Analytics and complexity: learning and leading for the future

There is growing interest in the application of learning analytics to manage, inform and improve learning and teaching within higher education. In particular, learning analytics is seen as enabling data-driven decision making as universities are seeking to respond a range of significant challenges that are reshaping the higher education landscape. Experience over four years with a project exploring the use of learning analytics to improve learning and teaching at a particular university has, however, revealed a much more complex reality that potentially limits the value of some analytics-based strategies. This paper uses this experience with over 80,000 students across three learning management systems, combined with literature from complex adaptive systems and learning analytics to identify the source and nature of these limitations along with a suggested path forward

Breaking BAD to bridge the reality/rhetoric chasm

The reality of using digital technologies to enhance learning and teaching has a history of falling short of the rhetoric. Past attempts at bridging this chasm have tried: increasing the perceived value of teaching; improving the pedagogical and technological knowledge of academics; redesigning organisational policies, processes and support structures; and, designing and deploying better pedagogical techniques and technologies. Few appear to have had any significant, widespread impact, perhaps because of the limitations of the (often implicit) theoretical foundations of the institutional implementation of e-learning. Using a design-based research approach, this paper develops an alternate theoretical framework (the BAD framework) for institutional e-learning and uses that framework to analyse the development, evolution, and very different applications of the Moodle Activity Viewer (MAV) at two separate universities. Based on this experience it is argued that the reality/rhetoric chasm is more likely to be bridged by interweaving the BAD framework into existing practice

Sick of the sick role: narratives of what ‘recovery’ means to people with chronic fatigue syndrome/myalgic encephalomyelitis

Little is known about what recovery means to those with chronic fatigue syndrome/myalgic encephalomyelitis, a poorly understood, disabling chronic health condition. To explore this issue, semi-structured interviews were conducted with patients reporting improvement (n=9) and deterioration (n=10) after a guided self-help intervention, and analysed via “constant comparison”. The meaning of recovery differed between participants - expectations for improvement and deployment of the sick role (and associated stigma) were key influences. Whilst some saw recovery as complete freedom from symptoms, many defined it as freedom from the ‘sick role’, with functionality prioritized. Others redefined recovery, reluctant to return to the lifestyle that may have contributed to their illness, or rejected the concept as unhelpful. Recovery is not always about eliminating all symptoms. Rather, it is a nexus between the reality of limited opportunities for full recovery, yet a strong desire to leave the illness behind and regain a sense of ‘normality’

Clustering South African households based on their asset status using latent variable models

The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and described by clustering the households into homogeneous groups based on their asset status. A model-based approach to clustering the Agincourt households, based on latent variable models, is proposed. In the case of modeling binary or ordinal items, item response theory models are employed. For nominal survey items, a factor analysis model, similar in nature to a multinomial probit model, is used. Both model types have an underlying latent variable structure - this similarity is exploited and the models are combined to produce a hybrid model capable of handling mixed data types. Further, a mixture of the hybrid models is considered to provide clustering capabilities within the context of mixed binary, ordinal and nominal response data. The proposed model is termed a mixture of factor analyzers for mixed data (MFA-MD). The MFA-MD model is applied to the survey data to cluster the Agincourt households into homogeneous groups. The model is estimated within the Bayesian paradigm, using a Markov chain Monte Carlo algorithm. Intuitive groupings result, providing insight to the different socio-economic strata within the Agincourt region.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS726 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

This patient is not breathing properly: is this COPD, heart failure, or neither?

© 2017 Informa UK Limited, trading as Taylor & Francis Group. Introduction: Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are two common, heterogeneous, long-term illnesses which cause significant morbidity and mortality. Although they both present with breathlessness, they are treated differently. Treatment of COPD focuses mainly on relieving short-term breathlessness, whilst treatment of HF has focused on long term morbidity and mortality. Areas covered: In this review, we aim to highlight the diagnostic challenges in distinguishing COPD from HF. We also explore the implications of their overlap, and the use of biomarkers and treatments for HF in patients with COPD to improve long-term outcomes. Expert commentary: Cardiovascular morbidity and mortality amongst patients with COPD is substantial. Approaches which identify patients with COPD at highest cardiovascular risk may therefore be helpful. A trial targeting those patients with COPD and raised natriuretic peptide levels might be the way to test whether cardiovascular medication has anything to offer the respiratory patient

Graded Exercise Therapy Guided Self-Help Trial for Patients with Chronic Fatigue Syndrome (GETSET): Protocol for a Randomized Controlled Trial and Interview Study

Background: Chronic fatigue syndrome, also known as myalgic encephalomyelitis (CFS/ME), is characterized by chronic disabling fatigue and other symptoms, which are not explained by an alternative diagnosis. Previous trials have suggested that graded exercise therapy (GET) is an effective and safe treatment. GET itself is therapist-intensive with limited availability. Objective: While guided self-help based on cognitive behavior therapy appears helpful to patients, Guided graded Exercise Self-help (GES) is yet to be tested. Methods: This pragmatic randomized controlled trial is set within 2 specialist CFS/ME services in the South of England. Adults attending secondary care clinics with National Institute for Health and Clinical Excellence (NICE)-defined CFS/ME (N=218) will be randomly allocated to specialist medical care (SMC) or SMC plus GES while on a waiting list for therapist-delivered rehabilitation. GES will consist of a structured booklet describing a 6-step graded exercise program, supported by up to 4 face-to-face/telephone/Skype™ consultations with a GES-trained physiotherapist (no more than 90 minutes in total) over 8 weeks. The primary outcomes at 12-weeks after randomization will be physical function (SF-36 physical functioning subscale) and fatigue (Chalder Fatigue Questionnaire). Secondary outcomes will include healthcare costs, adverse outcomes, and self-rated global impression change scores. We will follow up all participants until 1 year after randomization. We will also undertake qualitative interviews of a sample of participants who received GES, looking at perceptions and experiences of those who improved and worsened. Results: The project was funded in 2011 and enrolment was completed in December 2014, with follow-up completed in March 2016. Data analysis is currently underway and the first results are expected to be submitted soon. Conclusions: This study will indicate whether adding GES to SMC will benefit patients who often spend many months waiting for rehabilitative therapy with little or no improvement being made during that time. The study will indicate whether this type of guided self-management is cost-effective and safe. If this trial shows GES to be acceptable, safe, and comparatively effective, the GES booklet could be made available on the Internet as a practitioner and therapist resource for clinics to recommend, with the caveat that patients also be supported with guidance from a trained physiotherapist. The pragmatic approach in this trial means that GES findings will be generalizable to usual National Health Service (NHS) practice