The symbiotic roles of empirical experimentation and thought experimentation in the learning of physics

This study was an attempt to identify the epistemological roots of knowledge when students carry out hands-on experiments in physics. We found that, within the context of designing a solution to a stated problem, subjects constructed and ran thought experiments intertwined within the processes of conducting physical experiments. We show that the process of alternating between these two modes- empirically experimenting and experimenting in thought- leads towards a convergence on scientifically acceptable concepts. We call this process mutual projection. In the process of mutual projection, external representations were generated. Objects in the physical environment were represented in an imaginary world and these representations were associated with processes in the physical world. It is through this coupling that constituents of both the imaginary world and the physical world gain meaning. We further show that the external representations are rooted in sensory interaction and constitute a semi-symbolic pictorial communication system, a sort of primitive 'language', which is developed as the practical work continues. The constituents of this pictorial communication system are used in the thought experiments taking place in association with the empirical experimentation. The results of this study provide a model of physics learning during hands-on experimentation.

Example uses of a learning object

A self study course for learning to program using the C programming language has been developed. A Learning Object approach was used in the design of the course. One of the benefits of the Learning Object approach is that the learning material can be reused for different purposes. 'Me course developed is designed so that learners can choose the pedagogical approach most suited to their personal learning requirements. For all learning approaches a set of common Assessment Learning Objects (ALOs or tests) have been created. The design of formative assessments with ALOs can be carried out by the Instructional Designer grouping ALOs to correspond to a specific assessment intention. The course is non-credit earning, so there is no summative assessment, all assessment is formative. In this paper examples of ALOs and their uses is presented together with their uses as decided by the Instructional Designer and learner. Personalisation of the formative assessment of skills can be decided by the Instructional Designer or the learner using a repository of pre-designed ALOs. The process of combining ALOs can be carried out manually or in a semi-automated way using metadata that describes the ALO and the skill it is designed to assess.

Striving for inclusive design in the built environment: learning strategy adopted by policy implementers at local authorities in England

The research will explore views on inclusive design policy implementation and learning strategy used in practice by Local Authorities’ planning, building control and policy departments in England. It reports emerging research findings. The research aim was developed from an extensive literature review, and informed by a pilot study with relevant Local Authority departments. The pilot study highlighted gaps within the process of policy implementation, a lack of awareness of the process and flaws in the design guidance policy. This has helped inform the development of a robust research design using both a survey and semi-structured interviews. The questionnaire targeted key employees within Local Authorities designed to establish how employees learn about inclusive design policy and to determine their views on current approaches of inclusive design policy implementation adopted by their Local Authorities. The questionnaire produces 117 responses. Interestingly approximately 9 out of 129 Local Authorities approached claimed that they were unable to participate either because an inclusive design policy was not adopted or they were faced with a high workload and thus unable to take part. An emerging finding is a lack of understanding of inclusive design problems, which may lead to problem with inclusive design policy implementation, and thus adversely affect how the built environment can be experienced. There is a strong indication from the survey respondents indicating that they are most likely to learn about inclusive design from policy guides produced by their Local Authorities and from their colleagues.

Assessment for Learning in teaching English to Young Learners: teachers’ understanding, classroom practice and impact on interactions

It has been suggested that Assessment for Learning (AfL) plays a significant role in enhancing teaching and learning in mainstream educational contexts. However, little empirical evidence can support these claims. As AfL has been shown to be enacted predominantly through interactions in primary classes, there is a need to understand if it is appropriate, whether it can be efficiently used in teaching English to Young Learners (TEYL) and how it can facilitate learning in such a context. This emerging research focus gains currency especially in the light of SLA research, which suggests the important role of interactions in foreign language learning. This mixed-method, descriptive and exploratory study aims to investigate how teachers of learners aged 7-11 understand AfL; how they implement it; and the impact that such implementation could have on interactions which occur during lessons. The data were collected through lesson observations, scrutiny of school documents, semi-structured interviews and a focus group interview with teachers. The findings indicate that fitness for purpose guides the implementation of AfL in TEYL classrooms. Significantly, the study has revealed differences in the implementation of AfL between classes of 7-9 and 10-11 year olds within each of the three purposes (setting objectives and expectations; monitoring performance; and checking achievement) identified through the data. Another important finding of this study is the empirical evidence suggesting that the use of AfL could facilitate creating conditions conducive to learning in TEYL classes during collaborative and expert/novice interactions. The findings suggest that teachers’ understanding of AfL is largely aligned with the theoretical frameworks (Black & Wiliam, 2009; Swaffield, 2011) already available. However, they also demonstrate that there are TEYL specific characteristics. This research has important pedagogical implications and indicates a number of areas for further research.

Interactive learning in UK construction practice: examining the role of BIM process standards

From a construction innovation systems perspective, firms acquire knowledge from suppliers, clients, universities and institutional environment. Building information modelling (BIM) involves these firms using new process standards. To understand the implications on interactive learning using BIM process standards, a case study is conducted with the UK operations of a multinational construction firm. Data is drawn from: a) two workshops involving the firm and a wider industry group, b) observations of practice in the BIM core team and in three ongoing projects, c) 12 semi-structured interviews; and d) secondary publications. The firm uses a set of BIM process standards (IFC, PAS 1192, Uniclass, COBie) in its construction activities. It is also involved in a pilot to implement the COBie standard, supported by technical and management standards for BIM, such as Uniclass and PAS1192. Analyses suggest that such BIM process standards unconsciously shapes the firm's internal and external interactive learning processes. Internally standards allow engineers to learn from each through visualising 3D information and talking around designs with operatives to address problems during construction. Externally, the firm participates in trial and pilot projects involving other construction firms, government agencies, universities and suppliers to learn about the standard and access knowledge to solve its specific design problems. Through its BIM manager, the firm provides feedback to standards developers and information technology suppliers. The research contributes by articulating how BIM process standards unconsciously change interactive learning processes in construction practice. Further research could investigate these findings in the wider UK construction innovation system.

Comparing machine learning classifiers in potential distribution modelling

Species` potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species` potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.

Unsupervised learning to cluster the disease stages in parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder (after Alzheimer's disease) and directly affects upto 5 million people worldwide. The stages (Hoehn and Yaar) of disease has been predicted by many methods which will be helpful for the doctors to give the dosage according to it. So these methods were brought up based on the data set which includes about seventy patients at nine clinics in Sweden. The purpose of the work is to analyze unsupervised technique with supervised neural network techniques in order to make sure the collected data sets are reliable to make decisions. The data which is available was preprocessed before calculating the features of it. One of the complex and efficient feature called wavelets has been calculated to present the data set to the network. The dimension of the final feature set has been reduced using principle component analysis. For unsupervised learning k-means gives the closer result around 76% while comparing with supervised techniques. Back propagation and J4 has been used as supervised model to classify the stages of Parkinson's disease where back propagation gives the variance percentage of 76-82%. The results of both these models have been analyzed. This proves that the data which are collected are reliable to predict the disease stages in Parkinson's disease.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.

Estágio Supervisionado na formação de professores em serviço dos anos iniciais do ensino fundamental

This report aims to present the results of research on the possibilities and limits of Supervised Traineeship in a the course of Training of Teachers in Service of the early years of primary education conducted through an agreement between the Federal University of Piauí and the Teresina city council (PI) UFPI/PMT, offers subsidies to teaching practices that address the reality of students who live, work and/or studying in school located in rural areas. The research is based on a theoretical and methodological approach that places the object of study in the critical perspective and using procedures such as: literature (bibliographical research), documentary analysis, semi-structured interview and photographic record. We researched 10 (ten) teachers of the course promoted by UFPI graduating in 2006 and who develop teaching activities in schools located in rural areas in the city of Teresina-PI, 02 (two) teachers who are trainee supervisor of UFPI and the coordinator of the course. The analysis evidenced that the object, historically, educational policies have changed, but did not break the traditional patterns of teacher education, enough, especially in the curricular proposals in light of the changes required by the information and global society. We also note that in the supervised training, the process of critical reflection on teaching practice was made possible, in part, and raised the reconstruction of specific knowledge to make teaching in order to conduct the teaching-learning process articulating different realities of primary teaching and facing situations due to conservative practices in relation to education in rural areas. It contributed, therefore, this curricular component to resize, in part, the pedagogical action of the teachers graduated. The study also drives the research toward to similar studies in the perspective of overcoming the current model of teacher training that does not correspond to the demands of society in change and to the crisis of capital, with immeasurable consequences in the workplace. The contemporary claims for a University to form professionals capable of directing the destiny of society, where teacher training is a social and political demands on which is co-responsible

O estágio supervisionado como contexto de formação docente específica para educação infantil: o que dizem os formandos sobre suas aprendizagens?

Kindergarten teachers training gains the spotlight with the passing of Law number 9.394/96 (Guidelines and Basis Law) that defines this segment as the initial step of basic education, with pedagogical function. In this spectrum, the discussion about teacher training unravels to ensure social quality to education as well as the teacher s specificities towards child singularities. Adding to that, the growing propagation of Pedagogy in an undergraduate level, given that such course has been continually transformed by the National Curriculum Guidelines for Pedagogy (2006), highlighting the addition of curriculum components that are specific to upbringing. The complex debate circa kindergarten teachers training has advancements and hardships that need to be unveiled in order to improve both formation and social quality of education in the 0- 5 years old range. This investigation inserts itself in said context and aims to analyze which knowledge, specific to kindergarten teaching are constructed, according to undergraduate trainees, in Pedagogy s supervised internship. The study was conducted alongside the discipline: Supervised Internship in Child Education ministered by the Advanced Campus of Rio Grande do Norte s State University s in the municipality of Patu-RN (CAP-UERN-Patu) and was conducted through 2012 by accompanying four undergraduate interns. We first assumed that the development of teaching knowledge is a complex process of appropriation of cultural-social practices and is symbolically mediated by interactions that occur in the formation context, and the supervised internship can be understood as a space for the articulation and enlargement of theoretical and practical knowledge, directly related to the specificities of child education. The theoretical-methodological foundation was based upon the historiccultural approach of L. S. Vygotsky and M. Bakhtin s dialogism on human sciences research, as well as his postulates on learning and developmental processes, conceived as both essentially social and discursive. The investigation approached the principles of the qualitative perspective and to the construction and analysis of data, involved documental analysis and, specially, semi-structured interviews, both individual and collective, whose fundamental premise was the production-comprehension of meanings in a dialogical perspective. The participants texts/speeches produced a synthesis that points to the occurrence, within the supervised internship at CAP/UERN, of internalization/appropriation processes and, as such, of formulation of meanings that are pertinent to child education: child, childhood, kindergarten and teacher signification and this stage s specific teaching knowledge. It stood out that the internship, alongside other curriculum components, is, in fact, one of the primeval formation environment for the teachers, in which the interns interact with their colleagues, supervisor professor, collaborator professor, and of course, the children to construct their erudition. Such interactions allow the undergraduate interns to develop attitudes and procedures to reflect on what they know, what they ve done and what they can achieve. We have concluded that the undergraduate internship can constitute itself as an articulatorconsolidator environment in the future teacher s formation process and, since well oriented, can provide the effective initiation, not only to the practice, but to the praxis as a movement of non dissociability between theory and practice

Aquatic weed automatic classification using machine learning techniques

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

Efficient supervised optimum-path forest classification for large datasets

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

Improving the accuracy of the optimum-path forest supervised classifier for large datasets

In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OPF while still gaining in classification time, at the expense of a slight increase in training time. © 2010 Springer-Verlag.

Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.