Learning and management skills: the internationalization of Grupo Arcor. Analysis from a process-based approach

This paper analyzes the path of the international expansion of Grupo Arcor, an Argentine multinational company specializing in confectionery. The objective is to entify corporate strategies and business learning that led this Latin American firm to establish itself as one of the leading manufacturers in confectionery industry ,particularly in the 21st Century. The analysis is primarily qualitative in order to identify the economic dimension as a determinant in the internationalization process; a processbased approach from the Uppsala Model is used for this. However, the study is also complemented with a regression analysis to test if the firm was driven to expand internationally by the expectations on the degree of globalization of the industry and the accumulation of experience in foreign markets, and if the company was influenced by psychic distance in choosing the location of its investment; given the influence of these variables in Grupo Arcor business strategies. Our findings suggest that Grupo Arcor, was able to become global due to strategies such as vertical integration, diversification of products and geographical markets (based on psychic distance) and indeed some strategies were consequence of the globalization of the sector and the accumulation of experience in foreign markets.

Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG.

In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models. However, no study has revealed the decrease in viscoelasticity associated with learning that would imply improvement of internal models as well as synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trials having identical trajectory and torque profiles. Subjects were asked to perform a hand movement in unison with a target moving along a specified and unusual trajectory, with shoulder and elbow in the horizontal plane at the shoulder level. To evaluate joint viscoelasticity during the learning of this movement, we proposed an index of muscle co-contraction around the joint (IMCJ). The IMCJ was defined as the summation of the absolute values of antagonistic muscle torques around the joint and computed from the linear relation between surface EMG and joint torque. The IMCJ during isometric contraction, as well as during movements, was confirmed to correlate well with joint stiffness estimated using the conventional method, i.e., applying mechanical perturbations. Accordingly, the IMCJ during the learning of the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were decomposed into long-term components that showed decreases in IMCJ in accordance with learning with little change in the trajectory and short-term interactions between the IMCJ and performance error. A cross-correlation analysis and impulse responses both suggested that higher IMCJs follow poor performances, and lower IMCJs follow good performances within a few successive trials. Our results support the hypothesis that viscoelasticity contributes more when internal models are inaccurate, while internal models contribute more after the completion of learning. It is demonstrated that the CNS regulates viscoelasticity on a short- and long-term basis depending on performance error and finally acquires smooth and accurate movements while maintaining stability during the entire learning process.

Learning Euclidean Embeddings for Indexing and Classification

BoostMap is a recently proposed method for efficient approximate nearest neighbor retrieval in arbitrary non-Euclidean spaces with computationally expensive and possibly non-metric distance measures. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. The key idea is formulating embedding construction as a machine learning task, where AdaBoost is used to combine simple, 1D embeddings into a multidimensional embedding that preserves a large amount of the proximity structure of the original space. This paper demonstrates that, using the machine learning formulation of BoostMap, we can optimize embeddings for indexing and classification, in ways that are not possible with existing alternatives for constructive embeddings, and without additional costs in retrieval time. First, we show how to construct embeddings that are query-sensitive, in the sense that they yield a different distance measure for different queries, so as to improve nearest neighbor retrieval accuracy for each query. Second, we show how to optimize embeddings for nearest neighbor classification tasks, by tuning them to approximate a parameter space distance measure, instead of the original feature-based distance measure.

Learning a Family of Detectors

Object detection and recognition are important problems in computer vision. The challenges of these problems come from the presence of noise, background clutter, large within class variations of the object class and limited training data. In addition, the computational complexity in the recognition process is also a concern in practice. In this thesis, we propose one approach to handle the problem of detecting an object class that exhibits large within-class variations, and a second approach to speed up the classification processes. In the first approach, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly solved with using a multiplicative form of two kernel functions. One kernel measures similarity for foreground-background classification. The other kernel accounts for latent factors that control within-class variation and implicitly enables feature sharing among foreground training samples. For applications where explicit parameterization of the within-class states is unavailable, a nonparametric formulation of the kernel can be constructed with a proper foreground distance/similarity measure. Detector training is accomplished via standard Support Vector Machine learning. The resulting detectors are tuned to specific variations in the foreground class. They also serve to evaluate hypotheses of the foreground state. When the image masks for foreground objects are provided in training, the detectors can also produce object segmentation. Methods for generating a representative sample set of detectors are proposed that can enable efficient detection and tracking. In addition, because individual detectors verify hypotheses of foreground state, they can also be incorporated in a tracking-by-detection frame work to recover foreground state in image sequences. To run the detectors efficiently at the online stage, an input-sensitive speedup strategy is proposed to select the most relevant detectors quickly. The proposed approach is tested on data sets of human hands, vehicles and human faces. On all data sets, the proposed approach achieves improved detection accuracy over the best competing approaches. In the second part of the thesis, we formulate a filter-and-refine scheme to speed up recognition processes. The binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the face recognition grand challenge version 2 data set, hand shape detection and parameter estimation on a hand data set, and vehicle detection and estimation of the view angle on a multi-pose vehicle data set. On all data sets, our approach is at least five times faster than simply evaluating all foreground state hypotheses with virtually no loss in classification accuracy.

Learning Embeddings for Indexing, Retrieval, and Classification, with Applications to Object and Shape Recognition in Image Databases

Nearest neighbor retrieval is the task of identifying, given a database of objects and a query object, the objects in the database that are the most similar to the query. Retrieving nearest neighbors is a necessary component of many practical applications, in fields as diverse as computer vision, pattern recognition, multimedia databases, bioinformatics, and computer networks. At the same time, finding nearest neighbors accurately and efficiently can be challenging, especially when the database contains a large number of objects, and when the underlying distance measure is computationally expensive. This thesis proposes new methods for improving the efficiency and accuracy of nearest neighbor retrieval and classification in spaces with computationally expensive distance measures. The proposed methods are domain-independent, and can be applied in arbitrary spaces, including non-Euclidean and non-metric spaces. In this thesis particular emphasis is given to computer vision applications related to object and shape recognition, where expensive non-Euclidean distance measures are often needed to achieve high accuracy. The first contribution of this thesis is the BoostMap algorithm for embedding arbitrary spaces into a vector space with a computationally efficient distance measure. Using this approach, an approximate set of nearest neighbors can be retrieved efficiently - often orders of magnitude faster than retrieval using the exact distance measure in the original space. The BoostMap algorithm has two key distinguishing features with respect to existing embedding methods. First, embedding construction explicitly maximizes the amount of nearest neighbor information preserved by the embedding. Second, embedding construction is treated as a machine learning problem, in contrast to existing methods that are based on geometric considerations. The second contribution is a method for constructing query-sensitive distance measures for the purposes of nearest neighbor retrieval and classification. In high-dimensional spaces, query-sensitive distance measures allow for automatic selection of the dimensions that are the most informative for each specific query object. It is shown theoretically and experimentally that query-sensitivity increases the modeling power of embeddings, allowing embeddings to capture a larger amount of the nearest neighbor structure of the original space. The third contribution is a method for speeding up nearest neighbor classification by combining multiple embedding-based nearest neighbor classifiers in a cascade. In a cascade, computationally efficient classifiers are used to quickly classify easy cases, and classifiers that are more computationally expensive and also more accurate are only applied to objects that are harder to classify. An interesting property of the proposed cascade method is that, under certain conditions, classification time actually decreases as the size of the database increases, a behavior that is in stark contrast to the behavior of typical nearest neighbor classification systems. The proposed methods are evaluated experimentally in several different applications: hand shape recognition, off-line character recognition, online character recognition, and efficient retrieval of time series. In all datasets, the proposed methods lead to significant improvements in accuracy and efficiency compared to existing state-of-the-art methods. In some datasets, the general-purpose methods introduced in this thesis even outperform domain-specific methods that have been custom-designed for such datasets.

Capturing teachers' experience of learning design through case studies

This article distinguishes three dimensions to learning design: a technological infrastructure, a conceptual framework for practice that focuses on the creation of structured sequences of learning activities, and a way to represent and share practice through the use of mediating artefacts. Focusing initially on the second of these dimensions, the article reports the key findings from an exploratory study, eLIDA CAMEL. This project examined a hitherto under-researched aspect of learning design: what teachers who are new to the domain perceive to be its value as a framework for practice in the design of both flexible and classroom-based learning. Data collection comprised 13 case studies constructed from participants' self-reports. These suggest that providing students with a structured sequence of learning activities was the major value to teachers. The article additionally discusses the potential of such case studies to function as mediating artefacts for practitioners who are considering experimenting with learning design.

"Practise what we preach": a social constructivist approach to blended learning

This presentation reports on the formal evaluation, through questionnaires, of a new Level 1 undergraduate course, for 130 student teachers, that uses blended learning. The course design seeks to radicalise the department’s approach to teaching, learning and assessment and use students as change agents. Its structure and content, model social constructivist approaches to learning. Building on the student’s experiences of and, reflections on, previous learning, promotes further learning through the support of “able others” (Vygotsky 1978), facilitating and nurturing a secure community of practice for students new to higher education. The course’s design incorporates individual, paired, small and large group activities and exploits online video, audio and text materials. Course units begin and end with face-to-face tutor-led activities. Online elements, including discussions and formative submissions, are tutor-mediated. Students work together face-to-face and online to read articles, write reflections, develop presentations, research and share experiences and resources. Summative joint assignments and peer assessments emphasise the value of collaboration and teamwork for academic, personal and professional development. Initial informal findings are positive, indicating that students have engaged readily with course content and structure, with few reporting difficulties accessing or using technology. Students have welcomed the opportunity to work together to tackle readings in a new genre, pilot presentation skills and receive and give constructive feedback to peers. Course tutors have indicated that depth and quality of study are evident, with regular online formative submissions enabling tutors to identify and engage directly with student’s needs, provide feedback and develop appropriately designed distance and face-to-face teaching materials. Pastoral tutors have indicated that students have reported non-engagement of peers, leading to the rapid application of academic or personal support. Outcomes of the formal evaluation will inform the development of Level 2 and 3 courses and influence the department’s use of blended learning.

Bridging the gap with teamwork: collaborative leadership for communities of practice in the learning and skills sector

Collaborative approaches in leadership and management are increasingly acknowledged to play a key role in successful institutions in the learning and skills sector (LSS) (Ofsted, 2004). Such approaches may be important in bridging the potential 'distance' (psychological, cultural, interactional and geographical) (Collinson, 2005) that may exist between 'leaders' and 'followers', fostering more democratic communal solidarity. This paper reports on a 2006-07 research project funded by the Centre for Excellence in Leadership (CEL) that aimed to collect and analyse data on 'collaborative leadership' (CL) in the learning and skills sector. The project investigated collaborative leadership and its potential for benefiting staff through trust and knowledge-sharing in communities of practice (CoPs). The project forms part of longer-term educational research investigating leadership in a collaborative inquiry process (Jameson et al., 2006). The research examined the potential for CL to benefit institutions, analysing respondents' understanding of and resistance to collaborative practices. Quantitative and qualitative data from senior managers and lecturers was analysed using electronic data in SPSS and Tropes Zoom. The project aimed to recommend systems and practices for more inclusive, diverse leadership (Lumby et al., 2005). Collaborative leadership has increasingly gained international prominence as emphasis shifted towards team leadership beyond zero-sum 'leadership'/ 'followership' polarities into more mature conceptions of shared leadership spaces, within which synergistic leadership spaces can be mediated. The relevance of collaboration within the LSS has been highlighted following a spate of recent government-driven policy developments in FE. The promotion of CL addresses concerns about the apparent 'remoteness' of some senior managers, and the 'neo-management' control of professionals which can increase 'distance' between leaders and 'followers' and may de-professionalise staff in an already disempowered sector. Positive benefit from 'collaborative advantage' tends to be assumed in idealistic interpretations of CL, but potential 'collaborative inertia' may be problematic in a sector characterised by rapid top-down policy changes and continuous external audit and surveillance. Constant pressure for achievement against goals leaves little time for democratic group negotiations, despite the desires of leaders to create a more collaborative ethos. Yet prior models of intentional communities of practice potentially offer promise for CL practice to improve group performance despite multiple constraints. The CAMEL CoP model (JISC infoNet, 2006) was linked to the project, providing one practical way of implementing CL within situated professional networks.The project found that a good understanding of CL was demonstrated by most respondents, who thought it could enable staff to share power and work in partnership to build trust and conjoin skills, abilities and experience to achieve common goals for the good of the sector. However, although most respondents expressed agreement with the concept and ideals of CL, many thought this was currently an idealistically democratic, unachievable pipe dream in the LSS. Many respondents expressed concerns with the 'audit culture' and authoritarian management structures in FE. While there was a strong desire to see greater levels of implementation of CL, and 'collaborative advantage' from the 'knowledge sharing benefit potential' of team leadership, respondents also strongly advised against the pitfalls of 'collaborative inertia'. A 'distance' between senior leadership views and those of staff lower down the hierarchy regarding aspects of leadership performance in the sector was reported. Finally, the project found that more research is needed to investigate CL and develop innovative methods of practical implementation within autonomous communities of professional practice.

Verstetigung und Verbreitung von E-Learning im Verbundstudium. Onlinebefragung als Promotor und Instrument zur Einbeziehung der Lehrenden bei der Entwicklung und Umsetzung

Das Verbundstudium der nordrhein-westfälischen Fachhochschulen bietet über 3000 Studierenden die Möglichkeit, in einer Kombination von Präsenz- und Selbststudium neben dem Beruf zu studieren. Das Institut für Verbundstudien koordiniert und organisiert die Kooperationsprozesse der Hochschulen und engagiert sich mit seinem Bereich Hochschuldidaktik und Fernstudienentwicklung als Entwicklungs- und Kompetenzzentrum im Bereich der Neuen Medien und des E-Learnings. Zur Verbreitung und Verstetigung der digitalen Lehr- und Lernangebote sowie der Optimierung der Kooperations- und Supportstrukturen hat das Institut eine Onlinebefragung von 200 Lehrenden zur Situation und den Perspektiven des E-Learnings im Verbundstudium durchgeführt. Die Studie zeigt, dass für die Lehrenden auch zukünftig die gedruckte Lerneinheit das zentrale Element der Lehre sein wird. Sie sehen Bedarf zur Ergänzung und Anreicherung des Studiums sowie des Lernens und wünschen sich zur Unterstützung der Lehre ergänzende digitale Elemente vor allem in folgenden Bereichen: Kommunikation, Ergänzungen zu Lerneinheiten (Linklisten, Übungen, ergänzende Medien und Materialien), übergreifendes Glossar. Die Ergebnisse der Onlinebefragung sind die Grundlage des von den Gremien des Verbundstudiums beschlossenen E-Learning-Konzepts. Die von den Lehrenden gewünschten digitalen Elemente und Funktionen sind im Rahmen der Entwicklung durch den Bereich Hochschuldidaktik und Fernstudienentwicklung in der E-Learning-Umgebung VS-online umgesetzt worden. Zurzeit werden die bereitgestellten Elemente und Funktionen von den einzelnen Verbundstudiengängen mit Beiträgen und Inhalten gefüllt. (DIPF/Orig.)

Taiwanese Learners’ Motivation and Attitude Towards Completing an Online Distance Education Course During a One Year Industrial Placement

This mixed methods study investigated language learning motivation in an one-year e-learning course for technological university students to bridge the geographical divide between students on industrial placements when studying graded readers using an e-learning course to improve their English competence and to pass the General English Proficiency Test. Data was collected through questionnaires and course feedback. The results of this study extend Gardner’s socio-educational model in an e-learning environment by adding the new category, Computer Attitudes, which was proven to be highly correlated with Motivation. Although the low proficiency English students had good computer skills, their habits of using the computer for entertainment and their lack of the skill of “technological communication efficacy” caused increased anxiety when using computers and thus provided them with a lower computer confidence over time. Consequently, it is recommended that sound e-learning training should be provided to all of the students prior to embarking on an e-leaning course so that these learners can benefit from online language learning in the future.

Semi batch learning with store management using enhanced conjugate gradient

This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.

Implementing a Videoconferencing Studio in Cape Verde to Support a Blended Learning Education System

In 2004, the Calouste Gulbenkian Foundation invited the University of Aveiro to develop an education and training program in advanced topics of ICT for Cape Verde. The focus should be on technologies to support the development of distance education. Two years later, when the program was started, the University of Aveiro had a high-performance videoconferencing Studio installed by the Foundation for National Scientific Computing. However, the investment to duplicate this high quality structure and operating costs were not compatible neither with the project’s budget nor with the technological options available in Cape Verde. This paper demonstrates the decision-making process by an economically viable option to meet the needs and local peculiarities.

Adoção de uma plataforma de e-learning no ensino superior : o caso da Faculdade de Farmácia da Universidade de Lisboa

Trabalho de projeto de mestrado, Tecnologias e Metodologias em E-learning, Universidade de Lisboa, Instituto de Educação, Faculdade de Ciências, 2013

E-Tutor : especialização pedagógica em e-learning

Trabalho de projeto de mestrado, Ciências da Educação (Área de especialização Formação de Adultos), Universidade de Lisboa, Instituto de Educação, 2014