Search results for: normalized Hebbian learning

Authors: Muhammad Karim Ahmadzai

Abstract:

Floods are natural phenomena and are an integral part of the water cycle. The majority of them are the result of climatic conditions, but are also affected by the geology and geomorphology of the area, topography and hydrology, the water permeability of the soil and the vegetation cover, as well as by all kinds of human activities and structures. However, from the moment that human lives are at risk and significant economic impact is recorded, this natural phenomenon becomes a natural disaster. Flood management is now a key issue at regional and local levels around the world, affecting human lives and activities. The majority of floods are unlikely to be fully predicted, but it is feasible to reduce their risks through appropriate management plans and constructions. The aim of this Case Study is to identify, and map areas of flood risk in the Khinch District of Panjshir Province, Afghanistan specifically in the area of Peshghore, causing numerous damages. The main purpose of this study is to evaluate the contribution of remote sensing technology and Geographic Information Systems (GIS) in assessing the susceptibility of this region to flood events. Panjsher is facing Seasonal floods and human interventions on streams caused floods. The beds of which have been trampled to build houses and hotels or have been converted into roads, are causing flooding after every heavy rainfall. The streams crossing settlements and areas with high touristic development have been intensively modified by humans, as the pressure for real estate development land is growing. In particular, several areas in Khinch are facing a high risk of extensive flood occurrence. This study concentrates on the construction of a flood susceptibility map, of the study area, by combining vulnerability elements, using the Analytical Hierarchy Process/ AHP. The Analytic Hierarchy Process, normally called AHP, is a powerful yet simple method for making decisions. It is commonly used for project prioritization and selection. AHP lets you capture your strategic goals as a set of weighted criteria that you then use to score projects. This method is used to provide weights for each criterion which Contributes to the Flood Event. After processing of a digital elevation model (DEM), important secondary data were extracted, such as the slope map, the flow direction and the flow accumulation. Together with additional thematic information (Landuse and Landcover, topographic wetness index, precipitation, Normalized Difference Vegetation Index, Elevation, River Density, Distance from River, Distance to Road, Slope), these led to the final Flood Risk Map. Finally, according to this map, the Priority Protection Areas and Villages and the structural and nonstructural measures were demonstrated to Minimize the Impacts of Floods on residential and Agricultural areas.

Keywords: flood hazard, flood risk map, flood mitigation measures, AHP analysis

Procedia PDF Downloads 119

Authors: Addin Osman, Anwar Ali Yahya, Mohammed Basit Kamal

Abstract:

Datasets or collections are becoming important assets by themselves and now they can be accepted as a primary intellectual output of a research. The quality and usage of the datasets depend mainly on the context under which they have been collected, processed, analyzed, validated, and interpreted. This paper aims to present a collection of program educational objectives mapped to student’s outcomes collected from self-study reports prepared by 32 engineering programs accredited by ABET. The manual mapping (classification) of this data is a notoriously tedious, time consuming process. In addition, it requires experts in the area, which are mostly not available. It has been shown the operational settings under which the collection has been produced. The collection has been cleansed, preprocessed, some features have been selected and preliminary exploratory data analysis has been performed so as to illustrate the properties and usefulness of the collection. At the end, the collection has been benchmarked using nine of the most widely used supervised multiclass classification techniques (Binary Relevance, Label Powerset, Classifier Chains, Pruned Sets, Random k-label sets, Ensemble of Classifier Chains, Ensemble of Pruned Sets, Multi-Label k-Nearest Neighbors and Back-Propagation Multi-Label Learning). The techniques have been compared to each other using five well-known measurements (Accuracy, Hamming Loss, Micro-F, Macro-F, and Macro-F). The Ensemble of Classifier Chains and Ensemble of Pruned Sets have achieved encouraging performance compared to other experimented multi-label classification methods. The Classifier Chains method has shown the worst performance. To recap, the benchmark has achieved promising results by utilizing preliminary exploratory data analysis performed on the collection, proposing new trends for research and providing a baseline for future studies.

Keywords: ABET, accreditation, benchmark collection, machine learning, program educational objectives, student outcomes, supervised multi-class classification, text mining

Procedia PDF Downloads 173

Authors: Madrine King'endo

Abstract:

This study dealt with the influence of spelling errors on English language performance among learners with dysgraphia in public primary schools in West Embu, Embu County, Kenya. The study purposed to investigate the influence of spelling errors on the English language performance among the class three pupils with dysgraphia in public primary schools. The objectives of the study were to identify the spelling errors that learners with dysgraphia make when writing English words and classify the spelling errors they make. Further, the study will establish how the spelling errors affect the performance of the language among the study participants, and suggest the remediation strategies that teachers could use to address the errors. The study could provide the stakeholders with relevant information in writing skills that could help in developing a responsive curriculum to accommodate the teaching and learning needs of learners with dysgraphia, and probably ensure training of teachers in teacher training colleges is tailored within the writing needs of the pupils with dysgraphia. The study was carried out in Embu county because the researcher did not find any study in related literature review concerning the influence of spelling errors on English language performance among learners with dysgraphia in public primary schools done in the area. Moreover, besides being relatively populated enough for a sample population of the study, the area was fairly cosmopolitan to allow a generalization of the study findings. The study assumed the sampled schools will had class three pupils with dysgraphia who exhibited written spelling errors. The study was guided by two spelling approaches: the connectionist stimulation of spelling process and orthographic autonomy hypothesis with a view to explain how participants with learning disabilities spell written words. Data were collected through interviews, pupils’ exercise books, and progress records, and a spelling test made by the researcher based on the spelling scope set for class three pupils by the ministry of education in the primary education syllabus. The study relied on random sampling techniques in identifying general and specific participants. Since the study used children in schools as participants, voluntary consent was sought from themselves, their teachers and the school head teachers who were their caretakers in a school setting.

Keywords: dysgraphia, writing, language, performance

Procedia PDF Downloads 154

Authors: Dong Seop Lee, Byung Sik Kim

Abstract:

In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

Procedia PDF Downloads 129

Authors: Ahmed Makhoukh

Abstract:

Quality assurance (QA) has recently become a common practice, which is endorsed by most Higher Education (HE) institutions worldwide, due to the pressure of internal and external forces. One of the aims of this quality movement is to make the contribution of university education to socio-economic development highly significant. This entails that graduates are currently required have a high-quality profile, i.e., to be competent and master the 21st-century skills needed in the labor market. This wave of change, mostly imposed by globalization, has the effect that university education should be learner-centered in order to satisfy the different needs of students and meet the expectations of other stakeholders. Such a shift of focus on the student learning outcomes has led HE institutions to reconsider their strategic planning, their mission, the curriculum, the pedagogical competence of the academic staff, among other elements. To ensure that the overall institutional performance is on the right way, a QA system should be established to assume this task of checking regularly the extent to which the set of standards of evaluation are strictly respected as expected. This operation of QA has the advantage of proving the accountability of the institution, gaining the trust of the public with transparency and enjoying an international recognition. This is the case of Doha Institute (DI) for Graduate Studies, in Qatar, the object of the present study. The significance of this contribution is to show that the conception of quality has changed in this digital age, and the need to integrate a department responsible for QA in every HE institution to ensure educational quality, enhance learners and achieve academic leadership. Thus, to undertake the issue of QA in DI for Graduate Studies, an elite university (in the academic sense) that focuses on a small and selected number of students, a qualitative method will be adopted in the description and analysis of the data (document analysis). In an attempt to investigate the extent to which QA is achieved in Doha Institute for Graduate Studies, three broad indicators will be evaluated (input, process and learning outcomes). This investigation will be carried out in line with the UK Quality Code for Higher Education represented by Quality Assurance Agency (QAA).

Keywords: accreditation, higher education, quality, quality assurance, standards

Procedia PDF Downloads 147

Authors: Rossella Resi

Abstract:

This paper describes a project carried out in an Italian school with English learning pupils combining three didactic tools which are attested to be relevant for the success of young learner’s language curriculum: the use of technology, the intralingual and interlingual mediation (according to CEFR) and the cultural dimension. Aim of this project was to test a technological hands-on translation activity like subtitling in a formal teaching context and to exploit its potential as motivational tool for developing listening and writing, translation and cross-cultural skills among language learners. The activities proposed involved the use of professional subtitling software called Aegisub and culture-specific films. The workshop was optional so motivation was entirely based on the pleasure of engaging in the use of a realistic subtitling program and on the challenge of meeting the constraints that a real life/work situation might involve. Twelve pupils in the age between 16 and 18 have attended the afternoon workshop. The workshop was organized in three parts: (i) An introduction where the learners were opened up to the concept and constraints of subtitling and provided with few basic rules on spotting and segmentation. During this session learners had also the time to familiarize with the main software features. (ii) The second part involved three subtitling activities in plenum or in groups. In the first activity the learners experienced the technical dimensions of subtitling. They were provided with a short video segment together with its transcription to be segmented and time-spotted. The second activity involved also oral comprehension. Learners had to understand and transcribe a video segment before subtitling it. The third activity embedded a translation activity of a provided transcription including segmentation and spotting of subtitles. (iii) The workshop ended with a small final project. At this point learners were able to master a short subtitling assignment (transcription, translation, segmenting and spotting) on their own with a similar video interview. The results of these assignments were above expectations since the learners were highly motivated by the authentic and original nature of the assignment. The subtitled videos were evaluated and watched in the regular classroom together with other students who did not take part to the workshop.

Keywords: ICT, L2, language learning, language mediation, subtitling

Procedia PDF Downloads 416

Authors: Derek O Reilly, Piotr Milczarski, Shane Dowdall, Artur Hłobaż, Krzysztof Podlaski, Hiram Bollaert

Abstract:

Learning through creation of contextual games is a very promising way/tool for interdisciplinary and international group projects. During 2013 and 2014 we took part and organized two intensive students projects in different conditions. The projects enrolled 68 students and 12 mentors from 5 countries. In the paper we want to share our experience how to strengthen the chances to succeed in short (12-15 days long) student projects. In our case almost all teams prepared working prototype and the results were highly appreciated by external experts.

Keywords: contextual games, mobile games, GGULIVRR, walkabout, Erasmus intensive programme

Procedia PDF Downloads 502

Authors: Ahlem Bouziri, Chiraz Latiri, Eric Gaussier

Abstract:

The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to extract dependencies between terms, namely a generic basis of association rules between terms. The key feature of our approach is a better trade off between the size of the mining result and the conveyed knowledge. Thus, face to the huge number of derived association rules and in order to select the optimal combination of query terms from the generic basis, we propose to model the problem as a classification problem and solve it using a supervised learning algorithm such as SVM or k-means. For this purpose, we first generate a training set using a genetic algorithm based approach that explores the association rules space in order to find an optimal set of expansion terms, improving the MAP of the search results. The experiments were performed on SDA 95 collection, a data collection for information retrieval. It was found that the results were better in both terms of MAP and NDCG. The main observation is that the hybridization of text mining techniques and query expansion in an intelligent way allows us to incorporate the good features of all of them. As this is a preliminary attempt in this direction, there is a large scope for enhancing the proposed method.

Keywords: supervised leaning, classification, query expansion, association rules

Procedia PDF Downloads 325

Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 167

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 159

Authors: Victor Abu

Abstract:

Background: Social justice ideals are considered as the foundation of nursing practice. These ideals are not always clearly integrated into nursing professional standards or curricula. This hinders concerted global nursing agendas for becoming aware of social injustice or engaging in action for social justice to improve the health of individuals and groups. Aim and objectives: The aim was to create an educational framework for empowering nursing students for social justice awareness and action. This purpose was attained by understanding the meaning of social justice, the effect of social injustice, the visibility of social justice learning, and ways of integrating social justice in nursing education and practice. Methods: Critical interpretive methodologies and constructivist grounded theory research designs guided the processes of recruiting nursing students (n = 11) and nurse educators (n = 11) at a London nursing university to participate in interviews and focus groups, which were analysed by coding systems. Findings: Firstly, social justice was described as ethical practices that enable individuals and groups to have good access to health resources. Secondly, social injustice was understood as unfair practices that caused minimal access to resources, social deprivation, and poor health. Thirdly, social justice learning was considered to be invisible in nursing education due to a lack of explicit modules, educator knowledge, and organisational support. Lastly, explicit modules, educating educators, and attracting leaders’ support were suggested as approaches for the visible integration of social justice in nursing education and practice. Discussion: This research proposes approaches for nursing awareness and action for the development of critical active nurse-learner, critical conscious nurse-educator, and servant nurse leader. The framework on Awareness for Social Justice Action (ASJA) created in this research is an approach for empowering nursing students for social justice practices. Conclusion: This research contributes to and advocates for greater nursing scholarship to raise the spotlight on social justice in the profession.

Keywords: social justice, nursing practice, nursing education, nursing curriculum, social justice awareness, social justice action, constructivist grounded theory

Procedia PDF Downloads 58

Authors: Tingneyuc Sekac, Sujoy Kumar Jana, Indrajit Pal, Dilip Kumar Pal

Abstract:

Tectonism induced Tsunami, landslide, ground shaking leading to liquefaction, infrastructure collapse, conflagration are the common earthquake hazards that are experienced worldwide. Apart from human casualty, the damage to built-up infrastructures like roads, bridges, buildings and other properties are the collateral episodes. The appropriate planning must precede with a view to safeguarding people’s welfare, infrastructures and other properties at a site based on proper evaluation and assessments of the potential level of earthquake hazard. The information or output results can be used as a tool that can assist in minimizing risk from earthquakes and also can foster appropriate construction design and formulation of building codes at a particular site. Different disciplines adopt different approaches in assessing and monitoring earthquake hazard throughout the world. For the present study, GIS and Remote Sensing potentials were utilized to evaluate and assess earthquake hazards of the study region. Subsurface geology and geomorphology were the common features or factors that were assessed and integrated within GIS environment coupling with seismicity data layers like; Peak Ground Acceleration (PGA), historical earthquake magnitude and earthquake depth to evaluate and prepare liquefaction potential zones (LPZ) culminating in earthquake hazard zonation of our study sites. The liquefaction can eventuate in the aftermath of severe ground shaking with amenable site soil condition, geology and geomorphology. The latter site conditions or the wave propagation media were assessed to identify the potential zones. The precept has been that during any earthquake event the seismic wave is generated and propagates from earthquake focus to the surface. As it propagates, it passes through certain geological or geomorphological and specific soil features, where these features according to their strength/stiffness/moisture content, aggravates or attenuates the strength of wave propagation to the surface. Accordingly, the resulting intensity of shaking may or may not culminate in the collapse of built-up infrastructures. For the case of earthquake hazard zonation, the overall assessment was carried out through integrating seismicity data layers with LPZ. Multi-criteria Evaluation (MCE) with Saaty’s Analytical Hierarchy Process (AHP) was adopted for this study. It is a GIS technology that involves integration of several factors (thematic layers) that can have a potential contribution to liquefaction triggered by earthquake hazard. The factors are to be weighted and ranked in the order of their contribution to earthquake induced liquefaction. The weightage and ranking assigned to each factor are to be normalized with AHP technique. The spatial analysis tools i.e., Raster calculator, reclassify, overlay analysis in ArcGIS 10 software were mainly employed in the study. The final output of LPZ and Earthquake hazard zones were reclassified to ‘Very high’, ‘High’, ‘Moderate’, ‘Low’ and ‘Very Low’ to indicate levels of hazard within a study region.

Keywords: hazard micro-zonation, liquefaction, multi criteria evaluation, tectonism

Procedia PDF Downloads 266

Authors: Sachin Jain, Daniel Perez-Astudillo, Dunia A. Bachour, Antonio P. Sanfilippo

Abstract:

The future of solar energy in Qatar is evolving steadily. Hence, high-quality spatial solar radiation data is of the uttermost requirement for any planning and commissioning of solar technology. Generally, two types of solar radiation data are available: satellite data and ground observations. Satellite solar radiation data is developed by the physical and statistical model. Ground data is collected by solar radiation measurement stations. The ground data is of high quality. However, they are limited to distributed point locations with the high cost of installation and maintenance for the ground stations. On the other hand, satellite solar radiation data is continuous and available throughout geographical locations, but they are relatively less accurate than ground data. To utilize the advantage of both data, a product has been developed here which provides spatial continuity and higher accuracy than any of the data alone. The popular satellite databases: National Solar radiation Data Base, NSRDB (PSM V3 model, spatial resolution: 4 km) is chosen here for merging with ground-measured solar radiation measurement in Qatar. The spatial distribution of ground solar radiation measurement stations is comprehensive in Qatar, with a network of 13 ground stations. The monthly average of the daily total Global Horizontal Irradiation (GHI) component from ground and satellite data is used for error analysis. The normalized root means square error (NRMSE) values of 3.31%, 6.53%, and 6.63% for October, November, and December 2019 were observed respectively when comparing in-situ and NSRDB data. The method is based on the Empirical Bayesian Kriging Regression Prediction model available in ArcGIS, ESRI. The workflow of the algorithm is based on the combination of regression and kriging methods. A regression model (OLS, ordinary least square) is fitted between the ground and NSBRD data points. A semi-variogram is fitted into the experimental semi-variogram obtained from the residuals. The kriging residuals obtained after fitting the semi-variogram model were added to NSRBD data predicted values obtained from the regression model to obtain the final predicted values. The NRMSE values obtained after merging are respectively 1.84%, 1.28%, and 1.81% for October, November, and December 2019. One more explanatory variable, that is the ground elevation, has been incorporated in the regression and kriging methods to reduce the error and to provide higher spatial resolution (30 m). The final GHI maps have been created after merging, and NRMSE values of 1.24%, 1.28%, and 1.28% have been observed for October, November, and December 2019, respectively. The proposed merging method has proven as a highly accurate method. An additional method is also proposed here to generate calibrated maps by using regression and kriging model and further to use the calibrated model to generate solar radiation maps from the explanatory variable only when not enough historical ground data is available for long-term analysis. The NRMSE values obtained after the comparison of the calibrated maps with ground data are 5.60% and 5.31% for November and December 2019 month respectively.

Keywords: global horizontal irradiation, GIS, empirical bayesian kriging regression prediction, NSRDB

Procedia PDF Downloads 89

Authors: C. Fortin, D. Tokmeninova, O. Ushakova

Abstract:

Among 500 Russian universities Skolkovo Institute of Science and Technology (Skoltech) is one of the youngest (established in 2011), quite small and vastly international, comprising 20 percent of international students and 70 percent of faculty with significant academic experience at top-100 universities (QS, THE). The institute has emerged from close collaboration with MIT and leading Russian universities. Skoltech is an entirely English speaking environment. Skoltech curriculum plans of ten Master programs are based on the CDIO learning outcomes model. However, despite the Institute’s unique focus on industrial innovations and startups, one of the main challenges has become an evident large proportion of nearly half of MSc graduates entering PhD programs at Skoltech or other universities rather than industry or entrepreneurship. In order to increase the share of students joining the industrial sector after graduation, Skoltech started implementing a number of unique practices with a focus on employers’ expectations incorporated into the curriculum redesign. In this sense, extended knowledge exchange with industrial partners via collaboration in learning activities, industrial projects and assessments became essential for students’ headway into industrial and entrepreneurship pathways. Current academic curriculum includes the following types of components based on extended knowledge exchange with industrial partners: innovation workshop, industrial immersion, special industrial tracks, MSc defenses. Innovation workshop is a 4 week full time diving into the Skoltech vibrant ecosystem designed to foster innovators, focuses on teamwork, group projects, and sparks entrepreneurial instincts from the very first days of study. From 2019 the number of mentors from industry and startups significantly increased to guide students across these sectors’ demands. Industrial immersion is an exclusive part of Skoltech curriculum where students after the first year of study spend 8 weeks in an industrial company carrying out an individual or team project and are guided jointly by both Skoltech and company supervisors. The aim of the industrial immersion is to familiarize students with relevant needs of Russian industry and to prepare graduates for job placement. During the immersion a company plays the role of a challenge provider for students. Skoltech has started a special industrial track comprising deep collaboration with IPG Photonics – a leading R&D company and manufacturer of high-performance fiber lasers and amplifiers for diverse applications. The track is aimed to train a new cohort of engineers and includes a variety of activities for students within the “Photonics” MSc program. It is expected to be a successful story and used as an example for similar initiatives with other Russian high-tech companies. One of the pathways of extended knowledge exchange with industrial partners is an active involvement of potential employers in MSc Defense Committees to review and assess MSc thesis projects and to participate in defense procedures. The paper will evaluate the effect and results of the above undertaken measures.

Keywords: Curriculum redesign, knowledge exchange model, learning outcomes framework, stakeholder engagement

Procedia PDF Downloads 81

Authors: Anwar Beg, Sireetorn Kuharat, Rashid Mehmood, Rabil Tabassum, Meisam Babaie

Abstract:

Nano-polymeric solar paints and sol-gels have emerged as a major new development in solar cell/collector coatings offering significant improvements in durability, anti-corrosion and thermal efficiency. They also exhibit substantial viscosity variation with temperature which can be exploited in solar collector designs. Modern manufacturing processes for such nano-rheological materials frequently employ stagnation flow dynamics under high temperature which invokes radiative heat transfer. Motivated by elaborating in further detail the nanoscale heat, mass and momentum characteristics of such sol gels, the present article presents a mathematical and computational study of the steady, two-dimensional, non-aligned thermo-fluid boundary layer transport of copper metal-doped water-based nano-polymeric sol gels under radiative heat flux. To simulate real nano-polymer boundary interface dynamics, thermal slip is analysed at the wall. A temperature-dependent viscosity is also considered. The Tiwari-Das nanofluid model is deployed which features a volume fraction for the nanoparticle concentration. This approach also features a Maxwell-Garnet model for the nanofluid thermal conductivity. The conservation equations for mass, normal and tangential momentum and energy (heat) are normalized via appropriate transformations to generate a multi-degree, ordinary differential, non-linear, coupled boundary value problem. Numerical solutions are obtained via the stable, efficient Runge-Kutta-Fehlberg scheme with shooting quadrature in MATLAB symbolic software. Validation of solutions is achieved with a Variational Iterative Method (VIM) utilizing Langrangian multipliers. The impact of key emerging dimensionless parameters i.e. obliqueness parameter, radiation-conduction Rosseland number (Rd), thermal slip parameter (α), viscosity parameter (m), nanoparticles volume fraction (ϕ) on non-dimensional normal and tangential velocity components, temperature, wall shear stress, local heat flux and streamline distributions is visualized graphically. Shear stress and temperature are boosted with increasing radiative effect whereas local heat flux is reduced. Increasing wall thermal slip parameter depletes temperatures. With greater volume fraction of copper nanoparticles temperature and thermal boundary layer thickness is elevated. Streamlines are found to be skewed markedly towards the left with positive obliqueness parameter.

Keywords: non-orthogonal stagnation-point heat transfer, solar nano-polymer coating, MATLAB numerical quadrature, Variational Iterative Method (VIM)

Procedia PDF Downloads 135

Authors: C. E. Laburu, M. A. Barros, A. F. Zompero, O. H. M. Silva

Abstract:

The work presents a teaching strategy that employs syntagmatic and paradigmatic linguistic relations in order to monitor the understanding of physics students’ concepts. Syntagmatic and paradigmatic relations are theoretical elements of semiotics studies and our research circumstances and justified them within the research program of multi-modal representations. Among the multi-modal representations to learning scientific knowledge, the scope of action of syntagmatic and paradigmatic relations belongs to the discursive writing form. The use of such relations has the purpose to seek innovate didactic work with discourse representation in the write form before translate to another different representational form. The research was conducted with a sample of first year high school students. The students were asked to produce syntagmatic and paradigmatic of Newton’ first law statement. This statement was delivered in paper for each student that should individually write the relations. The student’s records were collected for analysis. It was possible observed in one student used here as example that their monemes replaced and rearrangements produced by, respectively, syntagmatic and paradigmatic relations, kept the original meaning of the law. In paradigmatic production he specified relevant significant units of the linguistic signs, the monemas, which constitute the first articulation and each word substituted kept equivalence to the original meaning of original monema. Also, it was noted a number of diverse and many monemas were chosen, with balanced combination of grammatical (grammatical monema is what changes the meaning of a word, in certain positions of the syntagma, along with a relatively small number of other monemes. It is the smallest linguistic unit that has grammatical meaning) and lexical (lexical monema is what belongs to unlimited inventories; is the monema endowed with lexical meaning) monemas. In syntagmatic production, monemas ordinations were syntactically coherent, being linked with semantic conservation and preserved number. In general, the results showed that the written representation mode based on linguistic relations paradigmatic and syntagmatic qualifies itself to be used in the classroom as a potential identifier and accompanist of meanings acquired from students in the process of scientific inquiry.

Keywords: semiotics, language, high school, physics teaching

Procedia PDF Downloads 131

Authors: Abeer Karaali

Abstract:

This paper will seek to clarify important key terms such as home schooling and home education as well as the legalities attached to such terms. It will reflect on the recent proposed changes to terminology in NSW, Australia. The various pedagogical approaches to home education will be explored including their prominence in the Australian context. There is a strong focus on literature from Australia. The historical background of home education in Australia will be explained as well as the difference between distance education and home education. The statistics related to home education in Australia will be explored in the scope and compared to the US. The future of home education in Australia will be discussed.

Keywords: alternative education, e-learning, home education, home schooling, online resources, technology

Procedia PDF Downloads 406

Authors: Arlene Caney, Linda Fellag

Abstract:

To counter the adverse effect of Covid-19 on college student success, two faculty members at a US community college have used web-based assessment data to improve curricula and, thus, student outcomes. This case study exemplifies how “closing the loop” by analyzing outcome assessments in real time can improve student learning for academically underprepared students struggling during the pandemic. The purpose of the study was to develop ways to mitigate the negative impact of Covid-19 on student success of underprepared college students. Using the Assessment, Evaluation, Feedback and Intervention System (AEFIS) and other assessment tools provided by the college’s Office of Institutional Research, an English professor and a Music professor collected data in skill areas related to their curricula over four semesters, gaining insight into specific course sections and learners’ performance across different Covid-driven course formats—face-to-face, hybrid, synchronous, and asynchronous. Real-time data collection allowed faculty to shorten and close the assessment loop, and prompted faculty to enhance their curricula with engaging material, student-centered activities, and a variety of tech tools. Frequent communication, individualized study, constructive criticism, and encouragement were among other measures taken to enhance teaching and learning. As a result, even while student success rates were declining college-wide, student outcomes in these faculty members’ asynchronous and synchronous online classes improved or remained comparable to student outcomes in hybrid and face-to-face sections. These practices have demonstrated that even high-risk students who enter college with remedial level language and mathematics skills, interrupted education, work and family responsibilities, and language and cultural diversity can maintain positive outcomes in college across semesters, even during the pandemic.

Keywords: AEFIS, assessment, distance education, institutional research center

Procedia PDF Downloads 87

Authors: Rabi Abdullahi Danjuma, Fatima Binta Attahir

Abstract:

Classroom discourse is important, and it is worth examining what the phenomena is and how it helps both the teacher and students in a classroom situation. This paper looks at the classroom as a traditional social setting which has its own norms and values. The paper also explains what discourse is, as extended communication in speech or writing often interactively dealing with some particular topics. It also discusses classroom discourse as the language which teachers and students use to communicate with each other in a classroom situation. The paper also looks at some strategies for effective classroom discourse. Finally, implications and recommendations were drawn.

Keywords: classroom, discourse, learning, student, strategies, communication

Procedia PDF Downloads 607

Authors: Selim M. Khan

Abstract:

Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

Procedia PDF Downloads 96

Authors: Cristina Maria Mendoza Sanchez, Maria Angeles Navarro Perán

Abstract:

Introduction: Nursing as a profession, from its initial formation and after its development in practice, has been built and identified mainly from its technical competence and professionalization within the positivist approach of the XIX century that provides a conception of the disease built on the basis of to the biomedical paradigm, where the care provided is more focused on the physiological processes and the disease than on the suffering person understood as a whole. The main issue that is in need of study here is a review of the nursing profession's history to get to know how the nursing profession was before the XIX century. It is unclear if there were organizations or people with knowledge about looking after others or if many people survived by chance. The holistic care, in which the appearance of the disease directly affects all its dimensions: physical, emotional, cognitive, social and spiritual. It is not a concept from the 21st century. It is common practice, most probably since established life in this world, with the final purpose of covering all these perspectives through quality care. Objective: In this paper, we describe and analyze the history of education in nursing learning in terms of reviewing and analysing theoretical foundations of clinical teaching and learning in nursing, with the final purpose of determining and describing the development of the nursing profession along the history. Method: We have done a descriptive systematic review study, doing a systematically searched of manuscripts and articles in the following health science databases: Pubmed, Scopus, Web of Science, Temperamentvm and CINAHL. The selection of articles has been made according to PRISMA criteria, doing a critical reading of the full text using the CASPe method. A compliment to this, we have read a range of historical and contemporary sources to support the review, such as manuals of Florence Nightingale and John of God as primary manuscripts to establish the origin of modern nursing and her professionalization. We have considered and applied ethical considerations of data processing. Results: After applying inclusion and exclusion criteria in our search, in Pubmed, Scopus, Web of Science, Temperamentvm and CINAHL, we have obtained 51 research articles. We have analyzed them in such a way that we have distinguished them by year of publication and the type of study. With the articles obtained, we can see the importance of our background as a profession before modern times in public health and as a review of our past to face challenges in the near future. Discussion: The important influence of key figures other than Nightingale has been overlooked and it emerges that nursing management and development of the professional body has a longer and more complex history than is generally accepted. Conclusions: There is a paucity of studies on the subject of the review to be able to extract very precise evidence and recommendations about nursing before modern times. But even so, as more representative data, an increase in research about nursing history has been observed. In light of the aspects analyzed, the need for new research in the history of nursing emerges from this perspective; in order to germinate studies of the historical construction of care before the XIX century and theories created then. We can assure that pieces of knowledge and ways of care were taught before the XIX century, but they were not called theories, as these concepts were created in modern times.

Keywords: nursing history, nursing theory, Saint John of God, Florence Nightingale, learning, nursing education

Procedia PDF Downloads 113

Authors: Lou J. Pino, Mark Campbell, Matthew J. Kennedy, Ashleigh C. Kennedy

Abstract:

A concussion is complex and nuanced, with cognitive rest being a key component of recovery. Cognitive overexertion during rehabilitation from a concussion is associated with delayed recovery. However, daily living imposes cognitive demands that may be unavoidable and difficult to quantify. Therefore, a portable tool capable of alerting patients before cognitive overexertion occurs could allow patients to maintain their quality of life while preventing symptoms and recovery setbacks. EEG allows for a sensitive measure of cognitive exertion. Clinical 32-lead EEG headsets are not practical for day-to-day concussion rehabilitation management. However, there are now commercially available and affordable portable EEG headsets. Thus, these headsets can potentially be used to continuously monitor cognitive exertion during mental tasks to alert the wearer of overexertion, with the aim of preventing the occurrence of symptoms to speed recovery times. The objective of this study was to test an algorithm for predicting cognitive exertion from EEG data collected from a portable headset. EEG data were acquired from 10 participants (5 males, 5 females). Each participant wore a portable 4 channel EEG headband while completing 10 tasks: rest (eyes closed), rest (eyes open), three levels of the increasing difficulty of logic puzzles, three levels of increasing difficulty in multiplication questions, rest (eyes open), and rest (eyes closed). After each task, the participant was asked to report their perceived level of cognitive exertion using the NASA Task Load Index (TLX). Each participant then completed a second session on a different day. A customized machine learning model was created using data from the first session. The performance of each model was then tested using data from the second session. The mean correlation coefficient between TLX scores and predicted cognitive exertion was 0.75 ± 0.16. The results support the efficacy of the algorithm for predicting cognitive exertion. This demonstrates that the algorithms developed in this study used with portable EEG devices have the potential to aid in the concussion recovery process by monitoring and warning patients of cognitive overexertion. Preventing cognitive overexertion during recovery may reduce the number of symptoms a patient experiences and may help speed the recovery process.

Keywords: cognitive activity, EEG, machine learning, personalized recovery

Procedia PDF Downloads 220

Authors: Zilungile Sosibo

Abstract:

Constructivism in teacher education is growing tremendously in both the developed and developing world. Proponents of constructivism emphasize active engagement of students in the teaching and learning process. In an effort to keep students engaged while they learn to learn, teachers use a variety of methods to incorporate constructivism in the teaching-learning situations. One area that has a potential for realizing constructivism in the classroom is self-assessment. Sadly, students are rarely involved in the assessment of their work. Instead, the most knowing teacher dominates this process. Student involvement in self-assessments has a potential to teach student teachers to become objective assessors of their students’ work by the time they become credentialed. This is important, as objectivity in assessments is a much-needed skill in the classroom contexts within which teachers deal with students from diverse backgrounds and in which biased assessments should be avoided at all cost. The purpose of the study presented in this paper was to investigate whether student teachers acquired the skills of administering self-assessments objectively after they had been immersed in a formal training program and participated in four sets of self-assessments. The objectives were to determine the extent to which they had mastered the skills of objective self-assessments, their growth and development in this area, and the challenges they encountered in administering self-assessments objectively. The research question was: To what extent did student teachers acquire objectivity in self-assessments after their theoretical and practical engagement in this activity? Data were collected from student teachers through participant observation and semi-structured interviews. The design was a qualitative case study. The sample consisted of 39 final-year student teachers enrolled in a Bachelor of Education teacher education program at a university in South Africa. Results revealed that the formal training program and participation in self-assessments had a minimal effect on students’ acquisition of objectivity in self-assessments, due to the factors associated with self-aggrandizement and hegemony, the latter resulting from gender, religious and racial differences. These results have serious implications for the need to incorporate self-assessments in the teacher-education curriculum, as well as for extended formal training programs for student teachers on assessment in general.

Keywords: objectivity, self-assessment, student teachers, teacher education curriculum

Procedia PDF Downloads 273

Authors: A. Raj Kumar, S. Bilaloglu

Abstract:

Stroke is a leading cause of adult disability worldwide. We depend on our hands for our activities of daily living(ADL). Although many patients regain the ability to walk, they continue to experience long-term hand motor impairments. As the number of individuals with young stroke is increasing, there is a critical need for effective approaches for rehabilitation of hand function post-stroke. Motor relearning for dexterity requires task-specific kinesthetic, tactile and visual feedback. However, when a stroke results in both sensory and motor impairment, it becomes difficult to ascertain when and what type of sensory substitutions can facilitate motor relearning. In an ideal situation, real-time task-specific data on the ability to learn and data-driven feedback to assist such learning will greatly assist rehabilitation for dexterity. We have found that kinesthetic and tactile information from the unaffected hand can assist patients re-learn the use of optimal fingertip forces during a grasp and lift task. Measurement of fingertip grip force (GF), load forces (LF), their corresponding rates (GFR and LFR), and other metrics can be used to gauge the impairment level and progress during learning. Currently ATI mini force-torque sensors are used in research settings to measure and compute the LF, GF, and their rates while grasping objects of different weights and textures. Use of the ATI sensor is cost prohibitive for deployment in clinical or at-home rehabilitation. A cost effective mechatronic device is developed to quantify GF, LF, and their rates for stroke rehabilitation purposes using off-the-shelf components such as load cells, flexi-force sensors, and an Arduino UNO microcontroller. A salient feature of the device is its integration with an interactive gaming environment to render a highly engaging user experience. This paper elaborates the integration of kinesthetic and tactile sensing through computation of LF, GF and their corresponding rates in real time, information processing, and interactive interfacing through augmented reality for visual feedback.

Keywords: feedback, gaming, kinesthetic, rehabilitation, tactile

Procedia PDF Downloads 240

Authors: Farah Al-Fityani, Aljohara Alsowail, Shatha Bindawood, Heba Balrbeah

Abstract:

Locket is a popular app that lets users share spontaneous photos with a close circle of friends. The app offers a unique way to stay connected with loved ones by allowing users to see glimpses of their day through photos displayed on a widget on their home screen. This summary outlines the process of developing an app like Locket, highlighting the importance of user privacy and security. It also details the findings of a study on user engagement with the Locket app, revealing positive sentiment towards its features and concept but also identifying areas for improvement. Overall, the summary portrays Locket as a successful app that is changing the way people connect on social media.

Keywords: locket, app, machine learning, connect

Procedia PDF Downloads 46

Authors: O. F. Elkommos

Abstract:

“Humour studies” is an interdisciplinary research area that is relatively recent. It interests researchers from the disciplines of psychology, sociology, medicine, nursing, in the work place, gender studies, among others, and certainly teaching, language learning, linguistics, and literature. Linguistic theories of humour research are numerous; some of which are of interest to the present study. In spite of the fact that humour courses are now taught in universities around the world in the Egyptian context it is not included. The purpose of the present study is two-fold: to review the state of arts and to show how linguistic theories of humour can be possibly used as an art and craft of teaching and of learning in EFL literature classes. In the present study linguistic theories of humour were applied to selected literary texts to interpret humour as an intrinsic artistic communicative competence challenge. Humour in the area of linguistics was seen as a fifth component of communicative competence of the second language leaner. In literature it was studied as satire, irony, wit, or comedy. Linguistic theories of humour now describe its linguistic structure, mechanism, function, and linguistic deviance. Semantic Script Theory of Verbal Humor (SSTH), General Theory of Verbal Humor (GTVH), Audience Based Theory of Humor (ABTH), and their extensions and subcategories as well as the pragmatic perspective were employed in the analyses. This research analysed the linguistic semantic structure of humour, its mechanism, and how the audience reader (teacher or learner) becomes an interactive interpreter of the humour. This promotes humour competence together with the linguistic, social, cultural, and discourse communicative competence. Studying humour as part of the literary texts and the perception of its function in the work also brings its positive association in class for educational purposes. Humour is by default a provoking/laughter-generated device. Incongruity recognition, perception and resolving it, is a cognitive mastery. This cognitive process involves a humour experience that lightens up the classroom and the mind. It establishes connections necessary for the learning process. In this context the study examined selected narratives to exemplify the application of the theories. It is, therefore, recommended that the theories would be taught and applied to literary texts for a better understanding of the language. Students will then develop their language competence. Teachers in EFL/ESL classes will teach the theories, assist students apply them and interpret text and in the process will also use humour. This is thus easing students' acquisition of the second language, making the classroom an enjoyable, cheerful, self-assuring, and self-illuminating experience for both themselves and their students. It is further recommended that courses of humour research studies should become an integral part of higher education curricula in Egypt.

Keywords: ABTH, deviance, disjuncture, episodic, GTVH, humour competence, humour comprehension, humour in the classroom, humour in the literary texts, humour research linguistic theories, incongruity-resolution, isotopy-disjunction, jab line, longer text joke, narrative story line (macro-micro), punch line, six knowledge resource, SSTH, stacks, strands, teaching linguistics, teaching literature, TEFL, TESL

Procedia PDF Downloads 302

Authors: Mehrnoosh Abouzari, Shahrokh Sahraei

Abstract:

Today, with the increase in quantity and quality and variety of crimes, the discussion of crime prevention has faced a serious challenge that human resources alone and with traditional methods will not be effective. One of the developments in the modern world is the presence of artificial intelligence in various fields, including criminal law. In fact, the use of artificial intelligence in criminal investigations and fighting crime is a necessity in today's world. The use of artificial intelligence is far beyond and even separate from other technologies in the struggle against crime. Second, its application in criminal science is different from the discussion of prevention and it comes to the prediction of crime. Crime prevention in terms of the three factors of the offender, the offender and the victim, following a change in the conditions of the three factors, based on the perception of the criminal being wise, and therefore increasing the cost and risk of crime for him in order to desist from delinquency or to make the victim aware of self-care and possibility of exposing him to danger or making it difficult to commit crimes. While the presence of artificial intelligence in the field of combating crime and social damage and dangers, like an all-seeing eye, regardless of time and place, it sees the future and predicts the occurrence of a possible crime, thus prevent the occurrence of crimes. The purpose of this article is to collect and analyze the studies conducted on the use of artificial intelligence in predicting and preventing crime. How capable is this technology in predicting crime and preventing it? The results have shown that the artificial intelligence technologies in use are capable of predicting and preventing crime and can find patterns in the data set. find large ones in a much more efficient way than humans. In crime prediction and prevention, the term artificial intelligence can be used to refer to the increasing use of technologies that apply algorithms to large sets of data to assist or replace police. The use of artificial intelligence in our debate is in predicting and preventing crime, including predicting the time and place of future criminal activities, effective identification of patterns and accurate prediction of future behavior through data mining, machine learning and deep learning, and data analysis, and also the use of neural networks. Because the knowledge of criminologists can provide insight into risk factors for criminal behavior, among other issues, computer scientists can match this knowledge with the datasets that artificial intelligence uses to inform them.

Keywords: artificial intelligence, criminology, crime, prevention, prediction

Procedia PDF Downloads 75

Authors: Xiaoli Zhang, Xiongfei Li, Yuncong Feng

Abstract:

ROC curves, as a widely used evaluating tool in machine learning field, are the tradeoff of true positive rate and negative rate. However, they are blamed for ignoring some vital information in the evaluation process, such as the amount of information about the target that each instance carries, predicted score given by each classification model to each instance. Hence, in this paper, a new classification performance method is proposed by extending the Receiver Operator Characteristic (ROC) curves to 3D space, which is denoted as 3D ROC Histogram. In the histogram, the

Keywords: classification, performance evaluation, receiver operating characteristic histogram, hardness prediction

Procedia PDF Downloads 314

Authors: Rosa Tiziana Bruno

Abstract:

The connection between education and sustainability has been posited to have benefit for realizing a social development compatible with environmental protection. However, an educational paradigm based on the passage of information or on the fear of a catastrophe might not favor the acquisition of eco-identity. To build a sustainable world, it is necessary to "become people" in harmony with other human beings, being aware of belonging to the same human community that is part of the natural world. This can only be achieved within an authentic educating community and the most effective tools for building educating communities are reading literacy and storytelling. This paper is the report of a research-action carried out in this direction, in agreement with the sociology department of the University of Salerno, which involved four hundred children and their teachers in a path based on the combination of reading literacy, storytelling, autobiographical writing and outdoor education. The goal of the research was to create an authentic educational community within the school, capable to encourage the acquisition of an eco-identity by the pupils, that is, personal and relational growth in the full realization of the Self, in harmony with the social and natural environment, with a view to an authentic education for sustainability. To ensure reasonable validity and reliability of findings, the inquiry started with participant observation and a process of triangulation has been used including: semi-structured interview, socio-semiotic analysis of the conversation and time budget. Basically, a multiple independent sources of data was used to answer the questions. Observing the phenomenon through multiple "windows" helped to comparing data through a variety of lenses. All teachers had the experience of implementing a socio-didactic strategy called "Fiabadiario" and they had the possibility to use it with approaches that fit their students. The data being collected come from the very students and teachers who are engaged with this strategy. The educational path tested during the research has produced sustainable relationships and conflict resolution within the school system and between school and families, creating an authentic and sustainable learning community.

Keywords: educating community, education for sustainability, literature in education, social relations

Procedia PDF Downloads 122

Authors: Mohaya Farzin, Parvin Babaei, Mohammad Rostampour

Abstract:

Ghrelin plays a considerable role in important neurological effects related to food intake and energy homeostasis. As was found, regular physical activity may make available significant improvements to cognitive functions in various behavioral situations. Anxiety is one of the main concerns of the modern world, affecting millions of individuals’ health. There are contradictory results regarding ghrelin's effects on anxiety-like behavior, and the plasma level of this peptide is increased during physical activity. Here we aimed to evaluate the coincident effects of exogenous ghrelin and aerobic exercise on anxiety-like behavior and passive avoidance memory in Wistar rats. Forty-five male Wistar rats (250 ± 20 g) were divided into 9 groups (n=5) and received intra-hippocampal injections of 3.0 nmol ghrelin and performed aerobic exercise training for 8 weeks. Control groups received the same volume of saline and diazepam as negative and positive control groups, respectively. Learning and memory were estimated using a shuttle box apparatus, and anxiety-like behavior was recorded by an elevated plus-maze test (EPM). Data were analyzed by ANOVA test, and p<0.05 was considered significant. Our findings showed that the combined effect of ghrelin and aerobic exercise improves the acquisition, consolidation, and retrieval of passive avoidance memory in Wistar rats. Furthermore, it is supposed that the ghrelin receiving group spent less time in open arms and fewer open arms entries compared with the control group (p<0.05). However, exercising Wistar rats spent more time in the open arm zone in comparison with the control group (p<0.05). The exercise + Ghrelin administration established reduced anxiety (p<0.05). The results of this study demonstrate that aerobic exercise contributes to an increase in the endogenous production of ghrelin, and physical activity alleviates anxiety-related behaviors induced by intra-hippocampal injection of ghrelin. In general, exercise and ghrelin can reduce anxiety and improve memory.

Keywords: anxiety, ghrelin, aerobic exercise, learning, passive avoidance memory

Procedia PDF Downloads 120