Learning to manage in knowledge based organizations: the role of portfolios

Paper presented at the 8th European Conference on Knowledge Management, Barcelona, 6-7 Sep. 2008 URL: http://www.academic-conferences.org/eckm/eckm2007/eckm07-home.htm

The role of Lifelong Learning in the creation of a knowledge-based society for Europe: european policies

Link do editor: http://www.igi-global.com/chapter/role-lifelong-learning-creation-european/13314

Knowledge sharing and learning processes: case study of portuguese technological schools and higher education institutions

Paper to be presented at the ESREA Conference Learning to Change? The Role of Identity and Learning Careers in Adult Education, 7-8 December, 2006, Université Catholique Louvain, Louvain–la-Neuve, Belgium

The European challenge of KMN and innovation: a skills and competence portfolio for knowledge worker in SME's

Versão editor: http://www.isegi.unl.pt/docentes/acorreia/documentos/European_Challenge_KM_Innovation_2004.pdf

Knowledge magement: a rewarding challenge for SME's?

Paper presented at Information Resources Management Association International Conference, in Philadelphia (PA), 18-21 May 2003

Virtual communities of practice: examining the motivations and constraints in their knowledge creation and knowledge transfer preocesses

Paper accepted for the OKLC 2009 - International Conference on Organizational Learning, Knowledge and Capabilities (26-28th, April 2009, Amsterdam, the Netherlands).

Multiple-choice tests: a tool in assessing knowledge

The purpose of this paper is to analyse if Multiple-Choice Tests may be considered an interesting alternative for assessing knowledge, particularly in the Mathematics area, as opposed to the traditional methods, such as open questions exams. In this sense we illustrate some opinions of the researchers in this area. Often the perception of the people about the construction of this kind of exams is that they are easy to create. But it is not true! Construct well written tests it’s a hard work and needs writing ability from the teachers. Our proposal is analyse the construction difficulties of multiple - choice tests as well some advantages and limitations of this type of tests. We also show the frequent critics and worries, since the beginning of this objective format usage. Finally in this context some examples of Multiple-Choice Items in the Mathematics area are given, and we illustrate as how we can take advantage and improve this kind of tests.

Knowledge and technologies in innovative information systems

7th Mediterranean Conference on Information Systems, MCIS 2012, Guimaraes, Portugal, September 8-10, 2012, Proceedings Series: Lecture Notes in Business Information Processing, Vol. 129

The Role of lifelong learning in creation of a european knowledge-based society

The dominant discourse in education and training policies, at the turn of the millennium, was on lifelong learning (LLL) in the context of a knowledge-based society. As Green points (2002, pp. 611-612) several factors contribute to this global trend: The demographic change: In most advanced countries, the average age of the population is increasing, as people live longer; The effects of globalisation: Including both economic restructuring and cultural change which have impacts on the world of education; Global economic restructuring: Which causes, for example, a more intense demand for a higher order of skills; the intensified economic competition, forcing a wave of restructuring and creating enormous pressure to train and retrain the workforce In parallel, the “significance of the international division of labour cannot be underestimated for higher education”, as pointed out by Jarvis (1999, p. 250). This author goes on to argue that globalisation has exacerbated differentiation in the labour market, with the First World converting faster to a knowledge economy and a service society, while a great deal of the actual manufacturing is done elsewhere.

Organization knowledge management change from a complex adaptive systems perpective with ability for ambidexeterity

We are working on the confluence of knowledge management, organizational memory and emergent knowledge with the lens of complex adaptive systems. In order to be fundamentally sustainable organizations search for an adaptive need for managing ambidexterity of day-to-day work and innovation. An organization is an entity of a systemic nature, composed of groups of people who interact to achieve common objectives, making it necessary to capture, store and share interactions knowledge with the organization, this knowledge can be generated in intra-organizational or inter-organizational level. The organizations have organizational memory of knowledge of supported on the Information technology and systems. Each organization, especially in times of uncertainty and radical changes, to meet the demands of the environment, needs timely and sized knowledge on the basis of tacit and explicit. This sizing is a learning process resulting from the interaction that emerges from the relationship between the tacit and explicit knowledge and which we are framing within an approach of Complex Adaptive Systems. The use of complex adaptive systems for building the emerging interdependent relationship, will produce emergent knowledge that will improve the organization unique developing.

Risk of drug interaction: combination of antidepressants and other drugs

OBJECTIVE: To assess the frequency of combination of antidepressants with other drugs and risk of drug interactions in the setting public hospital units in Brazil. METHODS: Prescriptions of all patients admitted to a public hospital from November 1996 to February 1997 were surveyed from the hospital's data processing center in São Paulo, Brazil. A manual search of case notes of all patients admitted to the psychiatric unit from January 1993 to December 1995 and all patients registered in the affective disorders outpatient clinic in December 1996 was carried out. Patients taking any antidepressant were identified and concomitant use of drugs was checked. By means of a software program (Micromedex®) drug interactions were identified. RESULTS: Out of 6,844 patients admitted to the hospital, 63 (0.9%) used antidepressants and 16 (25.3%) were at risk of drug interaction. Out of 311 patients in the psychiatric unit, 63 (20.2%) used antidepressants and 13 of them (20.6%) were at risk. Out of 87 patients in the affective disorders outpatient clinic, 43 (49.4%) took antidepressants and 7 (16.2%) were at risk. In general, the use of antidepressants was recorded in 169 patients and 36 (21.3%) were at risk of drug interactions. Twenty different forms of combinations at risk of drug interactions were identified: four were classified as mild, 15 moderate and one severe interaction. CONCLUSION: In the hospital general units the number of drug interactions per patient was higher than in the psychiatric unit; and prescription for depression was lower than expected.

Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.

Symbolic Knowledge Extraction from Trained Neural Networks Governed by Lukasiewicz Logics

This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.

Drug abuse among workers in Brazilian regions

OBJECTIVE: Many business organizations in Brazil have adopted drug testing programs in the workplace since 1992. Rehabilitation, rather than layoff and disciplinary measures, has been offered as part of the Brazilian employee assistance programs. The purpose study is to profile drug abuse among company workers of different Brazilian geographical regions. METHODS: Urine samples of 12,700 workers from five geographical regions were tested for the most common illicit drugs of abuse in the country: marijuana, cocaine, and amphetamine. Enzyme multiplied immunoassay technique (EMIT) and gas chromatography coupled with mass spectrometry (GC/MS) were the techniques utilized for urine testing. The distribution of collected urine samples according to geographical regions was: 72.0% southeast, 13.8% northeast, 7.9% south, 5.7% central west and 0.6% north. RESULTS: Of all samples analyzed, 1.8% was found to be positive for drugs: 0.5% from the south region, 1.1% from northeast, 1.2% from central west, 1.3% from north, and 2.2% from southeast. Of these, 59.9% was marijuana, 17.7% cocaine, 14.6% amphetamine, and 7.7% associated drugs. CONCLUSIONS: The distribution of drugs found in the samples shows a regional variation. Marijuana, however, was found in all regions. Cocaine was seen only in central west and southeast regions. Amphetamine was found in northeast, central west, and southeast regions.