Search results for "LEARNING"
showing 10 items of 6669 documents
How do we assess in Clinical Legal Education? A 'reflection' about reflective learning
2016
I suggest this hypothesis and these premises from the perspective of my experience in Clinical Legal Education and the use of experiential learning methods in other 'traditional' courses. Firstly, institutional assessment must be distinguished from the assessment of learning. Traditionally, assessment is reduced to institutional assessment: that is, to give a mark depending on the achievement of knowledge instead of focusing in the student's learning. However, I propose (to remember) that: 1) (Formative) assessment is part of learning; 2) Reflective learning (and reflective skills) is/are a part of assessment. This implies a process of continuous evaluation instead of summative evaluation, …
Teaching clinical reasoning and decision-making skills to nursing students: Design, development, and usability evaluation of a serious game
2016
Background\ud \ud Serious games (SGs) are a type of simulation technology that may provide nursing students with the opportunity to practice their clinical reasoning and decision-making skills in a safe and authentic environment. Despite the growing number of SGs developed for healthcare professionals, few SGs are video based or address the domain of home health care.\ud \ud Aims\ud \ud This paper aims to describe the design, development, and usability evaluation of a video based SG for teaching clinical reasoning and decision-making skills to nursing students who care for patients with chronic obstructive pulmonary disease (COPD) in home healthcare settings.\ud \ud Methods\ud \ud A prototy…
Developing a Serious Game for Nurse Education.
2018
Future nursing education is challenged to develop innovative and effective programs that align with current changes in health care and to educate nurses with a high level of clinical reasoning skills, evidence-based knowledge, and professional autonomy. Serious games (SGs) are computer-based simulations that combine knowledge and skills development with video game–playing aspects to enable active, experiential, situated, and problem-based learning. In a PhD project, a video-based SG was developed to teach nursing students nursing care for patients with chronic obstructive pulmonary disease in home health care and hospital settings. The current article summarizes the process of the SG devel…
Hybrid Deep Shallow Network for Assessment of Depression Using Electroencephalogram Signals
2020
Depression is a mental health disorder characterised by persistently depressed mood or loss of interest in activities resulting impairment in daily life significantly. Electroencephalography (EEG) can assist with the accurate diagnosis of depression. In this paper, we present two different hybrid deep learning models for classification and assessment of patient suffering with depression. We have combined convolutional neural network with Gated recurrent units (RGUs), thus the proposed network is shallow and much smaller in size in comparison to its counter LSTM network. In addition to this, proposed approach is less sensitive to parameter settings. Extensive experiments on EEG dataset shows…
Training Secondary Education Teachers through the Prism of Sustainability: The Case of the Universitat de València
2018
Designing the training of future teachers through holistic and interdisciplinary visions is vital to developing coherent contents, epistemologies, and methodologies that put Education for Sustainability into action. The research presented here analyzes the teaching guides from the curriculum for the Master&rsquo
Towards Intelligent IoT Networks: Reinforcement Learning for Reliable Backscatter Communications
2019
Backscatter communication is becoming the focal point of research for low-powered Internet of things (IoT). However, the intelligence aspect of the backscattering devices is not well-defined. Since future IoT networks are going to be a formidable platform of intelligent sensing devices operating in a self-organizing manner, it is necessary to incorporate learning capabilities in backscatter devices. Motivated by this objective, this paper aims to employ reinforcement learning for improving the performance of backscatter networks. In particular, a multicluster backscatter communication model is developed for shortrange information sharing. This is followed by a power allocation algorithm usi…
Feasibility Analysis For Constrained Model Predictive Control Based Motion Cueing Algorithm
2019
International audience; This paper deals with motion control for an 8-degree-of-freedom (DOF) high performance driving simulator. We formulate a constrained optimal control that defines the dynamical behavior of the system. Furthermore, the paper brings together various methodologies for addressing feasibility issues arising in implicit model predictive control-based motion cueing algorithms.The implementation of different techniques is described and discussed subsequently. Several simulations are carried out in the simulator platform. It is observed that the only technique that can provide ensured closed-loop stability by assuring feasibility over all prediction horizons is a braking law t…
Improving the Training Methods for Designers of Flexible Production Cells in Factories of the Future
2020
This work proposes a design method for flexible manufacturing systems (FMS). The method reduces the learning curve by helping employees to solve problems related to the design and optimization of the layout, operation and control of FMS, avoiding the drawbacks of current tools. The approach uses Domain Specific Modeling Languages (DSML) for specification of FMS. The paper presents the definition of the DSML and the implementation of the graphical modeling and simulation tool bringing important contributions to development of the domain through the use of constructions from categories theory for DSML specifications. This mathematical basis allows the definition of constraints to avoid supple…
Ant Colony Optimisation-Based Classification Using Two-Dimensional Polygons
2016
The application of Ant Colony Optimization to the field of classification has mostly been limited to hybrid approaches which attempt at boosting the performance of existing classifiers (such as Decision Trees and Support Vector Machines (SVM)) — often through guided feature reductions or parameter optimizations.
A class of third order iterative Kurchatov–Steffensen (derivative free) methods for solving nonlinear equations
2019
Abstract In this paper we show a strategy to devise third order iterative methods based on classic second order ones such as Steffensen’s and Kurchatov’s. These methods do not require the evaluation of derivatives, as opposed to Newton or other well known third order methods such as Halley or Chebyshev. Some theoretical results on convergence will be stated, and illustrated through examples. These methods are useful when the functions are not regular or the evaluation of their derivatives is costly. Furthermore, special features as stability, laterality (asymmetry) and other properties can be addressed by choosing adequate nodes in the design of the methods.