Integration of learning and practice as part of the educational system development in Eritrea. "School Management" - a distance learning program by technical university of Kaiserslautern

The distance learning program "School Management" supports decision makers at the school and ministerial levels in the shaping of formal and informal learning processes at different levels in schools and curricula in Eritrea. This paper examines how the distance learning program is interconnected to educational system development. (DIPF/Orig.)

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

The influence of initial teacher training in future teachers' perceptions about mathematics teaching and learning

ICEMST 2014 INTERNATIONAL CONFERENCE ON EDUCATION IN MATHEMATICS, SCIENCE & TECHNOLOGY PROCEEDING BOOK (pp.865-869). Disponível em http://www.2014.icemst.com/

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Intercultural musical learning in the era of technology

Foreign students abroad need to feel integrated in the new community, which includes complex learning processes in multicultural environments. The fact that we have experienced these processes ourselves was certainly a motivation for this research, especially knowing that we could contribute to help our fellow Portuguese brass players undergoing the same experience. From the singularity of music performance in the style of playing and communication emerge many cultural aspects, which have been developed through centuries of orchestral practice. As the new students are confronted with the aesthetic musical concepts and both professional and social practices of the country they arrive in, they strive to understand these concepts and adapt themselves to the values promoted by the new music practice. The aim of this on-going research is the study of the integration of brass music students in German universities and in the German society. Notably, through the understanding of intercultural processes experienced by the students, professors can become more aware of the challenges that concern music education. In this research all ten Portuguese brass students enrolled in any German music university in the last five years were interviewed in order to deeply understand this process. With a growing importance of the technological facilities, students are able to gather more information, to prepare themselves for the new concepts they try to embrace and to better deal with a different culture.

Learning to construct our identities over the life course: a study with lesbian, gay, bisexual and transgender adults in Scotland

To date, adult educational research has had a limited focus on lesbian, gay, bisexual and transgendered (LGBT) adults and the learning processes in which they engage across the life course. Adopting a biographical and life history methodology, this study aimed to critically explore the potentially distinctive nature and impact of how, when and where LGBT adults learn to construct their identities over their lives. In-depth, semi-structured interviews, dialogue and discussion with LGBT individuals and groups provided rich narratives that reflect shifting, diverse and multiple ways of identifying and living as LGBT. Participants engage in learning in unique ways that play a significant role in the construction and expression of such identities, that in turn influence how, when and where learning happens. Framed largely by complex heteronormative forces, learning can have a negative, distortive impact that deeply troubles any balanced, positive sense of being LGBT, leading to self- censoring, alienation and in some cases, hopelessness. However, learning is also more positively experiential, critically reflective, inventive and queer in nature. This can transform how participants understand their sexual identities and the lifewide spaces in which they learn, engendering agency and resilience. Intersectional perspectives reveal learning that participants struggle with, but can reconcile the disjuncture between evolving LGBT and other myriad identities as parents, Christians, teachers, nurses, academics, activists and retirees. The study’s main contributions lie in three areas. A focus on LGBT experience can contribute to the creation of new opportunities to develop intergenerational learning processes. The study also extends the possibilities for greater criticality in older adult education theory, research and practice, based on the continued, rich learning in which participants engage post-work and in later life. Combined with this, there is scope to further explore the nature of ‘life-deep learning’ for other societal groups, brought by combined religious, moral, ideological and social learning that guides action, beliefs, values, and expression of identity. The LGBT adults in this study demonstrate engagement in distinct forms of life-deep learning to navigate social and moral opprobrium. From this they gain hope, self-respect, empathy with others, and deeper self-knowledge.

Rural Participation in the Territorial Development Processes

This paper discusses the urgency of creating a bridge between social participation and civic capacity building.  The permanent dialogue between expert and local knowledge should sustain significant, relevant learning processes from/to the rural areas of the Central American region.  Consistency and persistence of these processes will enhance human welfare based on the changes experienced in the rural areas.  Numerous Central American initiatives require effective social and institutional participation to be implemented.  Education, in its different forms and through its different resources, has the crucial responsibility of helping citizens to take advantage of those initiatives.

How should we measure online learning activity?

The proliferation of Web-based learning objects makes finding and evaluating resources a considerable hurdle for learners to overcome. While established learning analytics methods provide feedback that can aid learner evaluation of learning resources, the adequacy and reliability of these methods is questioned. Because engagement with online learning is different from other Web activity, it is important to establish pedagogically relevant measures that can aid the development of distinct, automated analysis systems. Content analysis is often used to examine online discussion in educational settings, but these instruments are rarely compared with each other which leads to uncertainty regarding their validity and reliability. In this study, participation in Massive Open Online Course (MOOC) comment forums was evaluated using four different analytical approaches: the Digital Artefacts for Learning Engagement (DiAL-e) framework, Bloom's Taxonomy, Structure of Observed Learning Outcomes (SOLO) and Community of Inquiry (CoI). Results from this study indicate that different approaches to measuring cognitive activity are closely correlated and are distinct from typical interaction measures. This suggests that computational approaches to pedagogical analysis may provide useful insights into learning processes.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

Resonancias discursivas y cortesía en prácticas de lecto-escritura

In this paper we present a study of reading comprehension based on a contrastive argumentative-discursive approach. We examine the relationship between linguistic materiality and discursive processes, observing the connection between reading in a foreign language, writing production and textual memories in the mother tongue. In addition to an interest in practical language teaching and learning processes (in this case of Spanish and Portuguese), we investigate the question of politeness and the theoretical relationship between subjectivity, language, and textuality. The latter, being understood as the result of discourse regularities, is unique for each and every production, yet is also conditioned by plural discursive memories resulting from contradictory social relationships in a specific historical context (Foucault, 1986; Pêcheux, 1990). In the experiment presented here, we follow some of the procedures of the methodology applied in the European Galatea Project developed for the study of reading strategies in the inter-comprehension between Romance languages (Dabène, 1996). We use the procedure of simulation and the subjective projection of participants as well as the notion of discursive resonance in the analysis. The results, having to do with directness and indirectness in speech and the question of politeness in two typologically close languages, lead to the conclusion that the concept of politeness goes beyond a pragmatic strategy used to avoid conflicts to be approached as a marker of cultural identity constitution. The relevance of discursive awareness and its theoretical and practical consequences are then emphasized.

Educação fisica na educação infantil : concepções e praticas de professores

Universidade Estadual de Campinas. Faculdade de Educação Física

Possibilidades corporais como expressão da inteligencia humana no processo de ensino-aprendizagem

Universidade Estadual de Campinas . Faculdade de Educação Física

A comunicação dialógica como fator determinante para os processos de ensino-aprendizagem que ocorrem na capacitação rural: um estudo de caso em um órgão público de extensão localizado no interior do Estado de São Paulo

O objetivo deste artigo é discutir quais caminhos ou descaminhos estão sendo percorridos nos processos de capacitação rural de extensionistas e agricultores de algumas localidades do interior do Estado de São Paulo. Para tanto, a pesquisa aqui relatada teve como pressuposto analisar as práticas pedagógicas e os processos de comunicação que ocorrem durante as capacitações formativas entre extensionistas e agricultores, bem como entre formadores e extensionistas. Buscou-se, ainda, identificar as influências da comunicação nos processos de ensino e aprendizagem, a fim de se repensar práticas relativas à comunicação que devem ser utilizadas nos processos de ensino-aprendizagem. Para tanto, o método de coleta de dados foi o estudo de caso com viés fenomenológico. Os dados anunciam a necessidade da formação continuada com orientação de profissionais da pedagogia a fim de que mudanças efetivas possam ser realizadas.

Reconciliando divergências: conhecimento implícito e explícito na aprendizagem

O trabalho busca integrar, com base em propostas recentes de vários autores, perspectivas acerca da aprendizagem concebidas como mutuamente excludentes. Essa reflexão se justifica em vista da importância de não se introduzir descontinuidade filogenética em um processo concebido como adaptativo, mas que é também cultural. Assim, são examinadas propostas acerca da coevolução da mente humana e da cultura que apoiariam tal perspectiva, propondo-se uma visão integrada da aprendizagem como um conjunto de processos organizados em um continuum implícito-explícito.

A expressão da modularidade

Desde Fodor (1983) a modularidade comportamental tem gerado fortes controvérsias, especialmente em torno do quanto são especializados os módulos eventuais e do quanto são encapsulados, isto é, independentes de controles externos. No presente ensaio, depois de rever formulações precursoras a respeito de instinto e de aprendizagens especializadas e de expor as características básicas da modularidade, tomo, como estudo de caso, pesquisas sobre a percepção da expressão facial da emoção, mostrando tanto o lado especializado desse processo como a sua abertura para controles externos. Termino defendendo a necessidade de, junto à estratégia de delimitar processos comportamentais especializados, colocar a questão de como eles se integram e se controlam uns aos outros e são eventualmente gerenciados por processos gerais.