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PubMed Journals Articles About "Parallels Between Seminars Problem Based Learning" RSS

13:14 EDT 5th April 2020 | BioPortfolio

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Showing "Parallels between seminars problem based learning" PubMed Articles 1–25 of 38,000+

Parallels between seminars and problem-based learning.


Does problem-based learning in Nursing Education Empower Learning?

In this discussion paper, we explored our experiences with the integration of problem-based learning and use of evaluation tools in an undergraduate nursing research class. Six content areas in the course were adapted to problem-based learning. Understanding of concepts and being able to link concepts to the real world of practice can be achieved when nursing students actively engage to construct and reconstruct their knowledge. This journey has demonstrated to us the importance of reflecting on practice as...

Visual novelty, curiosity, and intrinsic reward in machine learning and the brain.

A strong preference for novelty emerges in infancy and is prevalent across the animal kingdom. When incorporated into reinforcement-based machine learning algorithms, visual novelty can act as an intrinsic reward signal that vastly increases the efficiency of exploration and expedites learning, particularly in situations where external rewards are difficult to obtain. Here we review parallels between recent developments in novelty-driven machine learning algorithms and our understanding of how visual novelt...


Using Statistical Measures and Machine Learning for Graph Reduction to Solve Maximum Weight Clique Problems.

In this paper, we investigate problem reduction techniques using stochastic sampling and machine learning to tackle large-scale optimization problems. These techniques heuristically remove decision variables from the problem instance, that are not expected to be part of an optimal solution. First we investigate the use of statistical measures computed from stochastic sampling of feasible solutions compared with features computed directly from the instance data. Two measures are particularly useful for this:...

Element interactivity as a factor influencing the effectiveness of worked example-problem solving and problem solving-worked example sequences.

The worked example effect in cognitive load theory suggests that providing worked examples first followed by solving similar problems would facilitate students' learning. Using problem solving-worked example sequence is another way of implementing example-based instruction. Although research has demonstrated the superiority of worked example-problem solving sequence on learning materials that presumably are high in element interactivity for novices, none of the previous studies have compared the two sequenc...

Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images.

Utilizing the idea of long-term cumulative return, reinforcement learning (RL) has shown remarkable performance in various fields. We follow the formulation of landmark localization in 3D medical images as an RL problem. Whereas value-based methods have been widely used to solve RL-based localization problems, we adopt an actor-critic based direct policy search method framed in a temporal difference learning approach. In RL problems with large state and/or action spaces, learning the optimal behavior is cha...

Using problem-based learning to improve patient safety in the emergency department.

Pressures from rising patient numbers and overcrowding in emergency departments (EDs) are putting patients' safety at risk. Beyond improved provision of resources, two elements are essential to patient safety in emergency care - work culture and staff training. In traditional training environments, the teacher dispenses knowledge to nursing students in a classroom setting. However, problem-based learning (PBL) and the related concept of team-based learning (TBL) aim to enhance learners' knowledge and skills...

Case-based blended eLearning scenarios-adequate for competence development or more?

Learning, competence development and scientific thinking in medicine need several strategies to facilitate new diagnostic and therapeutic ways. The optimal collaboration between creative thinking and biomedical informatics provides innovation for the individual patient and for a medical school or society. Utilizing the flexibilities of an e‑learning platform, a case based blended learning (CBBL) framework consisting of A) case based textbook material, B) online e‑CBL with question driven learning sc...

Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy.

In this article, we address the problem of the classification of the health state of the colon's wall of mice, possibly injured by cancer with machine learning approaches. This problem is essential for translational research on cancer and is a priori challenging since the amount of data is usually limited in all preclinical studies for practical and ethical reasons. Three states considered including cancer, health, and inflammatory on tissues. Fully automated machine learning-based methods are proposed, inc...

Research on OpenCL optimization for FPGA deep learning application.

In recent years, with the development of computer science, deep learning is held as competent enough to solve the problem of inference and learning in high dimensional space. Therefore, it has received unprecedented attention from both the academia and the business community. Compared with CPU/GPU, FPGA has attracted much attention for its high-energy efficiency, short development cycle and reconfigurability in the aspect of deep learning algorithm. However, because of the limited research on OpenCL optimiz...

Principles of problem-based learning for training and professional practice in ecotoxicology.

Problem-based learning (PBL) is a protagonist of constructivism widely used successfully in higher education. PBL is a learner-centered instructional and curricular approach that can use real problems for the development of the teaching and learning process. On the other hand, the complexity of knowledge of Ecotoxicology, as well as the importance of this field for Environmental Health and society demand reflections and proposals for the training of professionals who work in this field. Therefore, in accord...

Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training.

High-quality annotations for medical images are always costly and scarce. Many applications of deep learning in the field of medical image analysis face the problem of insufficient annotated data. In this paper, we present a semi-supervised learning method for chronic gastritis classification using gastric X-ray images. The proposed semi-supervised learning method based on tri-training can leverage unannotated data to boost the performance that is achieved with a small amount of annotated data. We utilize a...

Online sequential class-specific extreme learning machine for binary imbalanced learning.

Many real-world applications suffer from the class imbalance problem, in which some classes have significantly fewer examples compared to the other classes. In this paper, we focus on online sequential learning methods, which are considerably more preferable to tackle the large size imbalanced classification problems effectively. For example, weighted online sequential extreme learning machine (WOS-ELM), voting based weighted online sequential extreme learning machine (VWOS-ELM) and weighted online sequenti...

Policy Iteration Q-Learning for Data-Based Two-Player Zero-Sum Game of Linear Discrete-Time Systems.

In this article, the data-based two-player zero-sum game problem is considered for linear discrete-time systems. This problem theoretically depends on solving the discrete-time game algebraic Riccati equation (DTGARE), while it requires complete system dynamics. To avoid solving the DTGARE, the Q-function is introduced and a data-based policy iteration Q-learning (PIQL) algorithm is developed to learn the optimal Q-function by using data collected from the real system. Writing the Q-function in a quadratic ...

Online Learning Based on Online DCA and Application to Online Classification.

We investigate an approach based on DC (difference of convex functions) programming and DCA (dc algorithm) for online learning techniques. The prediction problem of an online learner can be formulated as a DC program for which online DCA is applied. We propose the two so-called complete/approximate versions of online DCA scheme and prove their logarithmic/sublinear regrets. Six online DCA-based algorithms are developed for online binary linear classification. Numerical experiments on a variety of benchmark ...

Dynamic time warping-based transfer learning for improving common spatial patterns in brain-computer interface.

Common spatial patterns (CSP) is a prominent feature extraction algorithm in motor imagery (MI)-based brain-computer interfaces (BCIs). However, CSP is computed using sample-based covariance-matrix estimation. Hence, its performance deteriorates if the number of training trials is small. To address this problem, this paper proposes a novel regularized covariance matrix estimation framework for CSP (i.e. DTW-RCSP) based on dynamic time warping (DTW) and transfer learning.

MBA: Mini-Batch AUC Optimization.

Area under the receiver operating characteristics curve (AUC) is an important metric for a wide range of machine-learning problems, and scalable methods for optimizing AUC have recently been proposed. However, handling very large data sets remains an open challenge for this problem. This article proposes a novel approach to AUC maximization based on sampling mini-batches of positive/negative instance pairs and computing U-statistics to approximate a global risk minimization problem. The resulting algorithm ...

Blended learning as an adjunct to tutor-led seminars in undergraduate orthodontics: a randomised controlled trial.

Aims To describe the use of blended learning as a method of undergraduate orthodontic teaching delivery and to assess its effectiveness in terms of knowledge gain.Design Randomised controlled trial.Setting Queen Mary University of London.Materials and methods Seventy dental undergraduate students in their fifth year were randomly allocated to receive orthodontic seminar-based teaching either using a blended approach based on an e-learning resource or with no prior teaching. All students were asked to comple...

The pharmacology course for preclinical students using team-based learning.

A pharmacology course in undergraduate medical education aims to enable students to cultivate the ability of applying drugs in the clinical context using basic scientific knowledge. Although team-based learning could be a useful approach, the literature on pharmacology education using team-based learning is limited. This study aims to evaluate the effectiveness of a pharmacology course using team-based learning.

Safe Triplet Screening for Distance Metric Learning.

Distance metric learning has been widely used to obtain the optimal distance function based on the given training data. We focus on a triplet-based loss function, which imposes a penalty such that a pair of instances in the same class is closer than a pair in different classes. However, the number of possible triplets can be quite large even for a small data set, and this considerably increases the computational cost for metric optimization. In this letter, we propose safe triplet screening that identifies ...

A cross-dataset deep learning-based classifier for people fall detection and identification.

Fall detection is an important problem for vulnerable sectors of the population such as elderly people, who frequently live alone. Note that a fall can be very dangerous for them if they cannot ask for help. Hence, in those situations, an automatic system that detected and informed to emergency services about the fall and subject identity could help to save lives. This way, they would know not only when but also who to help. Thus, our objective is to develop a new approach, based on deep learning, for fall ...

A survey on the effectiveness of WhatsApp for teaching doctors preparing for a licensing exam.

The use of WhatsApp for health professional education is not novel and is described increasingly in literature as an affordable, familiar, and convenient tool for collaboration. Social media technologies for health practitioner education allow the use of text and audio-visual aids, peer-to-peer based learning, and problem-based learning. This study presents a survey on the effectiveness of WhatsApp in doctors' preparation for a medical licensing exam.

Reprint of: Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts.

The fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support systems. It describes the analyzed phenomenon in the form of key concepts and the causal connections between them. The main aspects of the building of the FCM model are: concepts selection, determining the output concepts, criterion selection, and determining the relationships between concepts. It is usually based on expert knowledge. The main goal of the paper is to define the optimal in some sense FCM structure throug...

Introducing Hybrid Problem-Based Learning Modules in Ayurveda Education: Results of an Exploratory Study.

Problem-based learning (PBL) is a well-known student-centered instructional approach that is known to enhance problem-solving skills among the learners. Because teaching/learning methods in most of the Ayurveda colleges in India are still didactic and teacher centric, the effects of introducing PBL have not yet been evaluated. The primary objective of this study was to develop PBL modules for Kriya Sharira (Ayurveda Physiology) and their implementation in a hybrid format. In this method, PBL is used as an a...

Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights.

Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than traditional hardware with the Von-Neumann architecture. However, the memristive weight adjustment in the formal neuromorphic networks by the standard back-propagation techniques suffers from poor device-to-device reproducibility. One of the most promising approaches t...


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