Search results for: Learning materials

Authors: Sudheer Dhume, T. Prasad

Abstract:

There are distinct signs of Business Education losing its sheen. It is more so in developing countries. One of the reasons is the value addition at the end of 2 year MBA program is not matching with the requirements of present times and expectations of the students. In this backdrop, Pedagogy Innovation has become prerequisite for making our MBA programs relevant and useful. This paper is the description and analysis of innovative Action Learning pedagogical approach adopted by a group of faculty members at NITIE Mumbai. It not only promotes multidisciplinary research but also enhances integration of the functional areas skillsets in the students. The paper discusses the theoretical bases of this pedagogy and evaluates the effectiveness of it vis-à-vis conventional pedagogical tools. The evaluation research using Bloom’s taxonomy framework showed that this blended method of Business Education is much superior as compared to conventional pedagogy.

Keywords: action learning, blooms taxonomy, business education, innovation, pedagogy

Procedia PDF Downloads 253

Authors: Vinh Duy Cao, Shima Pilehvar, Anna M. Szczotok, Anna-Lena Kjøniksen

Abstract:

The total energy consumption is dramatically increasing on over the world, especially for building energy consumption where a significant proportion of energy is used for heating and cooling purposes. One of the solutions to reduce the energy consumption for the building is to improve construction techniques and enhance material technology. Recently, microcapsulated phase change materials (MPCM) with high energy storage capacity within the phase transition temperature of the materials is a potential method to conserve and save energy. A new composite materials with high energy storage capacity by mixing MPCM into concrete for passive building technology is the promising candidate to reduce the energy consumption. One of the most untilized building materials for mixing with MPCM is Portland cement concrete. However, the emission of carbon dioxide (CO2) due to producing cement which plays the important role in the global warming is the main drawback of PCC. Accordingly, an environmentally friendly building material, geopolymer, which is synthesized by the reaction between the industrial waste material (aluminosilicate) and a strong alkali activator, is a potential materials to mixing with MPCM. Especially, the effect of MPCM on the thermal and mechanical properties of geopolymer concrete (GPC) is very limited. In this study, high thermal energy storage capacity materials were fabricated by mixing MPCM into geopolymer concrete. This article would investigate the effect of MPCM concentration on thermal and mechanical properties of GPC. The target is to balance the effect of MPCM on improving the thermal performance and maintaining the compressive strength of the geopolymer concrete at an acceptable level for building application.

Keywords: microencapsulated phase change materials, geopolymer concrete, energy storage capacity, thermal performance

Procedia PDF Downloads 290

Authors: Katherine Karayianis

Abstract:

Children with Attention Deficit Hyperactivity Disorder (ADHD) have trouble learning in traditional classrooms. These children miss out on important developmental opportunities in school, which leads to challenges starting in early childhood, and these problems persist throughout their adult lives. Despite receiving supplemental support in school, children with ADHD still perform below their non-ADHD peers. Thus, there is a great need to find better ways of facilitating learning in children with ADHD. Evidence has shown that children with ADHD learn best through interactive engagement, but this is not always possible in schools, given classroom restraints and the large student-to-teacher ratio. Redesigning classrooms may not be feasible, so informal learning opportunities provide a possible alternative. One popular informal learning opportunity is educational TV shows like Sesame Street. These types of educational shows can teach children foundational skills taught in pre-K and early elementary school. One downside to these shows is the lack of interactive dialogue between the TV characters and the child viewers. Pseudo-interaction is often deployed, but the benefits are limited if the characters can neither understand nor contingently respond to the child. AI technology has become extremely advanced and is now popular in many electronic devices that both children and adults have access to. AI has been successfully used to create interactive dialogue in children’s educational TV shows, and results show that this enhances children’s learning and engagement, especially when children perceive the character as a reliable teacher. It is likely that children with ADHD, whose minds may otherwise wander, may especially benefit from this type of interactive technology, possibly to a greater extent depending on their perception of the animated dialogic agent. To investigate this issue, I have begun examining the moderating role of inattention among children’s learning from an educational TV show with different types of dialogic interactions. Preliminary results have shown that when character interactions are neither immediate nor accurate, children who are more easily distracted will have greater difficulty learning from the show, but contingent interactions with a TV character seem to buffer these negative effects of distractibility by keeping the child engaged. To extend this line of work, the moderating role of the child’s perception of the dialogic agent as a reliable teacher will be examined in the association between children’s attention and the type of dialogic interaction in the TV show. As such, the current study will investigate this moderated moderation.

Keywords: attention, dialogic TV, informal learning, educational TV, perception of teacher

Procedia PDF Downloads 60

Authors: Priyashri Kamlesh Sridhar, Suranga Nanayakkara

Abstract:

Current frameworks in assessment follow lesson delivery and rely heavily on test performance or teacher’s observations. This, however, neglects the underlying cognitive load during the learning process. Identifying the pivotal points when the load occurs helps design effective pedagogies and tools that respond to learners’ cognitive state. There has been limited research on quantifying cognitive load in preschoolers, real-time. In this study, we recorded electrodermal activity and heart rate variability (HRV) from 10 kindergarteners performing executive function tasks and Johnson Woodcock test of cognitive abilities. Preliminary findings suggest that there are indeed sensitive task-dependent markers in skin conductance (number of SCRs and average amplitude of SCRs) and HRV (mean heart rate and low frequency component) captured during the learning process.

Keywords: early childhood, learning, methodologies, pedagogies

Procedia PDF Downloads 306

Authors: A. Şahin Zaimoglu

Abstract:

A number of studies have been conducted recently to investigate the influence of randomly oriented fibers on some engineering properties of cohesive and cohesionless soils. However, few studies have been carried out on freezing-thawing behavior of fine-grained soils modified with discrete fiber inclusions and additive materials. This experimental study was performed to investigate the effect of randomly distributed polypropylene fibers (PP) and some additive materials [e.g.., borogypsum (BG), fly ash (FA) and cement (C)] on freezing-thawing durability (mass losses) of a fine-grained soil for 6,12 and 18 cycles. The Taguchi method was applied to the experiments and a standard L9 orthogonal array (OA) with four factors and three levels were chosen. A series of freezing-thawing tests were conducted on each specimen. 0-20 % BG, 0-20 % FA, 0-0.25 % PP and 0-3 % of C by total dry weight of mixture were used in the preparation of specimens. Experimental results showed that the most effective materials for the freezing-thawing durability (mass losses) of the samples were borogypsum and fly ash. The values of mass losses for 6, 12 and 18 cycles in optimum conditions were 16.1%, 5.1% and 3.6%, respectively.

Keywords: freezing-thawing, additive materials, reinforced soil, optimization

Procedia PDF Downloads 292

Authors: Yosr Ghozzi

Abstract:

The Cyber-Physical Systems terminology has been well received by the industrial community and specifically appropriated in educational settings. Indeed, our latest educational activities are based on the development of experimental platforms on an industrial scale. In fact, we built a collaborative learning model because of an international market study that led us to place ourselves at the heart of this technology. To align with these findings, a competency-based approach study was conducted, and program content was revised by reflecting the projectbased approach. Thus, this article deals with the development of educational devices according to a generated curriculum and specific educational activities while respecting the repository of skills adopted from what constitutes the educational cyber-physical production systems and the laboratories that are compliant and adapted to them. The implementation of these platforms was systematically carried out in the school's workshops spaces. The objective has been twofold, both research and teaching for the students in mechatronics and logistics of the electromechanical department. We act as trainers and industrial experts to involve students in the implementation of possible extension systems around multidisciplinary projects and reconnect with industrial projects for better professional integration.

Keywords: education 4.0, competency-based learning, teaching factory, project-based learning, cyber-physical systems, industry 4.0

Procedia PDF Downloads 80

Authors: Maha Awad M. Almuttairi

Abstract:

This research aimed to measure the impact of the use of a mobile learning (iTunes U) app for the development of metacognition skills delivered in the enrichment program offered to gifted students at the secondary level in Jeddah. The author targeted a group of students on an experimental scale to evaluate the achievement. The research sample consisted of a group of 38 gifted female students. The scale of evaluation of the metacognition skills used to measure the performance of students in the enrichment program was as follows: Satisfaction scale for the assessment of the technique used and the final product form after completion of the program. Appropriate statistical treatment used includes Paired Samples T-Test Cronbach’s alpha formula and eta squared formula. It was concluded in the results the difference of α≤ 0.05, which means the performance of students in the skills of metacognition in favor of using iTunes U. In light of the conclusion of the experiment, a number of recommendations and suggestions were present; the most important benefit of mobile learning applications is to provide enrichment programs for gifted students in the Kingdom of Saudi Arabia, as well as conducting further research on mobile learning and gifted student teaching.

Keywords: enrichment program, gifted students, metacognition skills, mobile learning

Procedia PDF Downloads 104

Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Yang Yung, Tracy Lin Huan

Abstract:

Glaucoma is a group of visual maladies represented by the scheduled optic nerve neuropathy; means to the increasing dwindling in vision ground, resulting in loss of sight. In this paper, a novel support vector machine based retinal therapeutic for glaucoma using machine learning algorithm is conservative. The algorithm has fitting pragmatism; subsequently sustained on correlation clustering mode, it visualizes perfect computations in the multi-dimensional space. Support vector clustering turns out to be comparable to the scale-space advance that investigates the cluster organization by means of a kernel density estimation of the likelihood distribution, where cluster midpoints are idiosyncratic by the neighborhood maxima of the concreteness. The predicted planning has 91% attainment rate on data set deterrent on a consolidation of 500 realistic images of resolute and glaucoma retina; therefore, the computational benefit of depending on the cluster overlapping system pedestal on machine learning algorithm has complete performance in glaucoma therapeutic.

Keywords: machine learning algorithm, correlation clustering mode, cluster overlapping system, glaucoma, kernel density estimation, retinal therapeutic

Procedia PDF Downloads 226

Authors: Sara ElElimy, Samir Moustafa

Abstract:

Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

Procedia PDF Downloads 124

Authors: Nihal Esam Abuzinadah, Areej Abbas Malibari, Arwa Abdulaziz Allinjawi, Paul Krause

Abstract:

Deaf and hearing-impaired students face many obstacles throughout their education, especially with learning applied sciences such as computer programming. In addition, there is no clear signs in the Arabic Sign Language that can be used to identify programming logic terminologies such as while, for, case, switch etc. However, hearing disabilities should not be a barrier for studying purpose nowadays, especially with the rapid growth in educational technology. In this paper, we develop an Avatar based system to teach computer programming to deaf and hearing-impaired students using Arabic Signed language with new signs vocabulary that is been developed for computer programming education. The system is tested on a number of high school students and results showed the importance of visualization in increasing the comprehension or understanding of concepts for deaf students through the avatar.

Keywords: hearing-impaired students, isolation, self-esteem, learning difficulties

Procedia PDF Downloads 132

Authors: Praveen S. Muthukumarana, Achala C. Aponso

Abstract:

A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images.

Keywords: agriculture, convolutional neural network, deep learning, plant disease classification, plant disease detection, plant disease diagnosis

Procedia PDF Downloads 130

Authors: Barry Ardley, Abi Hunt, Nick Taylor

Abstract:

As a theoretical and practical framework, this study uses the student-as-producer approach to learning in higher education, as adopted by the Lincoln International Business School, University of Lincoln, UK. Students as producer positions learners as skilled and capable agents, able to participate as partners with tutors in live research projects. To illuminate the nature of this approach to learning and to highlight its critical issues, the authors report on two guided student consultancy projects. These were set up with the assistance of two local organisations in the city of Lincoln, UK. Using the student as a producer model to deliver the projects enabled learners to acquire and develop a range of key skills and knowledge not easily accessible in more traditional educational settings. This paper presents a systematic case study analysis of the eight organising principles of the student-as-producer model, as adopted by university tutors. The experience of tutors implementing students as producers suggests that the model can be widely applied to benefit not only the learning and teaching experiences of higher education students and staff but additionally a university’s research programme and its community partners.

Keywords: consultancy, learning, student as producer, research

Procedia PDF Downloads 63

Authors: Karina Zaccari, Ernesto Cordeiro Marujo

Abstract:

This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

Procedia PDF Downloads 138

Authors: Farokh Alipour, Ali Falavand, Neda Beit Saeid

Abstract:

The calculation of the effect of accidental releases of flammable materials such as LNG requires the use of a suitable consequence model. This study is due to providing a planning advice for developments in the vicinity of LNG sites and other sites handling flammable materials. In this paper, an applicable algorithm that is able to model pool fires on water is presented and applied to estimate pool fire damage zone. This procedure can be used to model pool fires on land and could be helpful in consequence modeling and domino effect zone measurements of flammable materials which is needed in site selection and plant layout.

Keywords: LNG, pool fire, spill, radiation

Procedia PDF Downloads 385

Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

Abstract:

This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

Procedia PDF Downloads 200

Authors: Ksenia Siadkowska, Tytus Tulwin, Rafał Sochaczewski

Abstract:

Selecting appropriate materials is presently a complex task as material databases cover tens of thousands of different types of materials. Product designing proceeds in numerous stages and in most of them there are open questions with not only one correct solution but better and worse ones. This paper overviews the Diesel engine body construction materials mentioned in the literature and discusses a certain practical method to select materials for a cylinder head and a Diesel engine block as a prototype. The engine body, depending on its purpose, is most frequently iron or aluminum. If it is important to optimize parts to achieve low weight, aluminum alloys are usually applied, especially in the automotive and aviation industries. In the latter case, weight is even more important so new types of magnesium alloys which are even lighter than aluminum ones are developed and used. However, magnesium alloys are, for example, more flammable and not enough strong so, for safety reasons, this type of material is not used solely in engine bodies. Acknowledgement: This work has been realized in the cooperation with The Construction Office of WSK "PZL-KALISZ" S.A." and is part of Grant Agreement No. POIR.01.02.00-00-0002/15 financed by the Polish National Centre for Research and Development.

Keywords: aluminum alloy, cylinder head, Diesel engine, materials selection

Procedia PDF Downloads 382

Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

Abstract:

Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

Procedia PDF Downloads 150

Authors: Ali Bayati, Jamileh Azarnoush

Abstract:

Nowadays according to increasing the population all around the world, consuming of fossil fuels increased dramatically. Many believe that most of the atmospheric pollution comes by using fossil fuels. The process of natural sources entering cities shows one of the large challenges in consumption sources management. Nowadays, everyone considered about the consumption of fossil fuels and also Reduction of consumption civil energy in megacities that play a key role in solving serious problems such as air pollution, producing greenhouse gasses, global warming and damage ozone layer. In the construction industry, we should use the materials with the lowest need to energy for making and carrying them, and also the materials which need the lowest energy and expenses to recycling. In this way, the kind of usage material, the way of processing, regional materials and the adaptation with the environment is critical. Otherwise, the isolation should be use and mention in the long term. Accordingly, in this article we investigates the new ways in order to reduce environmental pollution and save more energy by using materials that are not harmful to the environment, fully insulated materials in buildings, sustainable and diversified buildings, suitable urban design and using solar energy more efficiently in order to reduce energy consumption.

Keywords: building design, construction masonry, insulation, sustainable construction

Procedia PDF Downloads 521

Authors: Abdelmenen Abobghala, Mahmud Ahmed, Mohamed Alwaheshi, Anwar Fanan, Meftah Mehdawi, Ahmed Abuhatira

Abstract:

This research aims to predict academic performance and identify weak points in students to aid teachers in understanding their learning needs. Both quantitative and qualitative methods are used to identify difficult test items and the factors causing difficulties. The study uses interventions like focus group discussions, interviews, and action plans developed by the students themselves. The research questions explore the predictability of final grades based on mock exams and assignments, the student's response to action plans, and the impact on learning performance. Ethical considerations are followed, respecting student privacy and maintaining anonymity. The research aims to enhance student engagement, motivation, and responsibility for learning.

Keywords: prediction, academic performance, weak points, understanding, learning, quantitative methods, qualitative methods, formative assessments, feedback, emotional responses, intervention, focus group discussion, interview, action plan, student engagement, motivation, responsibility, ethical considerations

Procedia PDF Downloads 52

Authors: Debneil Saha Roy

Abstract:

Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

Procedia PDF Downloads 154

Authors: Manisha A. Hira, Arup Rakshit

Abstract:

Textile substrates are endowed with flexibility and ease of making–up, but are non-conductors of electricity. Conductive materials like carbon can be incorporated into textile structures to make flexible conductive materials. Such conductive textiles find applications as electrostatic discharge materials, electromagnetic shielding materials and flexible materials to carry current or signals. This work focuses on use of carbon fiber as conductor of electricity. Carbon fibers in staple or tow form can be incorporated in textile yarn structure to conduct electricity. The paper highlights the process for development of these conductive yarns of polyester/carbon using Friction spinning (DREF) as well as ring spinning. The optimized process parameters for processing hybrid structure of polyester with carbon tow on DREF spinning and polyester with carbon staple fiber using ring spinning have been presented. The studies have been linked to highlight the electrical conductivity of the developed yarns. Further, the developed yarns have been incorporated as weft in fabric and their electrical conductivity has been evaluated. The paper demonstrates the structure and properties of fabrics developed from such polyester/carbon blend yarns and their suitability as electrically dissipative fabrics.

Keywords: carbon fiber, conductive textiles, electrostatic dissipative materials, hybrid yarns

Procedia PDF Downloads 283

Authors: Simon Bard, Martin Demleitner, Florian Schonl, Volker Altstadt

Abstract:

For an electrically driven aircraft, whose engine is based on semiconductors, alternative materials are needed. The avoid hotspots in the materials thermally conductive polymers are necessary. Nevertheless, the mechanical properties of these materials should remain. Herein, the work of three years in a project with airbus and Siemens is presented. Different strategies have been pursued to achieve conductive fiber-reinforced composites: Metal-coated carbon fibers, pitch-based fibers and particle-loaded matrices have been investigated. In addition, a combination of copper-coated fibers and a conductive matrix has been successfully tested for its conductivity and mechanical properties. First, prepregs have been produced with a laboratory scale prepreg line, which can handle materials with maximum width of 300 mm. These materials have then been processed to fiber-reinforced laminates. For the PAN-fiber reinforced laminates, it could be shown that there is a strong dependency between fiber volume content and thermal conductivity. Laminates with 50 vol% of carbon fiber offer a conductivity of 0.6 W/mK, those with 66 vol% of fiber a thermal conductivity of 1 W/mK. With pitch-based fiber, the conductivity enhances to 1.5 W/mK for 61 vol% of fiber, compared to 0.81 W/mK with the same amount of fibers produced from PAN (+83% in conducitivity). The thermal conductivity of PAN-based composites with 50 vol% of fiber is at 0.6 W/mK, their nickel-coated counterparts with the same fiber volume content offer a conductivity of 1 W/mK, an increase of 66%.

Keywords: carbon, electric aircraft, polymer, thermal conductivity

Procedia PDF Downloads 148

Authors: Ratima Damkham, Natasha Dejdumrong, Priyakorn Pusawiro

Abstract:

This paper explored the solution in game design to help game designers in the educational game designing using digital educational game model canvas (DEGMC) and digital educational game form (DEGF) based on Outcome-based Education program. DEGMC and DEGF can help designers develop an overview of the game while designing and planning their own game. The way to clearly assess players’ ability from learning outcomes and support their game learning design is by using the tools. Designers can balance educational content and entertainment in designing a game by using the strategies of the Business Model Canvas and design the gameplay and players’ ability assessment from learning outcomes they need by referring to the Constructive Alignment. Furthermore, they can use their design plan in this research to write their Game Design Document (GDD). The success of the research was evaluated by four experts’ perspectives in the education and computer field. From the experiments, the canvas and form helped the game designers model their game according to the learning outcomes and analysis of their own game elements. This method can be a path to research an educational game design in the future.

Keywords: constructive alignment, constructivist theory, educational game, outcome-based education

Procedia PDF Downloads 338

Authors: David Hough

Abstract:

Indigenous peoples can generally be described as the original or first peoples of a land prior to colonization. While there is no single definition of indigenous peoples, the United Nations has developed a general understanding based on self-identification and historical continuity with pre-colonial societies. Indigenous peoples are often traditional holders of unique languages, knowledge systems and beliefs who possess valuable knowledge and practices which support sustainable management of natural resources. They often have social, economic, political systems, languages and cultures, which are distinct from dominant groups in the society or state where they live. They generally resist attempts by the dominant culture at assimilation and endeavour to maintain and reproduce their ancestral environments and systems as distinctive peoples and communities. In 2007, the United Nations General Assembly passed a declaration on the rights of indigenous peoples, known as UNDRIP. It (in addition to other international instruments such as ILO 169), sets out far-reaching guidelines, which – among other things – attempt to protect and promote indigenous languages and cultures. Paragraphs 13 and 14 of the declaration state the following regarding language, culture and education: Article 13, Paragraph 1: Indigenous peoples have the right to revitalize, use, develop and transmit for future generations their histories, languages, oral traditions, philosophies, writing systems, and literatures, and to designate and retain their own names for communities, places and persons. Article 14, Paragraph I: Indigenous peoples have the right to establish and control their educational systems and institutions providing education in their own languages, in a manner appropriate to their cultural methods of teaching and learning. These two paragraphs call for the right of self-determination in education. Paragraph 13 gives indigenous peoples the right to control the content of their teaching, while Paragraph 14 states that the teaching of this content should be based on methods of teaching and learning which are appropriate to indigenous peoples. This paper reviews an approach to furthering linguistic and cultural human rights for indigenous peoples in education, which supports UNDRIP. It has been employed in countries in Asia and the Pacific, including the Republic of the Marshall Islands, the Federated States of Micronesia, Far East Russia and Nepal. It is based on bottom-up community-based initiatives where students, teachers and local knowledge holders come together to produce classroom materials in their own languages that reflect their traditional beliefs and value systems. They may include such things as knowledge about herbal medicines and traditional healing practices, local history, numerical systems, weights and measures, astronomy and navigation, canoe building, weaving and mat making, life rituals, feasts, festivals, songs, poems, etc. Many of these materials can then be mainstreamed into math, science language arts and social studies classes.

Keywords: Indigenous peoples, linguistic and cultural human rights, materials development, teacher training, traditional knowledge

Procedia PDF Downloads 233

Authors: Josef Steinberger, Vanni Zavarella, Hristo Tanev

Abstract:

Natural disasters cause tens of thousands of victims and massive material damages. We refer to all those events caused by natural disasters, such as damage on people, infrastructure, vehicles, services and resource supply, as micro events. This paper addresses the problem of micro - event detection in online media sources. We present a natural language grammar learning algorithm and apply it to online news. The algorithm in question is based on distributional clustering and detection of word collocations. We also explore the extraction of micro-events from social media and describe a Twitter mining robot, who uses combinations of keywords to detect tweets which talk about effects of disasters.

Keywords: online news, natural language processing, machine learning, event extraction, crisis computing, disaster effects, Twitter

Procedia PDF Downloads 466

Authors: Malini Ganapathy, Sarjit Kaur

Abstract:

Maximising learning with higher-order thinking skills with Information and Communications Technology (ICT) has been deep-rooted and emphasised in various developed countries such as the United Kingdom, the United States of America and Singapore. The transformation of the education curriculum in the Malaysia Education Development Plan (PPPM) 2013-2025 focuses on the concept of Higher Order Thinking (HOT) skills which aim to produce knowledgeable students who are critical and creative in their thinking and can compete at the international level. HOT skills encourage students to apply, analyse, evaluate and think creatively in and outside the classroom. In this regard, the National Education Blueprint (2013-2025) is grounded based on high-performing systems which promote a transformation of the Malaysian education system in line with the vision of Malaysia’s National Philosophy in achieving educational outcomes which are of world class status. This study was designed to investigate ESL students’ learning practices on the emphasis of promoting HOTs while using ICT in their curricula. Data were collected using a stratified random sampling where 100 participants were selected to take part in the study. These respondents were a group of undergraduate students who undertook ESL courses in a public university in Malaysia. A three-part questionnaire consisting of demographic information, students’ learning experience and ICT utilization practices was administered in the data collection process. Findings from this study provide several important insights on students’ learning experiences and ICT utilization in developing HOT skills.

Keywords: English as a second language students, critical and creative thinking, learning, information and communication technology and higher order thinking skills

Procedia PDF Downloads 468

Authors: Sara Corvino

Abstract:

The aim of this study is to explore the potential of inclusive learning and assessment strategies to foster students' engagement with historical debates surrounding the field of graphic design. The goal is to respond to the diversity of L4 Graphic Design students, at Nottingham Trent University, in a way that instead of 'lowering standards' can benefit everyone. This research tests, measures, and evaluates the impact of a specific intervention, an assessment task, to develop students' critical visual analysis skills and stimulate a deeper engagement with the subject matter. Within the action research approach, this work has followed a case study research method to understand students' views and perceptions of a specific project. The primary methods of data collection have been: anonymous electronic questionnaire and a paper-based anonymous critical incident questionnaire. NTU College of Business Law and Social Sciences Research Ethics Committee granted the Ethical approval for this research in November 2019. Other methods used to evaluate the impact of this assessment task have been Evasys's report and students' performance. In line with the constructivist paradigm, this study embraces an interpretative and contextualized analysis of the collected data within the triangulation analytical framework. The evaluation of both qualitative and quantitative data demonstrates that active learning strategies and the disruption of thinking patterns can foster greater students' engagement and can lead to meaningful learning.

Keywords: active learning, assessment for learning, graphic design, higher education, student engagement

Procedia PDF Downloads 161

Authors: P. Halder, A. Zaman

Abstract:

It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

Procedia PDF Downloads 138

Authors: Elnaz Akbari, Zolkafle Buntat

Abstract:

Today, the use of carbon-based materials such as graphene, carbon nanotubes, etc. in various applications is being extensively studied by researchers in the field. One of such applications is using them in gas sensors. While analytical investigations on the physical and chemical properties of carbon nanomaterials are the focal points in the studies, the need for experimental measurements on various physical characteristics of these materials is deeply felt. In this work, a set of experiments has been conducted using arc discharge Methane decomposition attempting to obtain carbonaceous materials (C-strands) formed between graphite electrodes. The current-voltage (I-V) characteristics of the fabricated C-strands have been investigated in the presence and absence of two different gases, NO2 and CO2. The results reveal that the current passing through the carbon films increases when the concentrations of gases are increased from 200 to 800 ppm. This phenomenon is a result of conductance changes and can be employed in sensing applications such as gas sensors.

Keywords: carbonaceous materials, gas sensing, methane arc discharge decomposition, I-V characteristics

Procedia PDF Downloads 204

Authors: Randa Alahmadi

Abstract:

Because the world has become a global village, English is not only used by native speakers, but also by non-native speakers from culturally diverse backgrounds. Even though learning a second/foreign language requires development of the four skills: reading, writing, listening, and speaking, there is also an intertwined relationship between language and culture, making it difficult to teach language without knowing the cultural context in which it is to be used. In the past decade, the number of international students enrolled in universities around the world has increased significantly. Having the urge to communicate effectively would serve as a motivation for both international and domestic students. The teaching of culture is important because linguistic competence is not enough for successful communication with speakers of other languages. Therefore, whether teaching natives or non-natives, students need to improve their cross-cultural communication skills and become culturally prepared to communicate successfully with people from other cultures. Teachers can equip their students for this environment by giving them appropriate knowledge and skills for effective intercultural communication. This paper will focus on the importance of intercultural communicative competence and its role in developing students’ understanding of diverse cultures as part of learning foreign/second languages. It will also explain how teachers can decide which culture should be taught: the target culture, the learners’ culture, or both. Moreover, practical and effective techniques that can be used in cultural-based language teaching will be shared.

Keywords: cultural-based language teaching, English as a lingua franca, English language learners, intercultural communicative competence

Procedia PDF Downloads 312