Inquiry learning: A pedagogical and curriculum framework for information literacy

This chapter presents an inquiry learning framework that can be used as a pathway for the development of information literacy in both K-12 and higher education. Inquiry learning is advocated as an authentic and active approach that draws upon students’ natural curiosity. The pedagogical and curriculum framework incorporates three major elements: questioning frameworks, information literacy and an iterative research cycle. Models and strategies for the elements of the framework are presented and discussed. The chapter ends with an acknowledgement of the challenges associated with implementing inquiry learning.

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

A framework for e-learning: a blended solution?: current developments in technology-assisted education

This paper describes a Framework for e-Learning and presents the findings of a study investigating whether the use of Blended Learning can fulfill or at least accommodate some of the human requirements presently neglected by current e-Learning systems. This study evaluates the in-house system: Teachmat, and discusses how the use of Blended Learning has become increasingly prevalent as a result of its enhancement and expansion, its relationship to the human and pedagogical issues, and both the positive and negative implications of this reality. [From the Authors]

Developing a framework for e-learning revisited

In this paper we revisit a study on e-Learning and suggestions for developing a framework for e-Learning. The original study in 2005 looked at e-Learning, specifically e-Tutoring and the issues that surround it. However, re-examining these findings led to the realization that whilst most courses were not fully "e" many were in essence using Blended Learning to varying degrees. It is concluded that the encroachment of a Blended Learning approach has been an indirect consequence of the extension and enhancement of in-house course management technologies now employed. The pros and cons of the situation are identified and discussed. In addition, we summarize the positions of participants of the workshop on Developing a Framework for e-Learning.

Learning curves, taking instructions, and patient safety:using a theoretical domains framework in an interview study to investigate prescribing errors among trainee doctors

ABSTRACT:

A leadership framework to support the use of e-learning resources

Aim
This inquiry aims to apply the NHS leadership framework to nurse education for the implementation of e-learning.
Background
Recognition needs to be given to the emerging postgraduate nursing students new status of consumer and the challenge now for nurse education is how to remain relevant and competitive in this consumer led market. The move towards an e-learning paradigm has been suggested as a competitive and contemporary way forward for the student consumer. The successful introduction of e-learning in nurse education will require leadership and a strong organisational management system.
Discussion
Each element of the NHS leadership framework is described and interpreted for application in a higher education setting for the implementation of e-learning.
Conclusions
Change in the delivery of post graduate nurse education is necessary to ensure it remains current and reflective of consumer need in a competitive marketplace. By applying a leadership framework that acknowledges the skills and abilities of staff and encourages the formation of collaborative partnerships from within the wider university community, educators can begin to develop skills and confidence in teaching using e-learning resources.

Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations

This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness.

Building and Sustaining a Learning Environment for Inclusive Design: A Framework for teaching inclusive design within built environment courses in the UK

Final report of the Special Interest Group in Inclusive Design for Centre for Education in the Built Environment

The use of YouTube Analytics of Video Lecture Capture as a Teaching and Learning Performance Enhancer

Do patterns in the YouTube viewing analytics of Lecture Capture videos point to areas of potential teaching and learning performance enhancement? The goal of this action based research project was to capture and quantitatively analyse the viewing behaviours and patterns of a series of video lecture captures across several computing modules in Queen’s University, Belfast, Northern Ireland. The research sought to establish if a quantitative analysis of viewing behaviours coupled with a qualitative evaluation of the material provided from the students could be correlated to provide generalised patterns that could then be used to understand the learning experience of students during face to face lectures and, thereby, present opportunities to reflectively enhance lecturer performance and the students’ overall learning experience and, ultimately, their level of academic attainment.

Community response in disasters: an ecological learning framework

Natural disasters are frequently exacerbated by anthropogenic mechanisms and have social and political consequences for communities. The role of community learning in disasters is seen to be increasingly important. However, the ways in which such learning unfolds in a disaster can differ substantially from case to case. This article uses a comparative case study methodology to examine catastrophes and major disasters from five countries (Japan, New Zealand, UK, US and Germany) to consider how community learning and adaptation occurs. An ecological model of learning is considered, where community learning is of small loop (adaptive, incremental, experimental) type or large loop (paradigm changing) type. Using this model we consider that there are three types of community learning that occur in disasters (navigation, organisation, reframing). The type of community learning that actually develops in a disaster depends upon a range of social factors such as stress and trauma, civic innovation and coercion.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Lean learning academy: an innovative framework for lean manufacturing training

Worldwide competitiveness poses enormous challenges on managers, demanding a continuous quest to increase rationality in the use of resources. As a management philosophy, Lean Manufacturing focuses on the elimination of activities that do not create any type of value and therefore are considered waste. For companies to successfully implement the Lean Manufacturing philosophy it is crucial that the human resources of the organization have the necessary training, for which proper tools are required. At the same time, higher education institutions need innovative tools to increase the attractiveness of engineering curricula and develop a higher level of knowledge among students, improving their employability. This paper describes how Lean Learning Academy, an international collaboration project between five EU universities and five companies, from SME to Multinational/Global companies, developed and applied an innovative training programme for Engineers on Lean Manufacturing, a successful alternative to the traditional teaching methods in engineering courses.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.