Nurses as educators: creating teachable moments in practice

Effective workplace teaching is increasingly important in healthcare, with all staff being potential educators. The introduction of new roles and the need to create capacity for increased numbers of students can make it difficult to create a good learning experience. Despite the richness of clinical practice as a learning environment, creating capacity for teaching can be challenging. This article explores the possibilities for identifying and creating teachable moments in busy clinical environments and suggests a developmental model for incorporating these learning opportunities. Teachable moments linked directly to optimal patient care can potentially influence and shape a positive learning culture in clinical environments

Learning and growing: Lessons learned in financial education

Financial literacy

Transition Planning -- Responsibilities and Strategies

This meta-synthesis of the literature, on transition planning for youth with disabilities, examines several important facets that impact the post school outcomes for students with disabilities. Eight specific areas have been highlighted that point out the common theme areas of this metasynthesis. Research recognizes the responsibilities of the regular and special education teachers to the secondary transition process and the roles of the student and parent are not minimized at all. Professional development and continuous training are needed and highlighted for teachers, counselors, administrators, parents and students. There are specific successful strategies and methods to apply to the transition planning process. Raising expectations will likely result in positive post school outcomes as well. However, it is only too often that teachers, counselors, parents, and students are ill prepared for secondary transitions from high school to employment or further training. Expectations are too low and students are not prepared to make decisions about their employment or training in spite of the fact that self determination and self advocacy are strong tools that can and will promote positive outcomes for students. Indeed, individualized transition planning and person centered planning are valuable tools

Shear resistance improvement of oil-contaminated ballast layer with rubber shred inclusions

Railway ballast, which form an integral part of rail tracks, is highly susceptible to subsistence due to both vibration transmitted by the passing trains, as well as the breakage of ballasts with repeated impact. The resulting subsistence necessitates regular monitoring and maintenance, involving cost- and time- consuming remedial actions, such as stone-blowing and ballast renewal. Measures to minimize the wear and tear effect are therefore desirable to prolong the lifespan of the ballast layer. It is even more critical when the ballast is contaminated with oil and grease from braking wheels and leakages. This paper describes the inclusion of rubber shreds (≤10 mm in length, 1.5 mm thick) derived from the inner tubes of motorcycle tyres in oil-contaminated ballast layer for shear resistance improvement. The tests are mainly carried out in a standard direct shear test setup, i.e. shear box measuring 60 mm x 60 mm. Granitic stones of suitable sizes were sieved and used as representative samples of typical ballast. The samples were soaked in lubricant oil for 14 days to simulate the contamination. The direct shear test results indicated rubber shreds inclusion could effectively improve the shear resistance of ballast and expedient in deformation control with increased ductility of the composites. This could potentially improve absorption of impact, hence reduction of breakages of the ballasts. Clearly both mechanisms contribute to the overall reduced subsistence, accompanied by an increase in the shear resistance. However, further investigations in a dynamic test setup are necessary for verifications prior to field implementation

Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings

We present an optimised multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human tutor, trained on real human-human tutoring data. Within a life-long interactive learning period, the agent, trained using Reinforcement Learning (RL), must be able to handle natural conversations with human users and achieve good learning performance (accuracy) while minimising human effort in the learning process. We train and evaluate this system in interaction with a simulated human tutor, which is built on the BURCHAK corpus -- a Human-Human Dialogue dataset for the visual learning task. The results show that: 1) The learned policy can coherently interact with the simulated user to achieve the goal of the task (i.e. learning visual attributes of objects, e.g. colour and shape); and 2) it finds a better trade-off between classifier accuracy and tutoring costs than hand-crafted rule-based policies, including ones with dynamic policies.Comment: 10 pages, RoboNLP Workshop from ACL Conferenc

Applying Theories in Language Programs

Selected Topics in Applied Linguistics: How to Choose a Theory. I offer a critical exploration of some of the conditions involved in Instructed Second Language Acquisition (ISLA), as well as of the paradoxical approaches in the theoretical questions, methods, categories, and perspectives of ISLA. The discussion proceeds with a very short overview of prevalent theories of ISLA generally. Then I add a contrastive look in more depth at only two “theories” and their possible applications in language programs. I emphasize some of the discussions in our profession concerning processing instruction, e.g. (VanPatten "Processing Instruction") or VanPatten ("Why Explicit Knowledge Cannot Become Implicit Knowledge" ), and the multiliteracies framework, e.g. (Paesani, Allen and Dupuy). I conclude with an invitation to a set of questions we might pose to any theory, framework, or approach as we consider its efficacy and applications for our own specific contexts

Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb

In this work we explore the use of reinforcement learning (RL) to help with human decision making, combining state-of-the-art RL algorithms with an application to prosthetics. Managing human-machine interaction is a problem of considerable scope, and the simplification of human-robot interfaces is especially important in the domains of biomedical technology and rehabilitation medicine. For example, amputees who control artificial limbs are often required to quickly switch between a number of control actions or modes of operation in order to operate their devices. We suggest that by learning to anticipate (predict) a user's behaviour, artificial limbs could take on an active role in a human's control decisions so as to reduce the burden on their users. Recently, we showed that RL in the form of general value functions (GVFs) could be used to accurately detect a user's control intent prior to their explicit control choices. In the present work, we explore the use of temporal-difference learning and GVFs to predict when users will switch their control influence between the different motor functions of a robot arm. Experiments were performed using a multi-function robot arm that was controlled by muscle signals from a user's body (similar to conventional artificial limb control). Our approach was able to acquire and maintain forecasts about a user's switching decisions in real time. It also provides an intuitive and reward-free way for users to correct or reinforce the decisions made by the machine learning system. We expect that when a system is certain enough about its predictions, it can begin to take over switching decisions from the user to streamline control and potentially decrease the time and effort needed to complete tasks. This preliminary study therefore suggests a way to naturally integrate human- and machine-based decision making systems.Comment: 5 pages, 4 figures, This version to appear at The 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making, Princeton, NJ, USA, Oct. 25-27, 201

Empirical Results on Interactive E-learning Using Knowledge Acquisition Based Learning

This paper presents empirical results on the efficiency of e-Learning systems which deploy and use knowledge acquisition based method (KA-LMS) for enhancing the learning capabilities of students. A new e-Learning method, which was developed by the author, is used to measure the impact of the new method on the learning achievements of the students. The method utilizes learning management systems, which restricts the ability of a learning student to advance from one topic to the next one unless he/she has acquired a minimum set of learning outcomes and knowledge. The data is collected from relatively large class rooms, where students attend online classes using the knowledge acquisition based method, and then the same set of students go through physical face to face exams. The results show that on the average students were able to score in the physical exam similar or higher grades compared to the results obtained automatically using the e-Learning KA-LMS. The effectiveness of KA-LMS was shown to be effective during the Covid-19 lockdown