A conceptual analytics model for an outcome-driven quality management framework as part of professional healthcare education

BACKGROUND: Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. OBJECTIVE: The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators’ decision making. METHODS: A deductive case study approach was applied to develop the conceptual model. RESULTS: The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. CONCLUSIONS: The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach

Improving Search through A3C Reinforcement Learning based Conversational Agent

We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities. Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming. We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent. We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user. We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes. Our experiments show that the agent learns to achieve higher rewards and better states.Comment: 17 pages, 7 figure

Deployment of quality assurance procedures for digital library programmes

Many digital library programmes have a development philosophy based on use of open standards. In practice, however, projects may not have procedures in place to ensure that project deliverables make use of appropriate open standards. In addition there will be occasions when open standards are not sufficiently mature for deployment in a service environment or use of open standards will require expertise or resources which are not readily available

Simplifying resource discovery and access in academic libraries : implementing and evaluating Summon at Huddersfield and Northumbria Universities

Facilitating information discovery and maximising value for money from library materials is a key driver for academic libraries, which spend substantial sums of money on journal, database and book purchasing. Users are confused by the complexity of our collections and the multiple platforms to access them and are reluctant to spend time learning about individual resources and how to use them - comparing this unfavourably to popular and intuitive search engines like Google. As a consequence the library may be seen as too complicated and time consuming and many of our most valuable resources remain undiscovered and underused. Federated search tools were the first commercial products to address this problem. They work by using a single search box to interrogate multiple databases (including Library catalogues) and journal platforms. While going some way to address the problem, many users complained that they were still relatively slow, clunky and complicated to use compared to Google or Google Scholar. The emergence of web-scale discovery services in 2009 promised to deal with some of these problems. By harvesting and indexing metadata direct from publishers and local library collections into a single index they facilitate resource discovery and access to multiple library collections (whether in print or electronic form) via a single search box. Users no longer have to negotiate a number of separate platforms to find different types of information and because the data is held in a single unified index searching is fast and easy. In 2009 both Huddersfield and Northumbria Universities purchased Serials Solutions Summon. This case study report describes the selection, implementation and testing of Summon at both Universities drawing out common themes as well as differences; there are suggestions for those who intend to implement Summon in the future and some suggestions for future development

Enhancing Web-Based Configuration with Recommendations and Cluster-Based Help

In a collaborative project with Tacton AB, we have investigated new ways of assisting the user in the process of on-line product configuration. A web-based prototype, RIND, was built for ephemeral users in the domain of PC configuration

Internet-based medical education: a realist review of what works, for whom and in what circumstances

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Designing the printed book as an interactive environment

Reading a book demands a certain level of interaction from the reader. The cover must be opened and pages turned to navigate the information inside. Conventions have been developed over the life of the book to assist the reader in this navigation and provide orientation. The evolution of electronic reading material has given readers greater opportunities for interacting with their reading material, but many readers still prefer reading from a printed book. This paper investigates how the interactive organizational paradigm of hypertext can be implemented in a printed book to give the reader the opportunity for greater interaction and benefit from some of the advantages that electronic reading environments provide. The investigation in this paper follows an iterative design process in consultation with a panel of four experts. Through four rounds of consultation and refinement two potential solutions were developed for the incorporation of hypertext methods in a printed book

Recommender System Using Collaborative Filtering Algorithm

With the vast amount of data that the world has nowadays, institutions are looking for more and more accurate ways of using this data. Companies like Amazon use their huge amounts of data to give recommendations for users. Based on similarities among items, systems can give predictions for a new item’s rating. Recommender systems use the user, item, and ratings information to predict how other users will like a particular item. Recommender systems are now pervasive and seek to make profit out of customers or successfully meet their needs. However, to reach this goal, systems need to parse a lot of data and collect information, sometimes from different resources, and predict how the user will like the product or item. The computation power needed is considerable. Also, companies try to avoid flooding customer mailboxes with hundreds of products each morning, thus they are looking for one email or text that will make the customer look and act. The motivation to do the project comes from my eagerness to learn website design and get a deep understanding of recommender systems. Applying machine learning dynamically is one of the goals that I set for myself and I wanted to go beyond that and verify my result. Thus, I had to use a large dataset to test the algorithm and compare each technique in terms of error rate. My experience with applying collaborative filtering helps me to understand that finding a solution is not enough, but to strive for a fast and ultimate one. In my case, testing my algorithm in a large data set required me to refine the coding strategy of the algorithm many times to speed the process. In this project, I have designed a website that uses different techniques for recommendations. User-based, Item-based, and Model-based approaches of collaborative filtering are what I have used. Every technique has its way of predicting the user rating for a new item based on existing users’ data. To evaluate each method, I used Movie Lens, an external data set of users, items, and ratings, and calculated the error rate using Mean Absolute Error Rate (MAE) and Root Mean Squared Error (RMSE). Finally, each method has its strengths and weaknesses that relate to the domain in which I am applying these methods

An evaluation of e-learning standards

The aim of this investigation is to perform an independent study of the various emerging elearning standards. This paper presents a summary of these standards in order to make them more accessible and understandable, and provide preliminary evidence as to their utility and adoption by the various UK higher and further education institutions. Recently there have been efforts to define standards for the elearning contents and elearning components like the IEEELOM, UKLOM, IMS, SCORM and OKI. Since it was not possible to cover all the standards in detail within the time available, so our independent study focuses on eight standards Although the results of the preliminary study suggest that the eight standards considered in the study may help interoperability, accessibility and reusability of the elearning content and elearning components, but it is yet to be seen how many of these are actually followed at UK higher education institutions