Theorizing why in e-learning – a frontier for cognitive engagement

Asking why is an important foundation of inquiry and fundamental to the development of reasoning skills and learning. Despite this, and despite the relentless and often disruptive nature of innovations in information and communications technology (ICT), sophisticated tools that directly support this basic act of learning appear to be undeveloped, not yet recognized, or in the very early stages of development. Why is this so? To this question, there is no single satisfactory answer; instead, numerous plausible explanations and related questions arise. After learning something, however, explaining why can be revealing of a person’s understanding (or lack of it). What then differentiates explanation from information; and, explanatory from descriptive content? What ICT scaffolding might support inquiry instigated by why-questioning? What is the role of reflective practice in inquiry-based learning? These and other questions have emerged from this investigation and underscore that why-questions often propagate further questions and are a catalyst for cognitive engagement and dialogue. This paper reports on a multi-disciplinary, theoretical investigation that informs the broad discourse on e-learning and points to a specific frontier for design and development of e-learning tools. Probing why reveals that versatile and ambiguous semantics present the core challenge – asking, learning, knowing, understanding, and explaining why.

Pattern-based topic models for information filtering

Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, which has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering is rarely known. Patterns are always thought to be more representative than single terms for representing documents. In this paper, a novel information filtering model, Pattern-based Topic Model(PBTM) , is proposed to represent the text documents not only using the topic distributions at general level but also using semantic pattern representations at detailed specific level, both of which contribute to the accurate document representation and document relevance ranking. Extensive experiments are conducted to evaluate the effectiveness of PBTM by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model achieves outstanding performance.

A cognitive processing framework for learning analytics

Incorporating a learner’s level of cognitive processing into Learning Analytics presents opportunities for obtaining rich data on the learning process. We propose a framework called COPA that provides a basis for mapping levels of cognitive operation into a learning analytics system. We utilise Bloom’s taxonomy, a theoretically respected conceptualisation of cognitive processing, and apply it in a flexible structure that can be implemented incrementally and with varying degree of complexity within an educational organisation. We outline how the framework is applied, and its key benefits and limitations. Finally, we apply COPA to a University undergraduate unit, and demonstrate its utility in identifying key missing elements in the structure of the course.

The art of mooting : mooting and the cognitive domain

Mooting is modeled principally on appellate advocacy. However, the skill set developed by participating in a moot program – being that necessary to persuade someone to your preferred position – is indispensible to anyone practising law. Developing effective mooting skills in students necessitates the engagement of coaches with an appropriate understanding of the theories underlying mooting and advocacy practice and their interconnection with each other. This article explains the relevance of the cognitive domain to mooting performance and places it in context with the psychomotor and affective domains.

Applying the ecological model of behavior change to a physical activity trial in retirement communities : description of the study protocol

Objectives To describe the intervention protocol for the first multilevel ecological intervention for physical activity in retirement communities that addresses individual, interpersonal and community influences on behavior change. Design A cluster randomized controlled trial design was employed with two study arms: a physical activity intervention and an attention control successful aging condition. Setting Sixteen continuing care retirement communities in San Diego County. Participants Three hundred twenty older adults, aged 65 years and older, are being recruited to participate in the trial. In addition, peer leaders are being recruited to lead some study activities, especially to sustain the intervention after study activities ceased. Intervention Participants in the physical activity trial receive individual, interpersonal and community intervention components. The individual level components include pedometers, goal setting and individual phone counseling. The interpersonal level components include group education sessions and peer-led activities. The community level components include resource audits and enumeration, tailored walking maps, and community improvement projects. The successful aging group receives individual and group attention about successful aging topics. Measurements The main outcome is light to moderate physical activity, measured objectively by accelerometry. Other objective outcomes included physical functioning, blood pressure, physical fitness, and cognitive functioning. Self report measures include depressive symptoms and health related quality of life. Results The intervention is being delivered successfully in the communities and compliance rates are high. Conclusion Ecological Models call for interventions that address multiple levels of the model. Previous studies have not included components at each level and retirement communities provide a model environment to demonstrate how to implement such an intervention.

In vivo and in vitro models demonstrate a role for caveolin-1 in the pathogenesis of ischaemic acute renal failure

Caveolae and their proteins, the caveolins, transport macromolecules; compartmentalize signalling molecules; and are involved in various repair processes. There is little information regarding their role in the pathogenesis of significant renal syndromes such as acute renal failure (ARF). In this study, an in vivo rat model of 30 min bilateral renal ischaemia followed by reperfusion times from 4 h to 1 week was used to map the temporal and spatial association between caveolin-1 and tubular epithelial damage (desquamation, apoptosis, necrosis). An in vitro model of ischaemic ARF was also studied, where cultured renal tubular epithelial cells or arterial endothelial cells were subjected to injury initiators modelled on ischaemia-reperfusion (hypoxia, serum deprivation, free radical damage or hypoxia-hyperoxia). Expression of caveolin proteins was investigated using immunohistochemistry, immunoelectron microscopy, and immunoblots of whole cell, membrane or cytosol protein extracts. In vivo, healthy kidney had abundant caveolin-1 in vascular endothelial cells and also some expression in membrane surfaces of distal tubular epithelium. In the kidneys of ARF animals, punctate cytoplasmic localization of caveolin-1 was identified, with high intensity expression in injured proximal tubules that were losing basement membrane adhesion or were apoptotic, 24 h to 4 days after ischaemia-reperfusion. Western immunoblots indicated a marked increase in caveolin-1 expression in the cortex where some proximal tubular injury was located. In vitro, the main treatment-induced change in both cell types was translocation of caveolin-1 from the original plasma membrane site into membrane-associated sites in the cytoplasm. Overall, expression levels did not alter for whole cell extracts and the protein remained membrane-bound, as indicated by cell fractionation analyses. Caveolin-1 was also found to localize intensely within apoptotic cells. The results are indicative of a role for caveolin-1 in ARF-induced renal injury. Whether it functions for cell repair or death remains to be elucidated.

Denial predicts outcome in anxiety following group cognitive behavioral therapy

This study aimed to explore whether participants' pretherapy coping strategies predicted the outcome of group cognitive behavioral therapy (CBT) for anxiety and depression. It was hypothesized that adaptive coping strategies such as the use of active planning and acceptance would be associated with higher reductions, whereas maladaptive coping strategies such as denial and disengagement would be associated with lower reductions in anxious and depressed symptoms following psychotherapy. There were 144 participants who completed group CBT for anxiety and depression. Measures of coping strategies were administered prior to therapy, whereas measures of depression and anxiety were completed both prior to and following therapy. The results showed that higher levels of denial were associated with a poorer outcome, in terms of change in anxiety but not depression, following therapy. These findings suggest the usefulness of using the Denial subscale from the revised Coping Orientation to Problems Experienced (COPE) as a predictor of outcome in group CBT for anxiety.

Impact of cognitive behaviour therapy via mail for cessation of benzodiazepine use : a series of case reports

Benzodiazepines are widely prescribed to manage sleep disorders, anxiety and muscular tension. While providing short-term relief, continued use induces tolerance and withdrawal, and in older users, increases the risk of falls. However, long-term prescription remains common, and effective interventions are not widely available. This study developed a self-managed cognitive behaviour therapy package for cessation of benzodiazepine use delivered to participants via mail (M-CBT) and trialled its effectiveness as an adjunct to a general practitioner (GP)-managed dose reduction schedule. In the pilot trial, participants were randomly assigned to GP management with immediate or delayed M-CBT. Significant recruitment and engagement problems were experienced, and only three participants were allocated to each condition. After immediate M-CBT, two participants ceased use, while none receiving delayed treatment reduced daily intake by more than 50%. Across the sample, doses at 12 months remained significantly lower than baseline, and qualitative feedback from participants was positive. While M-CBT may have promise, improved engagement of GPs and participants is needed for this approach to substantially impact on community-wide benzodiazepine use.

The relationship between cannabis outcome expectancies and cannabis refusal self-efficacy in a treatment population

Background and aims Self-efficacy beliefs and outcome expectancies are central to Social Cognitive Theory (SCT). Alcohol studies demonstrate the theoretical and clinical utility of applying both SCT constructs. This study examined the relationship between refusal self-efficacy and outcome expectancies in a sample of cannabis users, and tested formal mediational models. Design Patients referred for cannabis treatment completed a comprehensive clinical assessment, including recently validated cannabis expectancy and refusal self-efficacy scales. Setting A hospital alcohol and drug out-patient clinic. Participants Patients referred for a cannabis treatment [n = 1115, mean age 26.29, standard deviation (SD) 9.39]. Measurements The Cannabis Expectancy Questionnaire (CEQ) and Cannabis Refusal Self-Efficacy Questionnaire (CRSEQ) were completed, along with measures of cannabis severity [Severity of Dependence Scale (SDS)] and cannabis consumption. Findings Positive (β = −0.29, P < 0.001) and negative (β = −0.19, P < 0.001) cannabis outcome expectancies were associated significantly with refusal self-efficacy. Refusal self-efficacy, in turn, fully mediated the association between negative expectancy and weekly consumption [95% confidence interval (CI) = 0.03, 0.17] and partially mediated the effect of positive expectancy on weekly consumption (95% CI = 0.06, 0.17). Conclusions Consistent with Social Cognitive Theory, refusal self-efficacy (a person's belief that he or she can abstain from cannabis use) mediates part of the association between cannabis outcome expectancies (perceived consequences of cannabis use) and cannabis use.

Building adaptable agent-based models – application to the electricity distribution network

Agent-based modelling (ABM), like other modelling techniques, is used to answer specific questions from real world systems that could otherwise be expensive or impractical. Its recent gain in popularity can be attributed to some degree to its capacity to use information at a fine level of detail of the system, both geographically and temporally, and generate information at a higher level, where emerging patterns can be observed. This technique is data-intensive, as explicit data at a fine level of detail is used and it is computer-intensive as many interactions between agents, which can learn and have a goal, are required. With the growing availability of data and the increase in computer power, these concerns are however fading. Nonetheless, being able to update or extend the model as more information becomes available can become problematic, because of the tight coupling of the agents and their dependence on the data, especially when modelling very large systems. One large system to which ABM is currently applied is the electricity distribution where thousands of agents representing the network and the consumers’ behaviours are interacting with one another. A framework that aims at answering a range of questions regarding the potential evolution of the grid has been developed and is presented here. It uses agent-based modelling to represent the engineering infrastructure of the distribution network and has been built with flexibility and extensibility in mind. What distinguishes the method presented here from the usual ABMs is that this ABM has been developed in a compositional manner. This encompasses not only the software tool, which core is named MODAM (MODular Agent-based Model) but the model itself. Using such approach enables the model to be extended as more information becomes available or modified as the electricity system evolves, leading to an adaptable model. Two well-known modularity principles in the software engineering domain are information hiding and separation of concerns. These principles were used to develop the agent-based model on top of OSGi and Eclipse plugins which have good support for modularity. Information regarding the model entities was separated into a) assets which describe the entities’ physical characteristics, and b) agents which describe their behaviour according to their goal and previous learning experiences. This approach diverges from the traditional approach where both aspects are often conflated. It has many advantages in terms of reusability of one or the other aspect for different purposes as well as composability when building simulations. For example, the way an asset is used on a network can greatly vary while its physical characteristics are the same – this is the case for two identical battery systems which usage will vary depending on the purpose of their installation. While any battery can be described by its physical properties (e.g. capacity, lifetime, and depth of discharge), its behaviour will vary depending on who is using it and what their aim is. The model is populated using data describing both aspects (physical characteristics and behaviour) and can be updated as required depending on what simulation is to be run. For example, data can be used to describe the environment to which the agents respond to – e.g. weather for solar panels, or to describe the assets and their relation to one another – e.g. the network assets. Finally, when running a simulation, MODAM calls on its module manager that coordinates the different plugins, automates the creation of the assets and agents using factories, and schedules their execution which can be done sequentially or in parallel for faster execution. Building agent-based models in this way has proven fast when adding new complex behaviours, as well as new types of assets. Simulations have been run to understand the potential impact of changes on the network in terms of assets (e.g. installation of decentralised generators) or behaviours (e.g. response to different management aims). While this platform has been developed within the context of a project focussing on the electricity domain, the core of the software, MODAM, can be extended to other domains such as transport which is part of future work with the addition of electric vehicles.

Comprehensive Addictive Behaviors and Disorders, Volume 1 : Principles of Addiction

"Principles of Addiction provides a solid understanding of the definitional and diagnostic differences between use, abuse, and disorder. It describes in great detail the characteristics of these syndromes and various etiological models. The book's three main sections examine the nature of addiction, including epidemiology, symptoms, and course; alcohol and drug use among adolescents and college students; and detailed descriptions of a wide variety of addictive behaviors and disorders, encompassing not only drugs and alcohol, but caffeine, food, gambling, exercise, sex, work, social networking, and many other areas. This volume is especially important in providing a basic introduction to the field as well as an in-depth review of our current understanding of the nature and process of addictive behaviors. Principles of Addiction is one of three volumes comprising the 2,500-page series, Comprehensive Addictive Behaviors and Disorders. This series provides the most complete collection of current knowledge on addictive behaviors and disorders to date. In short, it is the definitive reference work on addictions."--publisher website

Social engineering in social networking sites : phase-based and source-based models

Social networking sites (SNSs), with their large numbers of users and large information base, seem to be perfect breeding grounds for exploiting the vulnerabilities of people, the weakest link in security. Deceiving, persuading, or influencing people to provide information or to perform an action that will benefit the attacker is known as “social engineering.” While technology-based security has been addressed by research and may be well understood, social engineering is more challenging to understand and manage, especially in new environments such as SNSs, owing to some factors of SNSs that reduce the ability of users to detect the attack and increase the ability of attackers to launch it. This work will contribute to the knowledge of social engineering by presenting the first two conceptual models of social engineering attacks in SNSs. Phase-based and source-based models are presented, along with an intensive and comprehensive overview of different aspects of social engineering threats in SNSs.

Future-proofing the pharmacy profession in a hypercompetitive market

This paper highlights the hypercompetitive nature of the current pharmacy landscape in Australia and to suggest either a superior level of differentiation strategy or a focused differentiation strategy targeting a niche market as two viable, alternative business models to cost leadership for small, independent community pharmacies. A description of the Australian health care system is provided as well as background information on the current community pharmacy environment in Australia. The authors propose a differentiation or focused differentiation strategy based on cognitive professional services (CPS) which must be executed well and of a superior quality to competitors' services. Market research to determine the services valued by target customers and that they are willing to pay for is vital. To achieve the superior level of quality that will engender high patient satisfaction levels and loyalty, pharmacy owners and managers need to develop, maintain and clearly communicate service quality specifications to the staff delivering these services. Otherwise, there will be a proliferation of pharmacies offering the same professional services with no evident service differential. However, to sustain competitive advantage over the long-term, these smaller, independent community pharmacies will need to exploit a broad core competency base in order to be able to continuously introduce new sources of competitive advantage. With the right expertise, the authors argue that smaller, independent community pharmacies can successfully deliver CPS and sustain profitability in a hypercompetitive market.

RatSLAM : using models of rodent hippocampus for robot navigation and beyond

We describe recent biologically-inspired mapping research incorporating brain-based multi-sensor fusion and calibration processes and a new multi-scale, homogeneous mapping framework. We also review the interdisciplinary approach to the development of the RatSLAM robot mapping and navigation system over the past decade and discuss the insights gained from combining pragmatic modelling of biological processes with attempts to close the loop back to biology. Our aim is to encourage the pursuit of truly interdisciplinary approaches to robotics research by providing successful case studies.