Perceptual user interface framework for immersive information retrieval environments (An experimental framework for testing and rapid iteration)

The use of perceptual inputs is an emerging area within HCI that suggests a developing Perceptual User Interface (PUI) that may prove advantageous for those involved in mobile serious games and immersive social network environments. Since there are a large variety of input devices, software platforms, possible interactions, and myriad ways to combine all of the above elements in pursuit of a PUI, we propose in this paper a basic experimental framework that will be able to standardize study of the wide range of interactive applications for testing efficacy in learning or information retrieval and also suggest improvements to emerging PUIs by enabling quick iteration. This rapid iteration will start to define a targeted range of interactions that will be intuitive and comfortable as perceptual inputs, and enhance learning and information retention in comparison to traditional GUI systems. The work focuses on the planning of the technical development of two scenarios, and the first steps in developing a framework to evaluate these and other PUIs for efficacy and pedagogy.

Intelligent decision making in electricity markets: simulated annealing Q-Learning

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 is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

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 is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

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.

Modelling an e-learning environment for learning programming languages

It is widely accepted that solving programming exercises is fundamental to learn how to program. Nevertheless, solving exercises is only effective if students receive an assessment on their work. An exercise solved wrong will consolidate a false belief, and without feedback many students will not be able to overcome their difficulties. However, creating, managing and accessing a large number of exercises, covering all the points in the curricula of a programming course, in classes with large number of students, can be a daunting task without the appropriated tools working in unison. This involves a diversity of tools, from the environments where programs are coded, to automatic program evaluators providing feedback on the attempts of students, passing through the authoring, management and sequencing of programming exercises as learning objects. We believe that the integration of these tools will have a great impact in acquiring programming skills. Our research objective is to manage and coordinate a network of eLearning systems where students can solve computer programming exercises. Networks of this kind include systems such as learning management systems (LMS), evaluation engines (EE), learning objects repositories (LOR) and exercise resolution environments (ERE). Our strategy to achieve the interoperability among these tools is based on a shared definition of programming exercise as a Learning Object (LO).

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.

Integrating rich learning applications in LMS

Currently, a learning management system (LMS) plays a central role in any e-learning environment. These environments include systems to handle the pedagogic aspects of the teaching–learning process (e.g. specialized tutors, simulation games) and the academic aspects (e.g. academic management systems). Thus, the potential for interoperability is an important, although over looked, aspect of an LMS. In this paper, we make a comparative study of the interoperability level of the most relevant LMS. We start by defining an application and a specification model. For the application model, we create a basic application that acts as a tool provider for LMS integration. The specification model acts as the API that the LMS should implement to communicate with the tool provider. Based on researches, we select the Learning Tools Interoperability (LTI) from IMS. Finally, we compare the LMS interoperability level defined as the effort made to integrate the application on the study LMS.

International Partnering strategy for e-learning provider in emerging economies: Evidence from East Africa learning institutions

International partnership has received growing interest in the literature during the past decades due to globalization, increased technological approaches and rapid changes in competitive environments. The study specifically determines the support provided by international partners on promotion of e-learning in East Africa, assess the motives of partner selection criteria, the determinants of selecting partners, partner models and partner competence of e-learning provider. The study also evaluates obstacles of e-learning partnering strategy in East Africa learning institutions. The research adopts a descriptive survey design. Target population involved East Africa learning institutions with a list of potential institutions generated from the Ministry of Higher Education database. Through a targeted reduction of the initial database, consisting of all learning institutions, both public and private, the study created a target sample base of 200 learning institutions. Structured questionnaires scheduled were used to collect primary data. Study findings showed the approach way East African communities in selecting their e-learning partners depend on international reputation of partners, partner with ability to negotiate with foreign governments, partner with international and local experiences, nationality of foreign partner and partners with local market knowledge.

An examination of learning resulting from a transition from a directed to a self-directed work environment

~ This study focuses on the process of self-directed learning that individuals go through as they adapt to new work situations. This is a study of how one critical incident, specifically the transition from a traditional office structure to a home office structure, affected employees and what their learning process was as they adapted to the new environment. This study has 3 educational foundations: adult learning, self-directed learning, and the social context from which the learning will occur. Six women and 2 men were interviewed approximately 1 year following the transition. Analysis of the data revealed 5 themes of: impacts of the self-directed environment on participants' personal lives, their roles, skill set, productivity, and the physical environment; support offered by the organization, family, and office administration; personal development, specific learning needs, and personal skills; boundaries as they relate to family and work; and skill set and orientation requirements of new home office employees. The findings revealed the learning processes of the 8 participants. The learning processes of these participants were discussed within a theoretical framework of the learners, their immediate surroundings, and the larger social environment. The results indicated that the transition from a directed work environment to a self directed work environment is a complex, interrelated process. An element found throughout the theoretical framework is that of control. A second critical element is the need for participants to have a clearly defined work role and an opportunity to engage in discussion with peers and the community. Further findings reinforced the importance of climate and found that the physical environment is a key factor in a successful selfdirected work environment. The findings of this study revealed that no one factor makes an individual function successfully in a self-directed work environment, but that it is a complex interplay among the leamer, their immediate surroundings, and the social environment that will have the greatest impact on success. Recommendations are made which can be used to guide organizational leaders in facilitating employees' transition from a directed to a self-directed work environment. Additionally, recommendations are made for further research in the area of self-directed work environments.

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Occupational therapy and its relevance in social projects. relationships, learning and opportunities: collection of experiences of University of Alberta Mscot students in Bogotá, Colombia

Occupational therapists are equipped to promote wellbeing through occupation and to enable participation and meaningful engagement of people in their social and physical environments (WFOT, 2012). As such, the role of the occupational therapists is profoundly linked to the social, cultural and environmental characteristics of the contexts in which occupations take place. The central role that context plays in occupational performance creates an interesting dichotomy for the occupational therapist: on one hand, a profound understanding of cultural and social factors is required from the Occupational Therapy (OT) in order to develop a meaningful and successful collaboration with the person; on the other hand, the ability of the occupational therapists to recognize and explore the contextual factor of an occupation-person dyad transcends cultural and spatial barriers. As a result, occupational therapists are equipped to engage in international collaboration and practice, and as such face unique and enriching challenges. International fieldwork experiences have become a tool through which occupational therapists in training can develop the critical skills for understanding the impact of cultural and social factors on occupation. An OT student in an international fieldwork experience faces numerous challenges in leading a process that is both relevant and respectful to the characteristics of the local context: language, cultural perceptions of occupation and personhood, religious backgrounds, health care access, etc. These challenges stand out as ethical considerations that must be considered when navigating an international fieldwork experience (AOTA, 2009). For more than five years now, the Faculty of Rehabilitation Medicine (FRM) of the University of Alberta (UoFA) and the School of Medicine and Health Sciences at the Universidad del Rosario (UR), Bogota, Colombia, have sustained a productive and meaningful international collaboration. This collaboration includes a visit by Dr. Albert Cook, professor of the FRM and former dean, to the UR as the main guest speaker in the International Congress of Technologies for Disability Support (IBERDISCAP) in 2008. Furthermore, Dr. Cook was a speaker in the research seminar of the Assistive Technology Research Group of the Universidad del Rosario. Following Dr. Cook’s visit, Professors Liliana Álvarez and Adriana Ríos travelled to Edmonton and initiated collaboration with the FRM, resulting in the signing of an agreement between the FRM and the UR in 2009, agreement that has been maintained to this day. The main goal of this agreement is to increase academic and cultural cooperation between the UR and the UofA. Other activities have included the cooperation between Dr. Kim Adams (who has largely maintained interest and effort in supporting the capacity building of the UR rehabilitation programs in coordinating the provision of research placement opportunities for UR students at the UofA), an Assistive Technology course for clinicians and students led by Dr. Adams, and a research project that researched the use of basic cell phones to provide social interaction and health information access for people with disabilities in a low-income community in Colombia (led by Tim Barlott, OT, MSc, under the supervision of Dr. Adams). Since the beginning, the occupational therapy programs of the Universidad del Rosario and the University of Alberta have promoted this collaboration and have strived to engage in interactions that provide further development opportunities for students and staff. As part of this process, the international placement experience of UofA OT students was born under the leadership of: Claudia Rozo, OT program director at UR, placement and academic leadership of Elvis Castro and Angélica Monsalve, professors of the occupational therapy program at UR; and Dr. Lili Liu, OT department director at UofA, Cori Schmitz, Academic coordinator of clinical education at the UofA; and Tim Barlott and Liliana Álvarez leading the international and cross-cultural aspect of this collaboration.This publication summarizes and illustrates the process of international placement in community settings in Colombia, undertaken by occupational therapy students of the University of Alberta. It is our hope that this document can provide and document the ethical considerations of international fieldwork experience, the special characteristics of communities and the ways in which cultural and social competences are developed and help international students navigate the international setting. We also hope that this document will stimulate discussion among professional and academic communities about the importance and richness of international placement experiences and encourage staff and students to articulate their daily efforts with the global occupational therapy agenda.

Leadership and performance in multicultural business environments a qualitative study in German-Colombian teams

Multicultural leadership is a topic a great interest in nowadays globalized work environment. Colombia emerges as an attractive marketplace with appealing business opportunities, especially for German enterprises. After presenting Colombia’s current political, social and economic situation, the thesis elaborates the complex subject of cultural differences while focusing on the peculiarities of German and Colombian national cultures. The resulting implications for a team’s collaboration and leader effectiveness are theoretically supported with reference to the landmark studies of Hofstede and GLOBE. By utilizing semi-structured interview techniques, a qualitative research enriches the previous findings and gives an all-encompassing insight in German-Colombian teamwork. The investigation identifies distinctive behavioral patterns and relations, which imply challenges and factors of success for multicultural team leaders. Finally, a categorical analysis examines the influence of cultural traits on team performance and evaluates the effectiveness of the applied leadership style.

Vision 2020 - A whole life-cycle information management system for built environments

A whole life-cycle information management vision is proposed, the organizational requirements for the realization of the scenario is investigated. Preliminary interviews with construction professionals are reported. Discontinuities at information transfer throughout life-cycle of built environments are resulting from lack of coordination and multiple data collection/storage practices. A more coherent history of these activities can improve the work practices of various teams by augmenting decision making processes and creating organizational learning opportunities. Therefore, there is a need for unifying these fragmented bits of data to create a meaningful, semantically rich and standardized information repository for built environment. The proposed vision utilizes embedded technologies and distributed building information models. Two diverse construction project types (large one-off design, small repetitive design) are investigated for the applicability of the vision. A functional prototype software/hardware system for demonstrating the practical use of this vision is developed and discussed. Plans for case-studies for validating the proposed model at a large PFI hospital and housing association projects are discussed.

Estimation of distances in virtual environments using size constancy

It is reported in the literature that distances from the observer are underestimated more in virtual environments (VEs) than in physical world conditions. On the other hand estimation of size in VEs is quite accurate and follows a size-constancy law when rich cues are present. This study investigates how estimation of distance in a CAVETM environment is affected by poor and rich cue conditions, subject experience, and environmental learning when the position of the objects is estimated using an experimental paradigm that exploits size constancy. A group of 18 healthy participants was asked to move a virtual sphere controlled using the wand joystick to the position where they thought a previously-displayed virtual cube (stimulus) had appeared. Real-size physical models of the virtual objects were also presented to the participants as a reference of real physical distance during the trials. An accurate estimation of distance implied that the participants assessed the relative size of sphere and cube correctly. The cube appeared at depths between 0.6 m and 3 m, measured along the depth direction of the CAVE. The task was carried out in two environments: a poor cue one with limited background cues, and a rich cue one with textured background surfaces. It was found that distances were underestimated in both poor and rich cue conditions, with greater underestimation in the poor cue environment. The analysis also indicated that factors such as subject experience and environmental learning were not influential. However, least square fitting of Stevens’ power law indicated a high degree of accuracy during the estimation of object locations. This accuracy was higher than in other studies which were not based on a size-estimation paradigm. Thus as indirect result, this study appears to show that accuracy when estimating egocentric distances may be increased using an experimental method that provides information on the relative size of the objects used.

Cognitive and environmental factors influencing the process of spatial knowledge acquisition within virtual reality environments

As the fidelity of virtual environments (VE) continues to increase, the possibility of using them as training platforms is becoming increasingly realistic for a variety of application domains, including military and emergency personnel training. In the past, there was much debate on whether the acquisition and subsequent transfer of spatial knowledge from VEs to the real world is possible, or whether the differences in medium during training would essentially be an obstacle to truly learning geometric space. In this paper, the authors present various cognitive and environmental factors that not only contribute to this process, but also interact with each other to a certain degree, leading to a variable exposure time requirement in order for the process of spatial knowledge acquisition (SKA) to occur. The cognitive factors that the authors discuss include a variety of individual user differences such as: knowledge and experience; cognitive gender differences; aptitude and spatial orientation skill; and finally, cognitive styles. Environmental factors discussed include: Size, Spatial layout complexity and landmark distribution. It may seem obvious that since every individual's brain is unique - not only through experience, but also through genetic predisposition that a one size fits all approach to training would be illogical. Furthermore, considering that various cognitive differences may further emerge when a certain stimulus is present (e.g. complex environmental space), it would make even more sense to understand how these factors can impact spatial memory, and to try to adapt the training session by providing visual/auditory cues as well as by changing the exposure time requirements for each individual. The impact of this research domain is important to VE training in general, however within service and military domains, guaranteeing appropriate spatial training is critical in order to ensure that disorientation does not occur in a life or death scenario.