Date adjustment tools : Date rollover and Individual date adjustment

Learning Management Systems (LMSs) have become a larger part of teaching and learning in the modern world. Therefore has Moodle, a free and open source e-learning tool surfaced and gained a lot of attraction and downloads. A purpose of this study has been to develop a new local plugin in Moodle with guidelines from Magnus Eriksson and Tsedey Terefe. A purpose for this project has also been to build a plugin which has the functions Date rollover and Individual date adjustment. Mid Sweden University (Miun) stated that WebCT/Blackboard was in use before Moodle and some other LMSs and the dissatisfaction with WebCT/Blackboard was rife, however some teachers liked it. Therefore WebCT/Blackboard was abandoned and Moodle was embraced. The methods of gaining information has generally been web based sources and three interviews, likewise called user tests. Programs and other aids that have been used include but are not limited to: Google Drive, LTI Provider, Moodle, Moodle documentation, Notepad++, PHP and XAMPP. The plugin has been implemented as a local plugin. The result has shown that the coded plugin, Date adjustment tools could be improved and that it was changed. In the plugin, support for old American English dates were added and the code for using the two functions “Date rollover” and “Individual date adjustment” were rewritten to not interfere with one another. A conclusion to draw from the result is that the plugin has been improved from Terefe’s implementation, although more work can be made with the plugin Date adjustment tools.

The Impact of Student Response Systems on the Learning Experience of Undergraduate Psychology Students

Student response systems (SRS) are hand-held devices or mobile phone polling systems which collate real-time, individual responses to on-screen questions. Previous research examining their role in higher education has highlighted both advantages and disadvantages of their use. This paper explores how different SRS influence the learning experience of psychology students across different levels of their programme. Across two studies, first year students’ experience of using Turningpoint clickers and second year students’ experience of using Poll Everywhere was investigated. Evaluations of both studies revealed that SRS has a number of positive impacts on learning, including enhanced engagement, active learning, peer interaction, and formative feedback. Technical and practical issues emerged as consistent barriers to the use of SRS. Discussion of these findings and the authors’ collective experiences of these technologies are used to provide insight into the way in which SRS can be effectively integrated within undergraduate psychology programmes.

Como as Percepções de Climas Autentizóticos Explicam os Comportamentos Inovadores e o Desempenho Individual: o caso de uma multinacional

O presente estudo procura investigar como as percepções de climas autentizóticos (espírito de camaradagem; confiança e credibilidade do líder; comunicação aberta e franca com o líder; oportunidades de aprendizagem e desenvolvimento pessoal; equidade/justiça; conciliação trabalho-família) explicam os comportamentos inovadores e melhoram o desempenho individual. Foram analisados os dados referentes a um questionário aplicado a 128 colaboradores de uma multinacional. Os resultados sugerem que (1) a percepção de espírito de camaradagem por parte dos colaboradores explica os seus comportamentos inovadores; (2) a percepção de equidade/justiça e de comunicação aberta e franca com o líder explica o desempenho individual; (3) o comportamento inovador influencia o desempenho individual. Pesem embora as limitações do estudo, a evidência empírica sugere que os indivíduos que percepcionam tais características autentizóticas tendem a adoptar mais comportamentos inovadores e a melhorar os seus desempenhos individuais. / This study investigates how the perception of authentizotic climate (camaraderie spirit; leader trust and credibility; the open and frank communication with the leader; learning opportunities and personal development; equity / justice; work-family conciliation) explain the innovative behavior and improve individual performance. The data was analyzed from a questionnaire answered by 128 employees of a multinational. The results suggest that (1) the employees perception of camaraderie spirit explain their innovative behavior, (2) the fairness / justice perception and open and frank communication with the leader explains the individual performance, (3) the innovative behavior influences individual performance. In spite of the study limitations, the empirical evidence suggests that individuals who perceive such authentizotic features tend to adopt more innovative behaviors and improve their individual performances.

Designing and Developing Digital and Blended Learning Solutions

The continuous advancement in computing, together with the decline in its cost, has resulted in technology becoming ubiquitous (Arbaugh, 2008, Gros, 2007). Technology is growing and is part of our lives in almost every respect, including the way we learn. Technology helps to collapse time and space in learning. For example, technology allows learners to engage with their instructors synchronously, in real time and also asynchronously, by enabling sessions to be recorded. Space and distance is no longer an issue provided there is adequate bandwidth, which determines the most appropriate format such text, audio or video. Technology has revolutionised the way learners learn; courses are designed; and ‘lessons’ are delivered, and continues to do so. The learning process can be made vastly more efficient as learners have knowledge at their fingertips, and unfamiliar concepts can be easily searched and an explanation found in seconds. Technology has also enabled learning to be more flexible, as learners can learn anywhere; at any time; and using different formats, e.g. text or audio. From the perspective of the instructors and L&D providers, technology offers these same advantages, plus easy scalability. Administratively, preparatory work can be undertaken more quickly even whilst student numbers grow. Learners from far and new locations can be easily accommodated. In addition, many technologies can be easily scaled to accommodate new functionality and/ or other new technologies. ‘Designing and Developing Digital and Blended Learning Solutions’ (5DBS), has been developed to recognise the growing importance of technology in L&D. This unit contains four learning outcomes and two assessment criteria, which is the same for all other units, besides Learning Outcome 3 which has three assessment criteria. The four learning outcomes in this unit are: • Learning Outcome 1: Understand current digital technologies and their contribution to learning and development solutions; • Learning Outcome 2: Be able to design blended learning solutions that make appropriate use of new technologies alongside more traditional approaches; • Learning Outcome 3: Know about the processes involved in designing and developing digital learning content efficiently and what makes for engaging and effective digital learning content; • Learning Outcome 4: Understand the issues involved in the successful implementation of digital and blended learning solutions. Each learning outcome is an individual chapter and each assessment unit is allocated its own sections within the respective chapters. This first chapter addresses the first learning outcome, which has two assessment criteria: summarise the range of currently available learning technologies; critically assess a learning requirement to determine the contribution that could be made through the use of learning technologies. The introduction to chapter one is in Section 1.0. Chapter 2 discusses the design of blended learning solutions in consideration of how digital learning technologies may support face-to-face and online delivery. Three learning theory sets: behaviourism; cognitivism; constructivism, are introduced, and the implication of each set of theory on instructional design for blended learning discussed. Chapter 3 centres on how relevant digital learning content may be created. This chapter includes a review of the key roles, tools and processes that are involved in developing digital learning content. Finally, Chapter 4 concerns delivery and implementation of digital and blended learning solutions. This chapter surveys the key formats and models used to inform the configuration of virtual learning environment software platforms. In addition, various software technologies which may be important in creating a VLE ecosystem that helps to enhance the learning experience, are outlined. We introduce the notion of personal learning environment (PLE), which has emerged from the democratisation of learning. We also review the roles, tools, standards and processes that L&D practitioners need to consider within a delivery and implementation of digital and blended learning solution.

Professional Practice in Learning and Development: How to Design and Deliver Plans for the Workplace

Introduction The world is changing! It is volatile, uncertain, complex and ambiguous. As cliché as it may sound the evidence of such dynamism in the external environment is growing. Business-as-usual is more of the exception than the norm. Organizational change is the rule; be it to accommodate and adapt to change, or instigate and lead change. A constantly changing environment is a situation that all organizations have to live with. What makes some organizations however, able to thrive better than others? Many scholars and practitioners believe that this is due to the ability to learn. Therefore, this book on developing Learning and Development (L&D) professionals is timely as it explores and discusses trends and practices that impact organizations, the workforce and L&D professionals. Being able to learn and develop effectively is the cornerstone of motivation as it helps to address people’s need to be competent and to be autonomous (Deci & Ryan, 2002; Loon & Casimir, 2008; Ryan & Deci, 2000). L&D stimulates and empowers people to perform. Organizations that are better at learning at all levels; the individual, group and organizational level, will always have a better chance of surviving and performing. Given the new reality of a dynamic external environment and constant change, L&D professionals now play an even more important role in their organizations than ever before. However, L&D professionals themselves are not immune to the turbulent changes as their practices are also impacted. Therefore, the challenges that L&D professionals face are two-pronged. Firstly, in relation to helping and supporting their organization and its workforce in adapting to the change, whilst, secondly developing themselves effectively and efficiently so that they are able to be one-step ahead of the workforce that they are meant to help develop. These challenges are recognised by the CIPD, as they recently launched their new L&D qualification that has served as an inspiration for this book. L&D plays a crucial role at both strategic (e.g. organizational capability) and operational (e.g. delivery of training) levels. L&D professionals have moved from being reactive (e.g. following up action after performance appraisals) to being more proactive (e.g. shaping capability). L&D is increasingly viewed as a driver for organizational performance. The CIPD (2014) suggest that L&D is increasingly expected to not only take more responsibility but also accountability for building both individual and organizational knowledge and capability, and to nurture an organizational culture that prizes learning and development. This book is for L&D professionals. Nonetheless, it is also suited for those studying Human Resource Development HRD at intermediate level. The term ‘Human Resource Development’ (HRD) is more common in academia, and is largely synonymous with L&D (Stewart & Sambrook, 2012) Stewart (1998) defined HRD as ‘the practice of HRD is constituted by the deliberate, purposive and active interventions in the natural learning process. Such interventions can take many forms, most capable of categorising as education or training or development’ (p. 9). In fact, many parts of this book (e.g. Chapters 5 and 7) are appropriate for anyone who is involved in training and development. This may include a variety of individuals within the L&D community, such as line managers, professional trainers, training solutions vendors, instructional designers, external consultants and mentors (Mayo, 2004). The CIPD (2014) goes further as they argue that the role of L&D is broad and plays a significant role in Organizational Development (OD) and Talent Management (TM), as well as in Human Resource Management (HRM) in general. OD, TM, HRM and L&D are symbiotic in enabling the ‘people management function’ to provide organizations with the capabilities that they need.

Transformational Leadership and Job-related Learning

Various empirical studies have examined transformational leadership on the effects of followers and organisations. Transformational leadership has been related to individual attitudes and behaviors such as satisfaction with leaders, organisational citizenship behavior, organisational commitment, motivation, trust in leader, creativity, performance, employee involvement, and empowerment. It has also been linked to such organisational outcomes as innovation, change, productivity, ethical climate, and organisational learning. Organisational learning occurs at three levels: individual, group, and organisational. The focus of the present study is on the individual level—job-related learning. Job-related learning is a measure of individual job behavior pertaining to acquisition of knowledge and skills and enhancement of job performance within the context of the individual’s workplace. It argues that transformational leadership inculcates individuals’ drive to learn. The aim of the study is to examine the relationship between transformational leadership and job-related learning. Transformational leadership is composed of four unique but interrelated facets--idealised influence, individualised consideration, inspirational motivation, and intellectual stimulation. The research results support the hypothesis that transformational leadership is positively related to job-related learning. The implications of the research findings and suggestions for future research are discussed.

“I know, therefore I adapt?” Complexities of individual adaptation to climate-induced forest dieback in Alaska

Individual actions to avoid, benefit from, or cope with climate change impacts partly shape adaptation; much research on adaptation has focused at the systems level, overlooking drivers of individual responses. Theoretical frameworks and empirical studies of environmental behavior identify a complex web of cognitive, affective, and evaluative factors that motivate stewardship. We explore the relationship between knowledge of, and adaptation to, widespread, climate-induced tree mortality to understand the cognitive (i.e., knowledge and learning), affective (i.e., attitudes and place attachment), and evaluative (i.e., use values) factors that influence how individuals respond to climate-change impacts. From 43 semistructured interviews with forest managers and users in a temperate forest, we identified distinct responses to local, climate-induced environmental changes that we then categorized as either behavioral or psychological adaptations. Interviewees developed a depth of knowledge about the dieback through a combination of direct, place-based experiences and indirect, mediated learning through social interactions. Knowing that the dieback was associated with climate change led to different adaptive responses among the interviewees, although knowledge alone did not explain this variation. Forest users reported psychological adaptations to process negative attitudes; these adaptations were spurred by knowledge of the causes, losses of intangible values, and impacts to a species to which they held attachment. Behavioral adaptations exclusive to a high level of knowledge included actions such as using the forests to educate others or changing transportation behaviors to reduce personal energy consumption. Managers integrated awareness of the dieback and its dynamics across spatial scales into current management objectives. Our findings suggest that adaptive management may occur from the bottom up, as individual managers implement new practices in advance of policies. As knowledge of climate-change impacts in local environments increases, resource users may benefit from programs and educational interventions that facilitate coping strategies.

An exploratory study of orthoptic student satisfaction in a blended learning environment

Purpose: To describe orthoptic student satisfaction in a blended learning environment. Methods: Blended learning and teaching approaches that include a mix of sessions with elearning are being used since 2011/2012 involving final year (4th year) students from an orthoptic program. This approach is used in the module of research in orthoptics during the 1 semester. Students experienced different teaching approaches, which include seminars, tutorial group discussions and e-learning activities using the moodle platform. The Constructivist OnLine Learning Environment Survey (COLLES ) was applied at the end of the semester with 24 questions grouped in 6 dimensions with 4 items each: Relevance to professional practice, Reflection, Interactivity, Tutor support, Peer support and Interpretation. A 5-point Likert scale was used to score each individual item of the questionnaire (1 - almost never to 5 – almost always). The sum of items in each dimension ranged between 4 (negative perception) and 20 (positive perception). Results: Twenty-four students replied to the questionnaire. Positive points were related with Relevance (16.13±2.63), Reflection (16.46±2.45), Tutor support (16.29±2.10) and Interpretation (15.38±2.16). The majority of the students (n=18; 75%) think that the on-line learning is relevant to students’ professional practice. Critical reflections about learning contents were frequent (n=19; 79.17%). The tutor was able to stimulate critical thinking (n=21; 87.50%), encouraged students to participate (n=18; 75%) and understood well the student’s contributions (n=15; 62.50%). Less positive points were related with Interactivity (14.13±2.77) and Peer support (13.29±2.60). Response from the colleagues to ideas (n=11; 45.83%) and valorization of individual contributions (n=10; 41.67%) scored lower than other items. Conclusions: The flow back and forth between face-to-face and online learning situations helps the students to make critical reflections. The majority of the students are satisfied with a blended e-learning system environment. However, more work needs to be done to improve interactivity and peer support.

Promoting oral health in adults with learning disability

Introduction: It is complex to define learning disabilities, there is no single universal definition used; there are different interpretations and definitions used for learning disabilities in different countries and communities. Primarily, the term “learning disability” sometimes used as “learning difficulties” is a term widely used in UK. There are various types and degree of severity of learning disabilities depending upon the extent of disorder. Though different definitions used all over the world, its types and classification coupled with their health and oral health needs are discussed in this review. Objectives:  To review the background literature on definitions of learning disabilities and health needs of this population.  To review literature on individual clinical preventive intervention to determine the effectiveness in promoting oral health amongst adults in learning disabilities.  To review literature in relation to community based preventive dental measures.  To determine the interventions in this areas are appropriate to support policy and practice and if these interventions establish good evidence to suggest that the oral health needs of adults with learning disabilities are met or not.  To make recommendations in implementing future preventive oral health interventions for adults with learning disabilities. Methodology: It was develop a comprehensive narrative synthesis of previously published literature from different sources and summarizes the whole research in a particular area identifying gap of knowledge. It provides a broad perspective of a subject and supports continuing education. It also is directed to inform policy and further research. It is a qualitative type of research with a broad question and critical analysis of literature published in books, article and journals. The research question evaluated on PICOS criteria is: Effectiveness of preventive dental interventions in adults with learning disabilities. The research question clearly defines the PICOS i.e. participants, interventions, comparison, outcome and study design. The Cochrane database of systematic reviews (CDSR), Database of Abstracts of Reviews of effects (DARE) through York University and National institute of Health and Clinical Excellence (NICE) was searched to identify need of this review. There was no literature review found on the preventive dental interventions found hence, justifying this review. The guidance used in this review is from York University and methods opted for search of literature is based on the following: Type of participants, interventions, outcome measure, studies and search. The review of literature; author search; systematic and narrative reviews, through the following electronic databases via UFP library services: Pub-Med, Medline, EMBASE, CINHAL, Google scholar; Science Direct; Social and Medicine. A comprehensive search of all available literature from 1990-2015, including systematic reviews, policy documents and some guideline documents was done. Internet resource used to access; Department of Health, World Health Organization, Disability World, Disability Rights Commission, the Stationery office, MENCAP, Australian Learning Disability Association. The literature search was carried out with single word, combined words and phrases, authors' names and the title of literature search. Results: It is primarily looking at the oral health interventions available for adults with learning disabilities in clinical settings and the community measures observed over a period of 25 years 1990-2015. There were 7of the clinical intervention studies and one community based intervention study was added in this review. Conclusion: There is a gap of knowledge identified in not having ample research in the area of preventive dental interventions in adults with learning or intellectual disabilities and there is a need of more research, studies need to be of a better quality and a special consideration is required in the community settings where maintenance of oral hygiene for this vulnerable group of society is hugely dependent on their caregivers. Though, the policy and guideline directs on the preventive dental interventions of adults with LD there still a gap evident in understanding and implication of the guidance in practice by the dental and care support team. Understanding learning disabilities and to identify their behavior, compliance and oral health needs is paramount for all professionals working with or for them at each level.

As dimensões individual e coletiva no ensino exploratório da matemática

Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção do grau de mestre em Ensino do 1.º e 2.º Ciclos do Ensino Básico

'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.

'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.

Programa em numeracia: proposta para a medida Currículo Específico Individual

The numeracy deals with the study of the ability to apply mathematical knowledge on solving everyday life problems (Goos, Geiger, & Dole, 2012; Ponte, 2002; Steen, 2001; Cockcroft, 1982) and appears to us as an essential dimension to programming in special education, for its applicability to the everyday life of individuals. For this, it seemed relevant to perceive essential competences that promote greater autonomy to the students in school-age and with profiles of functionality that express heightened difficulties in the use of skills normally associated with numeracy. In order to implementing it, it was made a content and documentary analysis, on the subject area of Mathematics, of three guiding documents: (a) Curriculum Guidelines – Presschool Education; (b) Mathematics Program – 1st Cycle; (c) Curricular Goals – Presschool and 1st Cycle. The analysis focused in the field of numeracy, i.e. were considered the contents related to the development of competences in the field of Numbers and Operationsà a????dà su????do????ai????àMo????e????àa????dàTi????eàDo????ai????àGeo????et????????àa????dàMeasu????e????e????t.àAs a result of this procedure, it has been elaborated a flexible program and with the capacity of adequacy to the most diverse profiles of functionality, in particular those that depict situations of greater complexity and gravity. The study suggests the possibility that, on the basis of available documents guiding teaching and learning, one can find a flexible and progressive program with an equivalent matrix, available for use and allowing the adoption of a reference and a common language in the context of special education.

Self-regulated learning in computer programming : strategies students adopted during an assignment

The SimProgramming teaching approach has the goal to help students overcome their learning difficulties in the transition from entry-level to advanced computer programming and prepare them for real-world labour environments, adopting learning strategies. It immerses learners in a businesslike learning environment, where students develop a problem-based learning activity with a specific set of tasks, one of which is filling weekly individual forms. We conducted thematic analysis of 401 weekly forms, to identify the students’ strategies for self-regulation of learning during assignment. The students are adopting different strategies in each phase of the approach. The early phases are devoted to organization and planning, later phases focus on applying theoretical knowledge and hands-on programming. Based on the results, we recommend the development of educational practices to help students conduct self-reflection of their performance during tasks.

EXAMINING SECONDARY MATHEMATICS TEACHER CANDIDATES’ LEARNING AND ENACTMENT OF MATHEMATICS TEACHING PRACTICES: A MULTIPLE CASE STUDY

This qualitative case study explored three teacher candidates’ learning and enactment of discourse-focused mathematics teaching practices. Using audio and video recordings of their teaching practice this study aimed to identify the shifts in the way in which the teacher candidates enacted the following discourse practices: elicited and used evidence of student thinking, posed purposeful questions, and facilitated meaningful mathematical discourse. The teacher candidates’ written reflections from their practice-based coursework as well as interviews were examined to see how two mathematics methods courses influenced their learning and enactment of the three discourse focused mathematics teaching practices. These data sources were also used to identify tensions the teacher candidates encountered. All three candidates in the study were able to successfully enact and reflect on these discourse-focused mathematics teaching practices at various time points in their preparation programs. Consistency of use and areas of improvement differed, however, depending on various tensions experienced by each candidate. Access to quality curriculum materials as well as time to formulate and enact thoughtful lesson plans that supported classroom discourse were tensions for these teacher candidates. This study shows that teacher candidates are capable of enacting discourse-focused teaching practices early in their field placements and with the support of practice-based coursework they can analyze and reflect on their practice for improvement. This study also reveals the importance of assisting teacher candidates in accessing rich mathematical tasks and collaborating during lesson planning. More research needs to be explored to identify how specific aspects of the learning cycle impact individual teachers and how this can be used to improve practice-based teacher education courses.