SocialDining: Design and Analysis of a Group Recommendation Application in a Mobile Context

Mobile social networks are rapidly becoming an important new domain showcasing the power of mobile computing systems. These networks combine mobile location information with social networking data to enable fully context-aware environments. This paper describes SocialDining, a system that fuses mobile and social data to power novel context-aware recommendation services that provide recommendations to small groups of users who want to meet together for food or drink at local restaurants. We report our analysis on the data collected from 31 users for the SocialDining application over the course of 15 weeks

Increasing MOOC completion rates through social interactions: a recommendation system

International audienceE-learning research shows students who interact with their peers are less likely to drop out from a course, but is this applicable to MOOCs? This paper examines MOOC attrition issues and how encouraging social interactions can address them: using data from 4 sessions of the GdP MOOC, a popular Project Management MOOC, we confirm that students displaying a high level of social interaction succeed more than those who don't. We successively explore two approaches fostering social interactions: 1) in MOOC GdP5, we give access to private group forums, testing various group types and sizes, 2) in MOOC GdP6, we implement a recommendation system, suggesting relevant chat contacts using demographic and progression criteria. This papers presents our preliminary findings

The design and study of pedagogical paper recommendation

For learners engaging in senior-level courses, tutors in many cases would like to pick some articles as supplementary reading materials for them each week. Unlike researchers ‘Googling’ papers from the Internet, tutors, when making recommendations, should consider course syllabus and their assessment of learners along many dimensions. As such, simply ‘Googling’ articles from the Internet is far from enough. That is, learner models of each individual, including their learning interest, knowledge, goals, etc. should be considered when making paper recommendations, since the recommendation should be carried out so as to ensure that the suitability of a paper for a learner is calculated as the summation of the fitness of the appropriateness of it to help the learner in general. This type of the recommendation is called a Pedagogical Paper Recommender.In this thesis, we propose a set of recommendation methods for a Pedagogical Paper Recommender and study the various important issues surrounding it. Experimental studies confirm that making recommendations to learners in social learning environments is not the same as making recommendation to users in commercial environments such as Amazon.com. In such learning environments, learners are willing to accept items that are not interesting, yet meet their learning goals in some way or another; learners’ overall impression towards each paper is not solely dependent on the interestingness of the paper, but also other factors, such as the degree to which the paper can help to meet their ‘cognitive’ goals.It is also observed that most of the recommendation methods are scalable. Although the degree of this scalability is still unclear, we conjecture that those methods are consistent to up to 50 papers in terms of recommendation accuracy. The experiments conducted so far and suggestions made on the adoption of recommendation methods are based on the data we have collected during one semester of a course. Therefore, the generality of results needs to undergo further validation before more certain conclusion can be drawn. These follow up studies should be performed (ideally) in more semesters on the same course or related courses with more newly added papers. Then, some open issues can be further investigated. Despite these weaknesses, this study has been able to reach the research goals set out in the proposed pedagogical paper recommender which, although sounding intuitive, unfortunately has been largely ignored in the research community. Finding a ‘good’ paper is not trivial: it is not about the simple fact that the user will either accept the recommended items, or not; rather, it is a multiple step process that typically entails the users navigating the paper collections, understanding the recommended items, seeing what others like/dislike, and making decisions. Therefore, a future research goal to proceed from the study here is to design for different kinds of social navigation in order to study their respective impacts on user behavior, and how over time, user behavior feeds back to influence the system performance

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

Coaching : an innovative new methodology for occupational social workers?

The 21st Century has ushered in complex global and economic challenges which have highlighted the need for innovative solutions to meet these challenges. Occupational social work is similarly challenged to investigate alternative methods of service delivery to meet the new workplace demands. Coaching is currently the single most planned developmental tool used in the workplace (O’ Flaherty, 2004). The aim of this study was to investigate occupational social workers knowledge, opinions, skill and interest in coaching as a possible occupational social work intervention. An exploratory research design was utilised in the study. Target sampling was appropriate for the purposes of this study as a target of occupational social workers was required for the final sample. Twelve occupational social workers were approached to participate in the pilot study. The final pilot study sample consisted of eight occupational social workers. The study sample consisted of twenty-eight occupational social workers from the Gauteng province. Data was collected by means of a self-administered questionnaire which was available to respondents in either paper-based or web-based format. The main findings indicated that the majority of occupational social workers (83%) do not possess a comprehensive understanding of coaching. Most (23) respondents (92%) agreed that the attributes of coaching and social work were similar. Ninety-six percent (25) indicated that they would be interested in attending a coaching course. Findings from the study assisted in the development of themes for an introductory coaching course for occupational social workers. The potential value of the study is that it could assist occupational social work educators in curriculum planning for future occupational social work programmes. The major recommendation for occupational social workers is to take personal responsibility for their learning and development and engage in continuing education as a lifestyle in order to remain relevant and keep abreast with organisational developments

Early evaluation of Unistats: user experiences

This paper sets out the findings of the user evaluation of Unistats.UK Higher Education Funding Bodie