Action learning: how learning transfers from entrepreneurs to small firms

The paper presents research with small and medium enterprise (SME) owners who have participated in a leadership development programme. The primary focus of the paper is on learning transfer and factors affecting it, arguing that entrepreneurs must engage in ‘action’ in order to ‘learn’ and that under certain conditions they may transfer learning to their firm. The paper draws on data from 19 focus groups undertaken from 2010 to 2012, involving 51 participants in the LEAD Wales programme. It considers the literatures exploring learning transfer and develops a conceptual framework, outlining four areas of focus for entrepreneurial learning. Utilising thematic analysis, it describes and evaluates what (actual facts and information) and how (techniques, styles of learning) participants transfer and what actions they take to improve the business and develop their people. The paper illustrates the complex mechanisms involved in this process and concludes that action learning is a method of facilitating entrepreneurial learning which is able to help address some of the problems of engagement, relevance and value that have been highlighted previously. The paper concludes that the efficacy of an entrepreneurial learning intervention in SMEs may depend on the effectiveness of learning transfer.

'Rules of engagement’: how experiential learning facilitates the formation of a public–private partnership in Russia

Purpose – The purpose of this paper is to investigate to what extent one can apply experiential learning theory (ELT) to the public-private partnership (PPP) setting in Russia and to draw insights regarding the learning cycle ' s nature. Additionally, the paper assesses whether the PPP case confirms Kolb ' s ELT. Design/methodology/approach – The case study draws upon primary data which the authors collected by interviewing informants including a PPP operator ' s managers, lawyers from Russian law firms and an expert from the National PPP Centre. The authors accomplished data source triangulation in order to ensure a high degree of research validity. Findings – Experiential learning has resulted in a successful and a relatively fast PPP project launch without the concessionary framework. The lessons learned include the need for effective stakeholder engagement; avoiding being stuck in bureaucracy such as collaboration with Federal Ministries and anti-trust agency; avoiding application for government funding as the approval process is tangled and lengthy; attracting strategic private investors; shaping positive public perception of a PPP project; and making continuous efforts in order to effectively mitigate the public acceptance risk. Originality/value – The paper contributes to ELT by incorporating the impact of social environment in the learning model. Additionally, the paper tests the applicability of ELT to learning in the complex organisational setting, i.e., a PPP.

An Information Theory framework for two-stage binary image operator design

The design of translation invariant and locally defined binary image operators over large windows is made difficult by decreased statistical precision and increased training time. We present a complete framework for the application of stacked design, a recently proposed technique to create two-stage operators that circumvents that difficulty. We propose a novel algorithm, based on Information Theory, to find groups of pixels that should be used together to predict the Output Value. We employ this algorithm to automate the process of creating a set of first-level operators that are later combined in a global operator. We also propose a principled way to guide this combination, by using feature selection and model comparison. Experimental results Show that the proposed framework leads to better results than single stage design. (C) 2009 Elsevier B.V. All rights reserved.

Translation, Communication and 'Learning' : Experiences from teaching a master program in Viet Nam

In Vietnam, as in other Asian countries, co-operation with foreign universities plays an important role for the development of higher education. This paper is based on personal experiences from teaching a Swedish Master Programme in Education Science at Vietnam National University in Hanoi. Using theories developed by Lev Vygotsky and Donald Schon, the programme is explored as an inter-cultural learning process. Three aspects are focused upon. Firstly, the fact that communication between students and teachers is conducted with the help of translators who support both teachers and students in their attempt to understand and make themselves understood. Secondly, the expressed need to connect the ideas and techniques which are studied in the programme to the students´ professional worlds. Thirdly, the need to construct a framework wherein the students can inquire into their own situations and to encourage them to try new and more productive ways to deal with problems they are confronted with.

E-learning opens the door to the global community. Novice users experiences of e-learning in a Somali University

E-learning has become one of the primary ways of delivering education around the globe. In Somalia, which is a country torn within and from the global community by a prolonged civil war, University of Hargeisa has in collaboration with Dalarna University in Sweden adopted, for the first time, e-learning. This study explores barriers and facilitators to e-learning usage, experienced by students in Somalia’s higher education, using the University of Hargeisa as case study. Interviews were conducted with students to explore how University of Hargeisa’s novice users perceived elearning, and what factors positively and negatively affected their e-learning experiences. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used as a framework for interpreting the results. The findings show that, in general, the students have a very positive attitude towards e-learning, and they perceived that e-learning enhanced their educational experience. The communication aspect was found to be especially important for Somali students, as it facilitated a feeling of belonging to the global community of students and scholars and alleviated the war-torn country’s isolation. However, some socio-cultural aspects of students’ communities negatively affected their e-learning experience. This study ends with recommendations based on the empirical findings to promote the use and enhance the experience of e-learning in post conflict Somali educational institutions

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.

Optimized Water Demand Management Through Intelligent Sensing And Analytics: The WISDOM Approach

New business and technology platforms are required to sustainably manage urban water resources [1,2]. However, any proposed solutions must be cognisant of security, privacy and other factors that may inhibit adoption and hence impact. The FP7 WISDOM project (funded by the European Commission - GA 619795) aims to achieve a step change in water and energy savings via the integration of innovative Information and Communication Technologies (ICT) frameworks to optimize water distribution networks and to enable change in consumer behavior through innovative demand management and adaptive pricing schemes [1,2,3]. The WISDOM concept centres on the integration of water distribution, sensor monitoring and communication systems coupled with semantic modelling (using ontologies, potentially connected to BIM, to serve as intelligent linkages throughout the entire framework) and control capabilities to provide for near real-time management of urban water resources. Fundamental to this framework are the needs and operational requirements of users and stakeholders at domestic, corporate and city levels and this requires the interoperability of a number of demand and operational models, fed with data from diverse sources such as sensor networks and crowsourced information. This has implications regarding the provenance and trustworthiness of such data and how it can be used in not only the understanding of system and user behaviours, but more importantly in the real-time control of such systems. Adaptive and intelligent analytics will be used to produce decision support systems that will drive the ability to increase the variability of both supply and consumption [3]. This in turn paves the way for adaptive pricing incentives and a greater understanding of the water-energy nexus. This integration is complex and uncertain yet being typical of a cyber-physical system, and its relevance transcends the water resource management domain. The WISDOM framework will be modeled and simulated with initial testing at an experimental facility in France (AQUASIM – a full-scale test-bed facility to study sustainable water management), then deployed and evaluated in in two pilots in Cardiff (UK) and La Spezia (Italy). These demonstrators will evaluate the integrated concept providing insight for wider adoption.

Optimal taxation with gradual learning of types

An important feature of life-cycle models is the presence of uncertainty regarding one’s labor income. Yet this issue, long recognized in different areas, has not received enough attention in the optimal taxation literature. This paper is an attempt to fill this gap. We write a simple 3 period model where agents gradually learn their productivities. In a framework akin to Mirrlees’ (1971) static one, we derive properties of optimal tax schedules and show that: i) if preferences are (weakly) separable, uniform taxation of goods is optimal, ii) if they are (strongly) separable capital income is to rate than others forms of investiment.

A case study approach to classroom experiential learning of problem structuring methods

The proposed research aims at consolidating two years of practical experience in developing a classroom experiential learning pedagogic approach for the problem structuring methods (PSMs) of operational research. The results will be prepared as papers to be submitted, respectively, to the Brazilian ISSS-sponsored system theory conference in São Paulo, and to JORS. These two papers follow the submission (in 2004) of one related paper to JORS which is about to be resubmitted following certain revisions. This first paper draws from the PSM and experiential learning literatures in order to introduce a basic foundation upon which a pedagogic framework for experiential learning of PSMs may be built. It forms, in other words, an integral part of my research in this area. By September, the area of pedagogic approaches to PSM learning will have received its first official attention - at the UK OR Society conference. My research and paper production during July-December, therefore, coincide with an important time in this area, enabling me to form part of the small cohort of published researchers creating the foundations upon which future pedagogic research will build. On the institutional level, such pioneering work also raises the national and international profile of FGVEAESP, making it a reference for future researchers in this area.

An Optimum-Path Forest framework for intrusion detection in computer networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A integração do tutorial interativo TryLogic via IMS Learning Tools Interoperability: construindo uma infraestrutura para o ensino de Lógica através de estratégias de demonstração e refutação

Logic courses represent a pedagogical challenge and the recorded number of cases of failures and of discontinuity in them is often high. Amont other difficulties, students face a cognitive overload to understand logical concepts in a relevant way. On that track, computational tools for learning are resources that help both in alleviating the cognitive overload scenarios and in allowing for the practical experimenting with theoretical concepts. The present study proposes an interactive tutorial, namely the TryLogic, aimed at teaching to solve logical conjectures either by proofs or refutations. The tool was developed from the architecture of the tool TryOcaml, through support of the communication of the web interface ProofWeb in accessing the proof assistant Coq. The goals of TryLogic are: (1) presenting a set of lessons for applying heuristic strategies in solving problems set in Propositional Logic; (2) stepwise organizing the exposition of concepts related to Natural Deduction and to Propositional Semantics in sequential steps; (3) providing interactive tasks to the students. The present study also aims at: presenting our implementation of a formal system for refutation; describing the integration of our infrastructure with the Virtual Learning Environment Moodle through the IMS Learning Tools Interoperability specification; presenting the Conjecture Generator that works for the tasks involving proving and refuting; and, finally to evaluate the learning experience of Logic students through the application of the conjecture solving task associated to the use of the TryLogic

Learning strategies evaluation in students with attention deficit hyperactivity disorder

This study aim to verify the use of learning strategies in students of the elementary level presenting interdisciplinary diagnosis of attention dei cit hyperactivity disorder (ADHD). Nine students, male gender, attending 3rd to 9th grade level of the elementary level, average age 10 years and 7 months, presenting interdisciplinary diagnosis of attention dei cit hyperactivity disorder (ADHD). h e students were submitted to the application of the Evaluation of Learning Strategies from elementary level – EAVAP-EF – scale, which aimed to evaluate the strategies reported and used by students in situation of study and learning, as follows: cognitive strategies, metacognitive strategies and absence of dysfunctional metacognitive strategies. h e general result at EAVAP-EF scale, showed that students with ADHD reached the percentile 25%, considered as low performance in the use of the learning strategies. For the variable absence of dysfunctional metacognitive strategies, the students presented percentile 30%, percentile 25% for cognitive strategies and 55% for metacognitive strategies. h e results showed that ADHD students do not use ef ectively the learning cognitive and metacognitive strategies and present the use of dysfunctional metacognitive strategies. h ese alterations match with the framework of ADHD because the entry of information, either visual or auditory, showed alterations, derived from inattention, which af ected the learning in classroom situation.

Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

Incorporating label dependency into the binary relevance framework for multi-label classification

In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. Aiming to accurately predict label combinations, in this paper we propose a simple approach that enables the binary classifiers to discover existing label dependency by themselves. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.