Learning CSR for sustainable corporate advantage

Corporate Social Responsibility (CSR) has become a strategic and operational reality of the business and academic world. Not that the principles of CSR are always respected or that its practice is consistently applied. Bearing in mind the multi-faceted nature of both CSR and the corporate environment, as well as the paradox of what is taught in Higher Education and what is practised within its own walls, this paper provides a learning cyclical pathway to sustainable CSR implementation and progress review. As well as highlighting the role that Higher Education has to play, the paper emphasises that in order to embed CSR within the corporate environment, questions need to be raised concerning on-going CSR improvement in order to both protect and engage a wide range of stakeholders towards sustainable corporate advantage.

Learning of shared attention in sociable robotics

Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They Should be able to recognize human beings and each other, and to engage in social, interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. Such architecture must have structures and mechanisms to allow social interaction. behavior control and learning from environment. Learning processes described oil Science of Behavior Analysis may lead to the development of promising methods and Structures for constructing robots able to behave socially and learn through interactions from the environment by a process of contingency learning. In this paper, we present a robotic architecture inspired from Behavior Analysis. Methods and structures of the proposed architecture, including a hybrid knowledge representation. are presented and discussed. The architecture has been evaluated in the context of a nontrivial real problem: the learning of the shared attention, employing an interactive robotic head. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human and the environment. The obtained results show that the robotic architecture is able to produce appropriate behavior and to learn from social interaction. (C) 2009 Elsevier Inc. All rights reserved.

Comparing machine learning classifiers in potential distribution modelling

Species` potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species` potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.

Andragogy vs Pedagogy: Teaching the Adult Learner in an Online Environment

The purpose of this presentation is to offer a deeper understanding of adult learning and provide tips on how to effectively teach in the online environment. The presenter will compare and contrast two learning theories: andragogy and pedagogy. Furthermore, the roles of the online instructor and e-learner will be outlined. An open discussion at the end of the presentation will allow participants to make implications as to which learning theory is more effective for online learning.

Languaging in virtual learning sites : studies of online encounters in the language-focused classroom

This thesis focuses upon a series of empirical studies which examine communication and learning in online glocal communities within higher education in Sweden. A recurring theme in the theoretical framework deals with issues of languaging in virtual multimodal environments as well as the making of identity and negotiation of meaning in these settings; analyzing the activity, what people do, in contraposition to the study of how people talk about their activity. The studies arise from netnographic work during two online Italian for Beginners courses offered by a Swedish university. Microanalyses of the interactions occurring through multimodal video-conferencing software are amplified by the study of the courses’ organisation of space and time and have allowed for the identification of communicative strategies and interactional patterns in virtual learning sites when participants communicate in a language variety with which they have a limited experience. The findings from the four studies included in the thesis indicate that students who are part of institutional virtual higher educational settings make use of several resources in order to perform their identity positions inside the group as a way to enrich and nurture the process of communication and learning in this online glocal community. The sociocultural dialogical analyses also shed light on the ways in which participants gathering in discursive technological spaces benefit from the opportunity to go to class without commuting to the physical building of the institution providing the course. This identity position is, thus, both experienced by participants in interaction, and also afforded by the ‘spaceless’ nature of the online environment.

Machine Translation and the Disruption of Foreign Language Learning Activities

This study examines the question of how language teachers in a highly technologyfriendly university environment view machine translation and the implications that this has for the personal learning environments of students. It brings an activity-theory perspective to the question, examining the ways that the introduction of new tools can disrupt the relationship between different elements in an activity system. This perspective opens up for an investigation of the ways that new tools have the potential to fundamentally alter traditional learning activities. In questionnaires and group discussions, respondents showed general agreement that although use of machine translation by students could be considered cheating, students are bound to use it anyway, and suggested that teachers focus on the kinds of skills students would need when using machine translation and design assignments and exams to practice and assess these skills. The results of the empirical study are used to reflect upon questions of what the roles of teachers and students are in a context where many of the skills that a person needs to be able to interact in a foreign language increasingly can be outsourced to laptops and smartphones.

Machine translation and the disruption of foreign language learning activities

This study examines the question of how language teachers in a highly technology-friendly university environment view machine translation and the implications that this has for the personal learning environments of students. It brings an activity-theory perspective to the question, examining the ways that the introduction of new tools can disrupt the relationship between different elements in an activity system. This perspective opens up for an investigation of the ways that new tools have the potential to fundamentally alter traditional learning activities. In questionnaires and group discussions, respondents showed general agreement that although use of machine translation by students could be considered cheating, students are bound to use it anyway, and suggested that teachers focus on the kinds of skills students would need when using machine translation and design assignments and exams to practice and assess these skills. The results of the empirical study are used to reflect upon questions of what the roles of teachers and students are in a context where many of the skills that a person needs to be able to interact in a foreign language increasingly can be outsourced to laptops and smartphones.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.