A virtual surgery in general practice: evaluation of a novel undergraduate virtual patient learning package

Background: A suite of 10 online virtual patients developed using the IVIMEDS ‘Riverside’ authoring tool has been introduced into our undergraduate general practice clerkship. These cases provide a multimedia-rich experience to students. Their interactive nature promotes the development of clinical reasoning skills such as discriminating key clinical features, integrating information from a variety of sources and forming diagnoses and management plans.

Aims: To evaluate the usefulness and usability of a set of online virtual patients in an undergraduate general practice clerkship.
Method: Online questionnaire completed by students after their general practice placement incorporating the System Usability Scale questionnaire.

Results: There was a 57% response rate. Ninety-five per cent of students agreed that the online package was a useful learning tool and ranked virtual patients third out of six learning modalities. Questions and answers and the use of images and videos were all rated highly by students as useful learning methods. The package was perceived to have a high level of usability among respondents.

Conclusion: Feedback from students suggest that this implementation of virtual patients, set in primary care, is user friendly and rated as a valuable adjunct to their learning. The cost of production of such learning resources demands close attention to design.

Telephone survey evaluation of the educational impact of a distance learning course

The educational impact of a distance learning (DL) course entitled ''Health Screening for Health Promotion, was investigated using a telephone questionnaire survey. An introduction to the DL course was distributed to all community pharmacists in England (16,400); the main body of the course, on which pharmacists were examined, was distributed free of charge to all pharmacists who requested it (1,485). Pharmacists participating in the survey (868) were organized by random selection into groups and stratified according to age, sex and postcode. A matched control group was randomly drawn from those pharmacists who had not participated in the course. The DL course improved pharmacists' knowledge about health screening/health promotion issues (e.g., mean score of 66 percent achieved by a group who had completed the course; 51 percent achieved by the control group; P<0.001). Factors influencing score achieved included sex and year of registration. Males performed better than females (P<0.008) while performance decreased with number of years on the register (P<0.001).

Asking the Critical Questions: An Evaluation of Social Work Students' Experiences in an International Placement

Preparing social work students for the demands of changing social environments and to promote student mobility and interest in overseas employment opportunities have resulted in an increasing demand for international social work placements. The literature describes numerous examples of social work programmes that offer a wide variety of international placements. However, research about the actual benefit of undertaking an overseas placement is scant with limited empirical evidence on the profile of students participating, their experience of the tasks offered, the supervisory practice and the outcomes for students' professional learning and career. This study contributes to the existing body of literature by exploring the relevance of international field placements for students and is unique in that it draws its sample from students who have graduated so provides a distinctive perspective in which to compare their international placement with their other placement/s as well as evaluating what were the benefits and drawbacks for them in terms of their careers, employment opportunities and current professional practice.

Evaluation of sampling methods for learning from imbalanced data

The problem of learning from imbalanced data is of critical importance in a large number of application domains and can be a bottleneck in the performance of various conventional learning methods that assume the data distribution to be balanced. The class imbalance problem corresponds to dealing with the situation where one class massively outnumbers the other. The imbalance between majority and minority would lead machine learning to be biased and produce unreliable outcomes if the imbalanced data is used directly. There has been increasing interest in this research area and a number of algorithms have been developed. However, independent evaluation of the algorithms is limited. This paper aims at evaluating the performance of five representative data sampling methods namely SMOTE, ADASYN, BorderlineSMOTE, SMOTETomek and RUSBoost that deal with class imbalance problems. A comparative study is conducted and the performance of each method is critically analysed in terms of assessment metrics. © 2013 Springer-Verlag.

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.