% specificity, 93% positive predicted value, 100% negative predicted value, and an overall accuracy of 97%

Creating human digital memories with the aid of pervasive mobile devices

The abundance of mobile and sensing devices, within our environment, has led to a society in which any object, embedded with sensors, is capable of providing us with information. A human digital memory, created with the data from these pervasive devices, produces a more dynamic and data rich memory. Information such as how you felt, where you were and the context of the environment can be established. This paper presents the DigMem system, which utilizes distributed mobile services, linked data and machine learning to create such memories. Along with the design of the system, a prototype has also been developed, and two case studies have been undertaken, which successfully create memories. As well as demonstrating how memories are created, a key concern in human digital memory research relates to the amount of data that is generated and stored. In particular, searching this set of big data is a key challenge. In response to this, the paper evaluates the use of machine learning algorithms, as an alternative to SPARQL, and treats searching as a classification problem. In particular, supervised machine learning algorithms are used to find information in semantic annotations, based on probabilistic reasoning. Our approach produces good results with 100% sensitivity, 93% specificity, 93% positive predicted value, 100% negative predicted value, and an overall accuracy of 97%. Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved

An iconic language for the graphical representation of medical concepts

<p>Abstract</p> <p>Background</p> <p>Many medication errors are encountered in drug prescriptions, which would not occur if practitioners could remember the drug properties. They can refer to drug monographs to find these properties, however drug monographs are long and tedious to read during consultation. We propose a two-step approach for facilitating access to drug monographs. The first step, presented here, is the design of a graphical language, called VCM.</p> <p>Methods</p> <p>The VCM graphical language was designed using a small number of graphical primitives and combinatory rules. VCM was evaluated over 11 volunteer general practitioners to assess if the language is easy to learn, to understand and to use. Evaluators were asked to register their VCM training time, to indicate the meaning of VCM icons and sentences, and to answer clinical questions related to randomly generated drug monograph-like documents, supplied in text or VCM format.</p> <p>Results</p> <p>VCM can represent the various signs, diseases, physiological states, life habits, drugs and tests described in drug monographs. Grammatical rules make it possible to generate many icons by combining a small number of primitives and reusing simple icons to build more complex ones. Icons can be organized into simple sentences to express drug recommendations. Evaluation showed that VCM was learnt in 2 to 7 hours, that physicians understood 89% of the tested VCM icons, and that they answered correctly to 94% of questions using VCM (versus 88% using text, <it>p </it>= 0.003) and 1.8 times faster (<it>p </it>< 0.001).</p> <p>Conclusion</p> <p>VCM can be learnt in a few hours and appears to be easy to read. It can now be used in a second step: the design of graphical interfaces facilitating access to drug monographs. It could also be used for broader applications, including the design of interfaces for consulting other types of medical document or medical data, or, very simply, to enrich medical texts.</p