Evaluation of a novel digital environment for learning medical parasitology.

open access articleEukaryotic parasites represent a serious human health threat requiring health professionals with parasitology skills to counteract this threat. However, recent surveys highlight an erosion of teaching of parasitology in medical and veterinary schools, despite reports of increasing instances of food and water borne parasitic infections. To address this we developed a web-based resource, DMU e-Parasitology®, to facilitate the teaching and learning of parasitology, comprising four sections: theoretical; virtual laboratory; virtual microscopy; virtual clinical case studies. Testing the package was performed using a questionnaire given to ninety-five Pharmacy students in 2017/18 to assess effectiveness of the package as a teaching and learning tool. 89.5% of students reported appropriate acquisition of knowledge of the pathology, prevention and treatment of some parasitic diseases. 82.1% also welcomed the clinical specialism of the package as it helped them to acquire basic diagnostic skills, through learning infective features/morphology of the parasites

Emergent Frameworks for Decision Support Systems

Knowledge is generated and accessed from heterogeneous spaces. The recent advances in in-formation technologies provide enhanced tools for improving the efficiency of knowledge-based decision support systems. The purpose of this paper is to present the frameworks for developing the optimal blend of technologies required in order to better the knowledge acquisition and reuse in large scale decision making environments. The authors present a case study in the field of clinical decision support systems based on emerging technologies. They consider the changes generated by the upraising social technologies and the challenges brought by the interactive knowledge building within vast online communities.Knowledge Acquisition, CDDSS, 2D Barcodes, Mobile Interface

A literature review of expert problem solving using analogy

We consider software project cost estimation from a problem solving perspective. Taking a cognitive psychological approach, we argue that the algorithmic basis for CBR tools is not representative of human problem solving and this mismatch could account for inconsistent results. We describe the fundamentals of problem solving, focusing on experts solving ill-defined problems. This is supplemented by a systematic literature review of empirical studies of expert problem solving of non-trivial problems. We identified twelve studies. These studies suggest that analogical reasoning plays an important role in problem solving, but that CBR tools do not model this in a biologically plausible way. For example, the ability to induce structure and therefore find deeper analogies is widely seen as the hallmark of an expert. However, CBR tools fail to provide support for this type of reasoning for prediction. We conclude this mismatch between experts’ cognitive processes and software tools contributes to the erratic performance of analogy-based prediction