Communal Constructivism and Networked Learning: Reflections on a Case Study

Holmes et al. (2001) have suggested that the advent of new educational technologies warrants a new kind of educational theory - 'communal constructivism.' Communal constructivism attempts to move beyond social constructivism and capture specific elements of the additional value that C&IT applications bring to the learning and teaching environment. Our paper will critically evaluate the usefulness of Holmes et al's ideas through a case study of the way in which a virtual learning environment, Blackboard, is currently being used to support students on a level three unit ICT in an Educational Context at Sheffield Hallam University. Key words: communal constructivism, social constructivism, case study, design and pedagog

The Landscape of Connected Cancer Symptom Management in Rural America: A Narrative Review of Opportunities for Launching Connected Health Interventions

Background: The 2016 President’s Cancer Panel called for projects focusing on improving cancer symptom management using connected health technologies (broadband and telecommunications). However, rural communities, like those in Appalachia, may experience a “double burden” of high cancer rates and lower rates of broadband access and adoption necessary for connected health solutions. Purpose: To better understand the current landscape of connected health in the management of cancer symptoms in rural America. Methods: A literature search was conducted using four academic databases (PubMed, CINAHL, MEDLINE, and PsycINFO) to locate articles published from 2010 to 2019 relevant to connected cancer symptom management in rural America. Text screening was conducted to identify relevant publications. Results: Among 17 reviewed studies, four were conducted using a randomized controlled trial; the remainder were formative in design or small pilot projects. Five studies engaged stakeholders from rural communities in designing solutions. Most commonly studied symptoms were psychological/emotional symptoms, followed by physical symptoms, particularly pain. Technologies used were primarily telephone-based; few were Internet-enabled video conferencing or web-based. Advanced mobile and Internet-based approaches were generally in the development phase. Overall, both rural patients and healthcare providers reported high acceptance, usage, and satisfaction of connected health technologies. Ten of the 17 studies reported improved symptom management outcomes. Methodological challenges that limited the interpretation of the findings were summarized. Implications: The review identified a need to engage rural stakeholders to develop and test connected cancer symptom management solutions that are based on advanced mobile and broadband Internet technologies

Do people with ME/CFS and joint hypermobility represent a disease subgroup? An analysis using registry data

BackgroundMyalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a chronic, multifaceted disease that affects millions globally. Despite its significant impact, the disease's etiology remains poorly understood, and symptom heterogeneity poses challenges for diagnosis and treatment. Joint hypermobility, commonly seen in hypermobile Ehlers-Danlos Syndrome (hEDS), has been observed in ME/CFS patients but its prevalence and clinical significance within this population are not well-characterized.ObjectiveTo compare the characteristics of ME/CFS patients with and without joint hypermobility (JH+ and JH-) as assessed using the Beighton scoring system, and to explore whether JH+ ME/CFS patients exhibit distinct disease characteristics, comorbidities, and health-related quality of life (HRQOL).MethodsThe study used cross-sectional, self-reported data from 815 participants of the You + ME Registry. Participants were categorized as JH+ or JH- based on self–assessed Beighton scores and compared across demographics, comorbidities, family history, and symptoms. HRQOL was assessed using the Short Form-36 RAND survey and Karnofsky Performance Status.Results15.5% (N = 126) of participants were classified as JH+. JH+ participants were more likely to be female, report Ehlers-Danlos Syndrome (EDS), Postural Orthostatic Tachycardia Syndrome (POTS), and a family history of EDS. They experienced worse HRQOL, particularly in physical functioning and pain, and a higher number of autonomic, neurocognitive, headache, gut, and musculoskeletal symptoms. Sensitivity analysis suggested that ME/CFS with concurrent JH+ and EDS was associated with more severe symptoms and greater functional impairment.ConclusionME/CFS patients with joint hypermobility, particularly those with EDS, demonstrate distinct clinical characteristics, including more severe symptomatology and reduced HRQOL. These findings highlight the need for comprehensive clinical assessments of ME/CFS patients with joint hypermobility. Understanding these relationships could aid in subgroup identification, improving diagnosis, and informing targeted therapeutic approaches. Further research is warranted to explore these associations and their implications for clinical practice

Exploiting empirical engagement in authenticated protocol design

Abstract. We develop the theme of an earlier paper [3], namely that security protocols for pervasive computing frequently need to exploit empirical channels and that the latter can be classified by variants of the Dolev-Yao attacker model. We refine this classification of channels and study three protocols in depth: two from our earlier paper and one new one.

Optimising vaccination uptake for Covid-19

Geo-spatial data on village locations, their size, population and other parameters is scarcely available to decision makers in many developing countries. In this paper, we demonstrate an automatic ?crawler? which can segment nucleated villages from satellite imagery freely available in public domain geographic information systems such as Google EarthTM. Our approach is to use frequency and color features to generate a number of weak classifiers, which are then combined through Adaboost to produce the final classifier. We use a total of 69 features in the generation of the weak classifiers, including phase gradients, cornerness measures and color features. Our primary dataset consists of 60 images having more than 345 million pixels and covering more than 100 km2 of area, containing nucleated villages in fifteen countries, spread over four continents and captured by different sensors. Using six manual annotations for ground-truth, we perform five-fold cross validation, using 25% of data for testing. Our results show an Equal Error Rate (EER) of around 3.4%. Using the trained classifier, we detect villages on a 50 km2 image (close to 184 million pixels) from a different site than the images used in training, and demonstrate highly accurate extraction of villages with 2.3% false positives and 0.01% false negatives