Patients' experiences of support for learning to live with diabetes to promote health and well-being : A lifeworld phenomenological study

Learning to live with diabetes in such a way that the new conditions will be a normal and natural part of life imposes requirements on the person living with diabetes. Previous studies have shown that there is no clear picture of what and how the learning that would allow persons to incorporate the illness into their everyday life will be supported. The aim of this study is to describe the phenomenon of support for learning to live with diabetes to promote health and well-being, from the patient's perspective. Data were collected by interviews with patients living with type 1 or type 2 diabetes. The interviews were analysed using a reflective lifeworld approach. The results show that reflection plays a central role for patients with diabetes in achieving a new understanding of the health process, and awareness of their own responsibility was found to be the key factor for such a reflection. The constituents are responsibility creating curiosity and willpower, openness enabling support, technology verifying bodily feelings, a permissive climate providing for participation and exchanging experiences with others. The study concludes that the challenge for caregivers is to create interactions in an open learning climate that initiates and supports reflection to promote health and well-being.

Moving together – conditions for intercultural development at a highly diverse Swedish school

This thesis is a case study of a primary school in a highly diverse urban neighbourhood in Sweden. Basic pre-conditions for intercultural school development are studied by examining the overall organisation of teaching, learning and opportunities for collaboration in the investigated case. The study focuses on the targeted support measures to enhance learning for students with an immigrant background: Mother tongue instruction, Swedish as a Second Language, and tutoring in the mother tongue, as well as looking at pedagogical support provided by the school library. The latter has a mission to promote learning and inclusion, where non-native speakers of Swedish are a prioritised group. Communities of practice linked to the work organisation at a meso-level are investigated, and the collaborative relationships between professional groups at the school involved in the various support measures. Teacher relationships and categorisations implied by support measures impact the learning spaces that are shaped for students and the teaching spaces within which teachers work. Collaborative opportunities and convergence of concerns in the teaching spaces combine to shape the overall space for intercultural development. The raw data for the case study consists of interviews, national policy documents and additional information on local work organisation gained through documents and observations. Four articles resulted from the case study, each focusing a specific support measure. An overarching analysis is then made of findings from these articles and the other dimensions of the investigation. The analysis describes the organisation in terms of monocultural or intercultural school cultures, pointing to significant characteristics of the landscapes of practice, with respect to their overall implications for the spaces of school development. In the discussion, findings are considered in relation to research on professional development in education, collaboration, democracy and inclusive schooling. The relative positioning of languages and cultures is given particular attention, to ascertain if the school culture is monocultural or intercultural in the sense given by Lahdenperä (2008), and to what extent it could enable intercultural development. Such positioning plays a role interms of affordances for identity, participation and engagement discussed by Wenger (1998). This case study should be understood against the wider background of recent social developments in Europe linked to globalisation and technological changes. It is argued that looking at the concrete specifics which facilitate or obstruct school development, and simultaneously reflecting on how the different forms of teaching interrelate in the overall organisation and in policy may provide a useful vantage point from which structural changes can be contemplated.The discussion underlines the importance of the physical localisation of activities, continuity in personal contacts and time available for joint pedagogical reflection, as basic conditions for effective intercultural dialogue in the organisation. Finally, the impact of policy is considered, looking at connections between levels of policy, expressed in official steering documents, and conditions for teaching and learning at the level of an individual school.

Dokumentation der Befragungsinstrumente der Interventionsstudie im Projekt “Conditions and Consequences of Classroom Assessment” (Co²CA)

Das im Rahmen des DFG-Schwerpunktprogramms „Kompetenzmodelle“ durchgeführte Projekt „Conditions and Consequences of Classroom Assessment“ (Co²CA) geht in vier Teilstudien der Frage nach, wie formatives Assessment im Unterricht gestaltet werden kann, um sowohl eine präzise Leistungsmessung zu ermöglichen als auch positive Wirkungen auf den Lehr-Lernprozess zu erreichen. Das Project Co²CA leistet damit einen wichtigen Beitrag zur Erforschung zweier Kernelemente formativen Assessments – der detaillierten Diagnose von Schülerleistungen und der Nutzung der gewonnenen Informationen in Form lernförderlichen Feedbacks. Zentrale Idee von formativem Assessment (Lernbegleitende Leistungsbeurteilung und –rückmeldung) ist es mit Hilfe von Leistungsmessungen Informationen über den Lernstand der Schülerinnen und Schüler zu gewinnen und diese Informationen für die Gestaltung des weiteren Lehr- und Lernprozess zu nutzen. Den Lernenden kann auf Basis der Leistungsbeurteilung lernförderliches Feedback gegeben werden, um so die Diskrepanz zwischen Lernstand und Lernziel zu verringern. Die Kernelemente formativem Assessments bestehen also aus einer detaillierten Diagnose des Lernstandes und der Nutzung der gewonnen Informationen – z.B. in Form von Feedback. [...] Das vorliegende Skalenhandbuch dokumentiert die in der Unterrichtsstudie eingesetzten Befragungsinstrumente für Schülerinnen und Schüler sowie für Lehrkräfte. (DIPF/Autor)

Distributional effects and individual differences in L2 morphology learning

Second language (L2) learning outcomes may depend on the structure of the input and learners’ cognitive abilities. This study tested whether less predictable input might facilitate learning and generalization of L2 morphology while evaluating contributions of statistical learning ability, nonverbal intelligence, phonological short-term memory, and verbal working memory. Over three sessions, 54 adults were exposed to a Russian case-marking paradigm with a balanced or skewed item distribution in the input. Whereas statistical learning ability and nonverbal intelligence predicted learning of trained items, only nonverbal intelligence also predicted generalization of case-marking inflections to new vocabulary. Neither measure of temporary storage capacity predicted learning. Balanced, less predictable input was associated with higher accuracy in generalization but only in the initial test session. These results suggest that individual differences in pattern extraction play a more sustained role in L2 acquisition than instructional manipulations that vary the predictability of lexical items in the input.

Creating Common Ground: Architecture For Tactical Learning and Creative Convergence

Certain environments can inhibit learning and stifle enthusiasm, while others enhance learning or stimulate curiosity. Furthermore, in a world where technological change is accelerating we could ask how might architecture connect resource abundant and resource scarce innovation environments? Innovation environments developed out of necessity within urban villages and those developed with high intention and expectation within more institutionalized settings share a framework of opportunity for addressing change through learning and education. This thesis investigates formal and informal learning environments and how architecture can stimulate curiosity, enrich learning, create common ground, and expand access to education. The reason for this thesis exploration is to better understand how architects might design inclusive environments that bring people together to build sustainable infrastructure encouraging innovation and adaptation to change for years to come. The context of this thesis is largely based on Colin McFarlane’s theory that the “city is an assemblage for learning” The socio-spatial perspective in urbanism, considers how built infrastructure and society interact. Through the urban realm, inhabitants learn to negotiate people, space, politics, and resources affecting their daily lives. The city is therefore a dynamic field of emergent possibility. This thesis uses the city as a lens through which the boundaries between informal and formal logics as well as the public and private might be blurred. Through analytical processes I have examined the environmental devices and assemblage of factors that consistently provide conditions through which learning may thrive. These parameters that make a creative space significant can help suggest the design of common ground environments through which innovation is catalyzed.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Learning Management Systems (LMS) and e-learning management: an integrative review and research agenda

Information Technology (IT) can be an important component for innovation since enabling e-learning it can provide conditions to which the organization can work with new business and improved processes. In this regard, the Learning Management Systems (LMS) allows communication and interaction between teachers and students in virtual spaces. However the literature indicates that there are gaps in the researches, especially concerning the use of IT for the management of e-learning. The purpose of this paper is to analyze the available literature about the application of LMS for the e-learning management, seeking to present possibilities for researches in the field. An integrative literature review was performed considering the Web of Science, Scopus, Ebsco and Scielo databases, where 78 references were found, of which 25 were full papers. This analysis derives interesting characteristics from scientific studies, highlighting gaps and guidelines for future research.

Metal measurement in aquatic environments by passive sampling methods: Lessons learning from an in situ intercomparison exercise

Passive sampling devices (PS) are widely used for pollutant monitoring in water, but estimation of measurement uncertainties by PS has seldom been undertaken. The aim of this work was to identify key parameters governing PS measurements of metals and their dispersion. We report the results of an in situ intercomparison exercise on diffusive gradient in thin films (DGT) in surface waters. Interlaboratory uncertainties of time-weighted average (TWA) concentrations were satisfactory (from 28% to 112%) given the number of participating laboratories (10) and ultra-trace metal concentrations involved. Data dispersion of TWA concentrations was mainly explained by uncertainties generated during DGT handling and analytical procedure steps. We highlight that DGT handling is critical for metals such as Cd, Cr and Zn, implying that DGT assembly/dismantling should be performed in very clean conditions. Using a unique dataset, we demonstrated that DGT markedly lowered the LOQ in comparison to spot sampling and stressed the need for accurate data calculation.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

The Impact of Motor Learning on Motor Behavior and Cortical Dynamics in a Complex Stressful Social Environment

An economy of effort is a core characteristic of highly skilled motor performance often described as being effortless or automatic. Electroencephalographic (EEG) evaluation of cortical activity in elite performers has consistently revealed a reduction in extraneous associative cortical activity and an enhancement of task-relevant cortical processes. However, this has only been demonstrated under what are essentially practice-like conditions. Recently it has been shown that cerebral cortical activity becomes less efficient when performance occurs in a stressful, complex social environment. This dissertation examines the impact of motor skill training or practice on the EEG cortical dynamics that underlie performance in a stressful, complex social environment. Sixteen ROTC cadets participated in head-to-head pistol shooting competitions before and after completing nine sessions of skill training over three weeks. Spectral power increased in the theta frequency band and decreased in the low alpha frequency band after skill training. EEG Coherence increased in the left frontal region and decreased in the left temporal region after the practice intervention. These suggest a refinement of cerebral cortical dynamics with a reduction of task extraneous processing in the left frontal region and an enhancement of task related processing in the left temporal region consistent with the skill level reached by participants. Partitioning performance into ‘best’ and ‘worst’ based on shot score revealed that deliberate practice appears to optimize cerebral cortical activity of ‘best’ performances which are accompanied by a reduction in task-specific processes reflected by increased high-alpha power, while ‘worst’ performances are characterized by an inappropriate reduction in task-specific processing resulting in a loss of focus reflected by higher high-alpha power after training when compared to ‘best’ performances. Together, these studies demonstrate the power of experience afforded by practice, as a controllable factor, to promote resilience of cerebral cortical efficiency in complex environments.

Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting

Extreme learning machine (ELM) is originally proposed for single- hidden layer feed-forward neural networks (SLFN). From the functional equivalence of fuzzy logic systems and SLFN, the fuzzy logic systems can be interpreted as a special case of SLFN under some mild conditions. Hence the fuzzy logic systems can be trained using SLFN's learning algorithms. Considering the same equivalence, ELM is utilized here to train interval type-2 fuzzy logic systems (IT2FLSs). Based on the working principle of the ELM, the parameters of the antecedent of IT2FLSs are randomly generated while the consequent part of IT2FLSs is optimized using Moore-Penrose generalized inverse of ELM. Application of the developed model to electricity load forecasting is another novelty of the research work. Experimental results shows better forecasting performance of the proposed model over the two frequently used forecasting models.

A trial of e-simulation of sudden patient deterioration (FIRST2ACT WEB) on student learning

BACKGROUND: High-fidelity simulation pedagogy is of increasing importance in health professional education; however, face-to-face simulation programs are resource intensive and impractical to implement across large numbers of students. OBJECTIVES: To investigate undergraduate nursing students' theoretical and applied learning in response to the e-simulation program-FIRST2ACT WEBTM, and explore predictors of virtual clinical performance. DESIGN AND SETTING: Multi-center trial of FIRST2ACT WEBTM accessible to students in five Australian universities and colleges, across 8 campuses. PARTICIPANTS: A population of 489 final-year nursing students in programs of study leading to license to practice. METHODS: Participants proceeded through three phases: (i) pre-simulation-briefing and assessment of clinical knowledge and experience; (ii) e-simulation-three interactive e-simulation clinical scenarios which included video recordings of patients with deteriorating conditions, interactive clinical tasks, pop up responses to tasks, and timed performance; and (iii) post-simulation feedback and evaluation. Descriptive statistics were followed by bivariate analysis to detect any associations, which were further tested using standard regression analysis. RESULTS: Of 409 students who commenced the program (83% response rate), 367 undergraduate nursing students completed the web-based program in its entirety, yielding a completion rate of 89.7%; 38.1% of students achieved passing clinical performance across three scenarios, and the proportion achieving passing clinical knowledge increased from 78.15% pre-simulation to 91.6% post-simulation. Knowledge was the main independent predictor of clinical performance in responding to a virtual deteriorating patient R(2)=0.090, F(7, 352)=4.962, p<0.001. DISCUSSION: The use of web-based technology allows simulation activities to be accessible to a large number of participants and completion rates indicate that 'Net Generation' nursing students were highly engaged with this mode of learning. CONCLUSION: The web-based e-simulation program FIRST2ACTTM effectively enhanced knowledge, virtual clinical performance, and self-assessed knowledge, skills, confidence, and competence in final-year nursing students.

The effect of mobile technology as an active student response system on the acquisition of U.S. history content of secondary students with specific learning disabilities

Students with specific learning disabilities (SLD) typically learn less history content than their peers without disabilities and show fewer learning gains. Even when they are provided with the same instructional strategies, many students with SLD struggle to grasp complex historical concepts and content area vocabulary. Many strategies involving technology have been used in the past to enhance learning for students with SLD in history classrooms. However, very few studies have explored the effectiveness of emerging mobile technology in K-12 history classrooms. ^ This study investigated the effects of mobile devices (iPads) as an active student response (ASR) system on the acquisition of U.S. history content of middle school students with SLD. An alternating treatments single subject design was used to compare the effects of two interventions. There were two conditions and a series of pretest probesin this study. The conditions were: (a) direct instruction and studying from handwritten notes using the interactive notebook strategy and (b) direct instruction and studying using the Quizlet App on the iPad. There were three dependent variables in this study: (a) percent correct on tests, (b) rate of correct responses per minute, and (c) rate of errors per minute. ^ A comparative analysis suggested that both interventions (studying from interactive notes and studying using Quizlet on the iPad) had varying degrees of effectiveness in increasing the learning gains of students with SLD. In most cases, both interventions were equally effective. During both interventions, all of the participants increased their percentage correct and increased their rate of correct responses. Most of the participants decreased their rate of errors. ^ The results of this study suggest that teachers of students with SLD should consider a post lesson review in the form of mobile devices as an ASR system or studying from handwritten notes paired with existing evidence-based practices to facilitate students’ knowledge in U.S. history. Future research should focus on the use of other interactive applications on various mobile operating platforms, on other social studies subjects, and should explore various testing formats such as oral question-answer and multiple choice. ^

Scaling-up an efficacious school-based physical activity intervention: study protocol for the 'Internet-based professional learning to help teachers support activity in youth' (iPLAY) cluster randomized controlled trial and scale-up implementation evaluat

BACKGROUND: Despite the health benefits of regular physical activity, most children are insufficiently active. Schools are ideally placed to promote physical activity; however, many do not provide children with sufficient in-school activity or ensure they have the skills and motivation to be active beyond the school setting. The aim of this project is to modify, scale up and evaluate the effectiveness of an intervention previously shown to be efficacious in improving children's physical activity, fundamental movement skills and cardiorespiratory fitness. The 'Internet-based Professional Learning to help teachers support Activity in Youth' (iPLAY) study will focus largely on online delivery to enhance translational capacity.

METHODS/DESIGN: The intervention will be implemented at school and teacher levels, and will include six components: (i) quality physical education and school sport, (ii) classroom movement breaks, (iii) physically active homework, (iv) active playgrounds, (v) community physical activity links and (vi) parent/caregiver engagement. Experienced physical education teachers will deliver professional learning workshops and follow-up, individualized mentoring to primary teachers (i.e., Kindergarten - Year 6). These activities will be supported by online learning and resources. Teachers will then deliver the iPLAY intervention components in their schools. We will evaluate iPLAY in two complementary studies in primary schools across New South Wales (NSW), Australia. A cluster randomized controlled trial (RCT), involving a representative sample of 20 schools within NSW (1:1 allocation at the school level to intervention and attention control conditions), will assess effectiveness and cost-effectiveness at 12 and 24 months. Students' cardiorespiratory fitness will be the primary outcome in this trial. Key secondary outcomes will include students' moderate-to-vigorous physical activity (via accelerometers), fundamental movement skill proficiency, enjoyment of physical education and sport, cognitive control, performance on standardized tests of numeracy and literacy, and cost-effectiveness. A scale-up implementation study guided by the RE-AIM framework will evaluate the reach, effectiveness, adoption, implementation, and maintenance of the intervention when delivered in 160 primary schools in urban and regional areas of NSW.

DISCUSSION: This project will provide the evidence and a framework for government to guide physical activity promotion throughout NSW primary schools and a potential model for adoption in other states and countries.

Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for disease auto management but prominently by activating the less differentiated caregiver for any specific need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.