Ampliando as ações educativas dos museus por meio da formação de comunidades virtuais de aprendizagem

Este artigo tem como objetivo mostrar que é possível incentivar a aprendizagem informal em museus através da construção de comunidades virtuais, com base em repositórios de objetos de aprendizagem, ferramentas comunicacionais e produção de OAs por parte dos visitantes. O enfoque é incentivar a aprendizagem no sentido de motivar a participação/envolvimento do visitante nas atividades da comunidade virtual. Nesta perspectiva, partimos do pressuposto de que a informação, a comunicação, a interação e a cooperação são essenciais para o processo de aprender no contexto informal dos museus. Acreditamos que a interação e a cooperação são partes integrantes do processo de aprendizagem proporcionado por comunidades virtuais e que o principal recurso de aprendizagem oferecido nessas comunidades são os objetos de aprendizagem. Diante do exposto, construímos a Comunidade Virtual do Muzar e realizamos uma experimentação do ambiente de modo a verificar o quanto os visitantes são incentivados a produzir novos conhecimentos.

RELATIONAL MEMORY IN EARLY CHILDHOOD: DOES UNITIZATION HELP?

Young children often experience relational memory failures, which are thought to be due to underdeveloped recollection processes. Manipulations with adults, however, have suggested that relational memory tasks can be accomplished with familiarity, a processes that is fully developed during early childhood. The goal of the present study was to determine if relational memory performance could be improved in early childhood by teaching children a memory strategy (i.e., unitization) shown to increase familiarity in adults. Six- and 8-year old children were taught to use visualization strategies that either unitized or did not unitize pictures and colored borders. Analysis revealed inconclusive results regarding differences in familiarity between the two conditions, suggesting that the unitization memory strategy did not improve the contribution of familiarity as it has been shown to do in adults. Based on these findings, it cannot be concluded that unitization strategies increase the contribution of familiarity in childhood.

La enseñanza por competencias

La enseñanza por competencias, de manera semejante con el conjunto de actividades académicas que realizan los centros de educación superior, poseen una compleja estructura de atributos, tales como: conocimientos, actitudes, valores y habilidades. Bajo estas consideraciones, entendemos que los docentes de las universidades deberían de orientar entre otras, sus preocupaciones académicas, para impulsar el cambio de enfoque educativo que viabilice la construcción de puentes, para modificar favorablemente el desfase existente entre lo que demanda el sector productivo y la práctica cotidiana en los salones de clase de las universidades. Las universidades como entes responsables de la formación profesional de los(as) ciudadanos(as) de la sociedad, deben velar por coadyuvar a insertar al mercado laboral a sus graduados con una mentalidad flexible, dinámica, creativos, y con visión de futuros conocedores de las realidades de mercado, con una formación integral, plurifuncionales y versados en las herramientas tecnológicas de punta, con el propósito que sean competentes en sus labores específicas. Abstract Teaching through competencies as well as the academic activities carried out in universities, has complex structures as attributes such as: knowledge, values, and abilities. Based on this consideration, we understand that university professors should guide their academic interest towards a change on the teaching approach, allowing the building of bridges that can positively modify the existing gap between the demands of the productive sector on the current practice in the university classroom. The Universities are responsible for the professional education of the members of society and should watch over to help introduce graduates in the labor market. These graduates should characterized by a flexible, dynamic, and creative mind, capable of future projections, as acquainted with the characteristics of the real market, with a well rounded formation, multifunctional and versed in the leading technological tools; so they can be competent on their specific tasks.

Vivências de cuidado familiar à criança dependente de tecnologias: subsídios para a enfermagem

Crianças dependentes do uso de tecnologias para viver necessitam de cuidados diferenciados, tanto dos profissionais da saúde como de sua família. Para o enfermeiro atuar junto à família da criança dependente do uso de tecnologias é necessário que compreenda quais são os recursos importantes para o enfrentamento de situações estressantes que envolvem, necessariamente, o conviver com a doença e o cuidado. Ao vivenciar a relação com essa família, o enfermeiro estuda e desenvolve sua prática de aprendizado e de ensino sobre o cuidado humano, criando o fazer profissional e, nesse processo de aprender/ensinar/criar, ele concebe, organiza e expressa ações de cuidado. A compreensão da experiência da família no processo de cuidar da criança em seu cotidiano pode subsidiar as intervenções da enfermagem nessas situações. Assim, objetivou-se conhecer as vivências de famílias no cuidado às crianças dependentes de tecnologias. Realizou-se uma pesquisa qualitativa, descritiva e exploratória no primeiro semestre de 2014. Os dados foram coletados por meio de entrevistas semiestruturadas com treze familiares cuidadores de crianças dependentes de tecnologias atendidas em uma Unidade de Pediatria de um hospital universitário do sul do país e submetidos à análise temática. Encontraram-se como categorias: caracterização da população do estudo; identificação da percepção do familiar cuidador a cerca do cuidado à criança dependente de tecnologia; recepção do diagnóstico da criança; mudanças do cotidiano familiar em função do cuidado à criança; profissionais de saúde e a enfermagem: contribuições para a instrumentalização do familiar cuidador; facilidades e dificuldades encontradas pelo familiar cuidador durante o cuidado à criança dependente de tecnologia; recebimento de ajuda da rede de apoio social para o cuidado à criança. Acredita-se que este estudo possibilitou a compreensão da experiência de famílias no processo de cuidar da criança dependente de tecnologias em seu cotidiano, subsidiando as intervenções da enfermagem nessas situações.

Moradia e meio ambiente: extensão universitária e compromisso social

This book’s guideline is a description of the activities developed during the University Extension project entitled "Housing and Environment: building dialogue over the urbanization of the settlement Ilha", located in the Metropolitan Region of Curitiba. This project was coordinated by professors from the Federal University of Technology - Paraná (UTFPR). The initial objectives of the extension project were to investigate ways of intervention on the scenario of poor conditions of housing and urbanization of the settlement Ilha, for their land regularization. The book tells the story of the extension project, showing how the initial goals have changed with time. In addition, this book describes the frustrations and the learning process along the way, from the view of professors and students of UTFPR who actively participated in this project. This book also intends to report the feelings that the villagers attributed to their place of residence; the joys, stumbling and learning by using a participatory methodology from what Paulo Freire says about popular education. Moreover, the book brings the confrontation between the technical and popular vision on the regularization of the area.

'A Bayesian Optimisation Algorithm for the Nurse Scheduling Problem'

Abstract- A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. 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 conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically 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 yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all 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. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,

An Estimation of Distribution Algorithm for Nurse Scheduling

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. 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 conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

Every picture tells a story: using selfie-inspired activities to enhance social relations and encourage self-reflexivity.

This chapter explores the results of a study in Thailand that capitalised on the popularity of the selfie, providing second-year English language students with an opportunity to practise their oral presentation and speaking skills. The selfie was used not in the usual sense of online picture-sharing, but as a visual aid in a face-to-face interaction, thus serving as a “currency for social interaction” (van Dijck 2008, p.62) and communication device (Saltz, 2014). Mining the rich insights gained from the Thai study, this chapter presents another selfie-inspired activity adapted for a different context and purpose at a UK university. Initially designed to facilitate recall of students’ names linked with faces, the initiative evolved into an effective conversation starter. It is suggested that both selfie-inspired initiatives have led to serendipitous results, such as encouraging self-reflexivity among the students and promoting the development of “rapid intimacy” in the classroom (Victoria 2011, p.72). Indeed, creating a space for students to share their personal stories and enact different identities can help enrich the learning and teaching experience. This chapter also demonstrates how aspects of visual methodologies can be employed as a resource for theorising visual data, such as the selfie, for classroom application.

A intervenção educativa no processo de aprendizagem: a agência da criança

O presente relatório procura elucidar todo o processo que foi passado em contexto pré-escolar e 1º ciclo do ensino básico. Representa a simbiose entre a prática pedagógica e os pressupostos teóricos que sustentaram as metodologias e estratégias adotadas que funcionaram como alicerces fundamentais no processo de ensino e de aprendizagem. Ao longo do relatório, abordaram-se temáticas fulcrais que fundamentaram e destacaram a prática pedagógica em que foi necessário recolher informação recorrendo a instrumentos de observação e análise e utilizando uma metodologia de carater qualitativo. Para esta intervenção teve-se em conta a criança e aluno como intervenientes ativos de todo o processo de ensino/aprendizagem envolvendo a motivação para que a construção do conhecimento pudesse advir das crianças/alunos. Os princípios e intencionalidades orientadoras da prática pedagógica estão em constante confronto levando o profissional generalista a adotar uma postura reflexiva e construtiva no sentido de desenvolver a sua profissionalidade.ABSTRACT The existing report tries to clarify the whole process which refers to an experience lived in a Pre-schooler and Elementary School’s context. It represents the symbioses between pedagogical practice and theoretical assumptions that sustained methodologies and strategies adopted, which worked as fundamental foundations in the learning and teaching process. Along the report, it was approached key themes that sustain and highlight the pedagogical practice, in which it was necessary to collect information appealing to the observation and analysis instruments and using a quality character methodology. For this intervention, are taking in account, the child and student as active protagonists of the teaching/learning process, involving motivation, so that the knowledge can arise from them. The guiding principles and intentions of pedagogical practice are in constant confrontation, leading the generalist professional to adopt a reflective and constructive behavior towards developing their professionalism.

Multimediale, interaktive und patientennahe Lehrszenarien in der medizinischen Ausbildung

MeduMobile ist ein im Rahmen der Ausschreibung „Notebook-University“ vom bmb+f gefördertes Projekt. Ziel des Projektes ist es, die Ausbildung am Krankenbett im Medizinstudium zu verbessern, indem bestimmte Lehrveranstaltungen mit Hilfe von WLAN und Notebook ubiquitär auf dem Campus verfügbar gemacht werden. Hierzu werden neue, so genannte OnCall-Lehrszenarien entwickelt und erprobt, bei denen die auf Abruf bereit stehenden Medizinstudierenden alarmiert und zur Teilnahme gebeten werden, wenn akute und/oder seltene Fälle in die Klinik eingeliefert werden. Die Studierenden nehmen aktiv an den vielfach interdisziplinären Lehrveranstaltungen teil. Der Unterricht findet vor, während und nach der Live-Session statt. Der Hochschullehrer und die Studierenden können den Fall gemeinsam besprechen und die Diagnose bzw. Therapie u.a. an Hand bildgebender Verfahren (CT, Mikroskop, Röntgen, Ultraschall, ...) erarbeiten. Parallel dazu können die Studierenden Lehrmaterialen aus multimedialen Datenbanken, Medline und Internet nutzen sowie eigene Videokonferenzen für die Gruppenarbeit einsetzen. Eine solche Lehrveranstaltung kann somit mehrere didaktische Elemente beinhalten: instruktives, konstruktives, kognitives und kooperatives Lernen. An der Erprobung nehmen etwa 80 Studierende an Veranstaltungen aus 8 Fachgebieten teil. Es werden Studien zur Evaluation des didaktischen Mehrwerts sowie technischer und organisatorischer Qualität durchgeführt. Endgültige Ergebnisse werden Ende 2003 vorliegen.(DIPF/Orig.)

Contributo da utilização dos recursos educativos digitais no 1.º Ciclo do Ensino Básico

O estudo foi concretizado numa turma do 1.º ano de escolaridade do 1.º CEB, constituída por 27 alunos, numa Escola EB1, onde se realizou a Prática de Ensino Supervisionada (PES), no âmbito do Mestrado em Educação Pré-Escolar e Ensino do 1.º CEB.. O objetivo da investigação pretendeu averiguar quais os potenciais contributos que a utilização complementar de um recurso em formato digital pode contribuir para melhorar a motivação e o envolvimento dos alunos no sentido de promover mais e melhores aprendizagens. Relativamente ao tipo de investigação, optou-se por uma metodologia de natureza qualitativa que recaiu numa investigação-ação. Como técnicas de recolha de dados foram utilizadas as notas de campo, a observação participante, a entrevista semiestruturada, o inquérito por questionário e os registos fotográficos. Nesta recolha de dados houve a participação direta dos alunos da turma, da Orientadora Cooperante, do «Par Pedagógico» e dos professores titulares de turma da Escola EB1 Quinta da Granja. Os resultados obtidos após a análise e tratamento dos dados, permitiram concluir que ao utilizar este RED os alunos demonstraram terem adquirido aprendizagens mais significativas, pelo facto de se terem potenciado níveis de maior interesse, empenho, motivação, envolvimento e espírito de iniciativa no decorrer das atividades propostas.

An Estimation of Distribution Algorithm for Nurse Scheduling

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. 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 conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

Vom Widerspruch des Unterrichtens und Aneignens. Wie in der unterrichtlichen Vermittlung auch die Differenz von Sein und Sollen vermittelt wird

Schulunterricht beansprucht, mit Wolfgang Klafki gesprochen, die „doppelseitige Erschließung“ (Klafki 1959/1967, S. 43): In ihm sollen sich als relevant erachtete Inhalte den Heranwachsenden erschließen und diese sollen zugleich für jene Inhalte erschlossen werden. Nur wenn die Lern- und Bildungsprozesse der SchülerInnen im Unterricht befördert werden, wird er diesem mit ihm gesetzten pädagogischen Anspruch gerecht. Angesichts dessen, dass unterrichtliche Vermittlung vielfach ihr Ziel nicht erreicht, also SchülerInnen nicht das lernen, was ihnen gelehrt wird, stellt sich die Frage, wie die an Unterricht Beteiligten mit dieser Differenz von Sein und Sollen umgehen. Um diese zu beantworten, ist zum einen nachzuvollziehen, wie im Prozess des Unterrichts so gelehrt und gelernt wird, dass einige das zu Lernende lernen und andere dies nicht tun [...], wie also im Unterricht die „Grenze“ zwischen Verstehen und Nicht-Verstehen praktisch gezogen wird. Und zum anderen ist zu erhellen, wie der Widerspruch zwischen Sein und Sollen individuell verarbeitet wird.(DIPF/Orig.)

'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically 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 yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all 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. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,