Patience, Persistence and Pragmatism: Experiences and Lessons Learnt from the Implementation of Clinically Integrated Teaching and Learning of Evidence-Based Health Care - A Qualitative Study

Background

Clinically integrated teaching and learning are regarded as the best options for improving evidence-based healthcare (EBHC) knowledge, skills and attitudes. To inform implementation of such strategies, we assessed experiences and opinions on lessons learnt of those involved in such programmes.

Methods and Findings

We conducted semi-structured interviews with 24 EBHC programme coordinators from around the world, selected through purposive sampling. Following data transcription, a multidisciplinary group of investigators carried out analysis and data interpretation, using thematic content analysis. Successful implementation of clinically integrated teaching and learning of EBHC takes much time. Student learning needs to start in pre-clinical years with consolidation, application and assessment following in clinical years. Learning is supported through partnerships between various types of staff including the core EBHC team, clinical lecturers and clinicians working in the clinical setting. While full integration of EBHC learning into all clinical rotations is considered necessary, this was not always achieved. Critical success factors were pragmatism and readiness to use opportunities for engagement and including EBHC learning in the curriculum; patience; and a critical mass of the right teachers who have EBHC knowledge and skills and are confident in facilitating learning. Role modelling of EBHC within the clinical setting emerged as an important facilitator. The institutional context exerts an important influence; with faculty buy-in, endorsement by institutional leaders, and an EBHC-friendly culture, together with a supportive community of practice, all acting as key enablers. The most common challenges identified were lack of teaching time within the clinical curriculum, misconceptions about EBHC, resistance of staff, lack of confidence of tutors, lack of time, and negative role modelling.

Conclusions

Implementing clinically integrated EBHC curricula requires institutional support, a critical mass of the right teachers and role models in the clinical setting combined with patience, persistence and pragmatism on the part of teachers.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.

Fuzzy Ensembles for Embedding Adaptive Behaviours in Semi-Autonomous Avatars in 3D Virtual Worlds

Semi-autonomous avatars should be both realistic and believable. The goal is to learn from and reproduce the behaviours of the user-controlled input to enable semi-autonomous avatars to plausibly interact with their human-controlled counterparts. A powerful tool for embedding autonomous behaviour is learning by imitation. Hence, in this paper an ensemble of fuzzy inference systems cluster the user input data to identify natural groupings within the data to describe the users movement and actions in a more abstract way. Multiple clustering algorithms are investigated along with a neuro-fuzzy classifier; and an ensemble of fuzzy systems are evaluated.

Dinamização da hora do conto: recursos e estratégias

Relatório da Prática Profissional Supervisionada Mestrado em Educação Pré-Escolar

Práticas de ensino supervisionadas do 1º e 2º ciclo do ensino básico: a prática de resolução de problemas na aquisição do conceito de comprimento e de perímetro no 3.º ano de escolaridade

Relatório final apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1.º e 2.º Ciclos do Ensino Básico

A importância dos diferentes membros da equipa educativa na inclusão de uma criança com necessidades educativas especiais

Relatório da Prática Profissional Supervisionada Mestrado em Educação Pré-Escolar

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Perturbing the impulse activity of a single identified neuron augments the formation of long-term memory in a molluscan semi-intact preparation /

The aim of this study was to investigate the neural correlates of operant conditioning in a semi-intact preparation of the pond snail, Lymnaea stagnalis. Lymnaea learns, via operant conditioning, to reduce its aerial respiratory behaviour in response to an aversive tactile stimulus to its open pneumostome. This thesis demonstrates the successful conditioning of na'ive semiintact preparations to show learning in the dish. Furthermore, these conditioned preparations show long-term memory that persists for at least 18 hours. As the neurons that generate this behaviour have been previously identified I can, for the first time, monitor neural activity during both learning and long-term memory consolidation in the same preparation. In particular, I record from the respiratory neuron Right Pedal Dorsal 1 (RPeD 1) which is part of the respiratory central pattern generator. In this study, I demonstrate that preventing RPeDl impulse activity between training sessions reduces the number of sessions needed to produce long-term memory in the present semi-intact preparation.

Self-Regulated Learning and Children At-Risk for Learning Disabilities: Using Motivational Tactics to Support "Reading Rocks!"

This project explored self-regulation among children impacted by leaming disabilities. More specifically, this thesis examined whether a remedial literacy program called Reading Rocks! offered by the Leaming Disabilities Association of Niagara Region, provided participating children opportunities to set goals, develop strategies to meet these goals, and provide intemal and extemal feedback- all processes associated with a model of self-regulated leaming as pioneered by Butler and Winne (1995) and Winne and Hadwin (1999). In this thesis, I triangulate the data through the combination of three different methodologies. Firstly, I describe the various elements of the Reading Rocks! program. Secondly, I analyze the data gathered through three semi-structured interviews with three parents of children that participated in the Reading Rocks! program to demonstrate whether the program provides opportunities for children to self-regulate their learning. Thirdly, I also analyze photographic evidence of the motivational workstation boards created by the tutors and children to further illustrate how Reading Rocks! promotes self-regulatory processes among children.

Algorithmes d'apprentissage pour la recommandation

L'ère numérique dans laquelle nous sommes entrés apporte une quantité importante de nouveaux défis à relever dans une multitude de domaines. Le traitement automatique de l'abondante information à notre disposition est l'un de ces défis, et nous allons ici nous pencher sur des méthodes et techniques adaptées au filtrage et à la recommandation à l'utilisateur d'articles adaptés à ses goûts, dans le contexte particulier et sans précédent notable du jeu vidéo multi-joueurs en ligne. Notre objectif est de prédire l'appréciation des niveaux par les joueurs. Au moyen d'algorithmes d'apprentissage machine modernes tels que les réseaux de neurones profonds avec pré-entrainement non-supervisé, que nous décrivons après une introduction aux concepts nécessaires à leur bonne compréhension, nous proposons deux architectures aux caractéristiques différentes bien que basées sur ce même concept d'apprentissage profond. La première est un réseau de neurones multi-couches pour lequel nous tentons d'expliquer les performances variables que nous rapportons sur les expériences menées pour diverses variations de profondeur, d'heuristique d'entraînement, et des méthodes de pré-entraînement non-supervisé simple, débruitant et contractant. Pour la seconde architecture, nous nous inspirons des modèles à énergie et proposons de même une explication des résultats obtenus, variables eux aussi. Enfin, nous décrivons une première tentative fructueuse d'amélioration de cette seconde architecture au moyen d'un fine-tuning supervisé succédant le pré-entrainement, puis une seconde tentative où ce fine-tuning est fait au moyen d'un critère d'entraînement semi-supervisé multi-tâches. Nos expériences montrent des performances prometteuses, notament avec l'architecture inspirée des modèles à énergie, justifiant du moins l'utilisation d'algorithmes d'apprentissage profonds pour résoudre le problème de la recommandation.

Optimal Unsupervised Learning in Feedforward Neural Networks

We investigate the properties of feedforward neural networks trained with Hebbian learning algorithms. A new unsupervised algorithm is proposed which produces statistically uncorrelated outputs. The algorithm causes the weights of the network to converge to the eigenvectors of the input correlation with largest eigenvalues. The algorithm is closely related to the technique of Self-supervised Backpropagation, as well as other algorithms for unsupervised learning. Applications of the algorithm to texture processing, image coding, and stereo depth edge detection are given. We show that the algorithm can lead to the development of filters qualitatively similar to those found in primate visual cortex.

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Evaluación de la competencia comunicativa académica en semi-presencialidad

En la E.U. de Magisterio de Donostia de la Universidad del País Vasco (UPV/EHU), este curso 2010/2011 ha comenzado la oferta semi-presencial para aquellos estudiantes que no pueden matricularse a todas las asignaturas de primer curso. Dentro de esta experiencia piloto se ha impartido la asignatura "Desarrollo de la competencia comunicativa I" en el Grado de Educación Primaria, centrada en la competencia comunicativa académica. El diseño de esta asignatura se ha apoyado en investigaciones relacionadas con el desarrollo de esta competencia en entornos virtuales y ha contado con actividades diversas que han permitido a los estudiantes su autoevaluación y también la coevaluación

Innovative use of vodcast (Video-Podcast) to enrich learning experience in structures laboratory

Students may have difficulty in understanding some of the complex concepts which they have been taught in the general areas of science and engineering. Whilst practical work such as a laboratory based examination of the performance of structures has an important role in knowledge construction this does have some limitations. Blended learning supports different learning styles, hence further benefits knowledge building. This research involves an empirical study of how vodcasts (video-podcasts) can be used to enrich learning experience in the structural properties of materials laboratory of an undergraduate course. Students were given the opportunity of downloading and viewing the vodcasts on the theory before and after the experimental work. It is the choice of the students when (before or after, before and after) and how many times they would like to view the vodcasts. In blended learning, the combination of face-to-face teaching, vodcasts, printed materials, practical experiments, writing reports and instructors’ feedbacks benefits different learning styles of the learners. For the preparation of the practical, the students were informed about the availability of the vodcasts prior to the practical session. After the practical work, students submitted an individual laboratory report for the assessment of the structures laboratory. The data collection consisted of a questionnaire completed by the students, follow-up semi-structured interviews and the practical reports submitted by them for assessment. The results from the questionnaire were analysed quantitatively, whilst the data from the assessment reports were analysed qualitatively. The analysis shows that most of the students who have not fully grasped the theory after the practical, managed to gain the required knowledge by viewing the vodcasts. According to their feedbacks, the students felt that they have control over how to use the material and to view it as many times as they wish. Some students who have understood the theory may choose to view it once or not at all. Their understanding was demonstrated by their explanations in their reports, and was illustrated by the approach they took to explicate the results of their experimental work. The research findings are valuable to instructors who design, develop and deliver different types of blended learning, and are beneficial to learners who try different blended approaches. Recommendations were made on the role of the innovative application of vodcasts in the knowledge construction for structures laboratory and to guide future work in this area of research.

Learning across business sectors: facets of innovation in aerospace and construction

Innovation is notoriously difficult to define and is invariably intertwined with issues of knowledge creation, continuous improvement and organisational change. An extensive literature classifies numerous types of innovation and militates against any simplistic attempt at definition. It is widely accepted that innovation is at least partly dependent upon the surrounding environment. Industry recipes and institutionally embedded practices shape the environment within which innovation occurs. Recent research directions have addressed the diffusion of innovation and its dependence upon social and institutional structures. In this respect, it is highly pertinent to compare the way that innovation is interpreted and enacted in different industrial sectors. The comparison between UK aerospace and construction is especially revealing because the two sectors are so different and therefore constitute radically different climates for innovation. Empirical research is reported based on semi-structured interviews with practitioners from both sectors. Interpretations of innovation are found to differ dramatically between aerospace and construction. Within the context of an ongoing struggle to define innovation, both industries are striving to become more innovative. The aerospace sector is found to emphasise technical innovation whereas the construction sector emphasises process innovation. An overriding cultural bias in Western economies towards technological innovation results in the common perception that aerospace is much more innovative than construction. The experienced realities of practitioners in the two sectors are much more complex.