Search results for: the creative learning process

Authors: Greg Turner, Bin Lu, Cheer-Sun Yang

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As computing technology advances, smartphone applications can assist in student learning in a pervasive way. For example, the idea of using a mobile apps for the PA Common Trees, Pests, Pathogens, in the field as a reference tool allows middle school students to learn about trees and associated pests/pathogens without bringing a textbook. In the past, some researches study the mobile software Mobile Application Software Development Life Cycle (MADLC) including traditional models such as the waterfall model, or more recent Agile Methods. Others study the issues related to the software development process. Very little research is on the development of three heterogenous mobile systems simultaneously in a small university where the availability of developers is an issue. In this paper, we propose to use a hybride model of Waterfall Model and the Agile Model, known as the Relay Race Methodology (RRM) in practice, to reflect the concept of racing and relaying for scheduling. Based on the development project, we observe that the modeling of the transition between any two phases is manifested naturally. Thus, we claim that the RRM model can provide a de fecto rather than a de jure basis for the core concept in the MADLC. In this paper, the background of the project is introduced first. Then, the challenges are pointed out followed by our solutions. Finally, the experiences learned and the future work are presented.

Keywords: agile methods, mobile apps, software process model, waterfall model

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Authors: Dibyendu Adak, Saroj Mandal

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The fly ash based geopolymer concrete generally requires heat activation after casting, which has been considered as an important limitation for its practical application. Such limitation can be overcome by a modification in the process at the time of mixing of ingredients (fly and activator fluid) for geopolymer concrete so that curing can be made at ambient temperature. This process modified geopolymer concrete shows an appreciable improvement in structural performance compared to conventional heat cured geopolymer concrete and control cement concrete. The improved durability performance based on water absorption, sulphate test, and RCPT is also noted. The microstructural properties analyzed through Field Emission Scanning Electron Microscope (FESEM) with Energy Dispersive X-ray Spectroscopy (EDS) and X-ray Diffraction (XRD) techniques show the better interaction of fly ash and activator solution at early ages for the process modified geopolymer concrete. This accelerates the transformation of the amorphous phase of fly ash to the crystalline phase.

Keywords: fly ash, geopolymer concrete, process modification, structural properties, durability, micro-structures

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Authors: Stefan Ninković

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The main purpose of this paper is to examine the different theoretical approaches and relevant empirical evidence and thus, recognize some of the most pressing challenges faced by school leaders. This paper starts from the fact that the new mission of the school is characterized by the need for stronger coordination among students' academic, social and emotional learning. In this sense, school leaders need to focus their commitment, vision and leadership on the issues of students' attitudes, language, cultural and social background, and sexual orientation. More specifically, they should know what a good teaching is for student’s at-risk, students whose first language is not dominant in school, those who’s learning styles are not in accordance with usual teaching styles, or who are stigmatized. There is a rather wide consensus around the fact that the traditionally popular concept of instructional leadership of the school principal is no longer sufficient. However, in a number of "pro-leadership" circles, including certain groups of academic researchers, consultants and practitioners, there is an established tendency of attributing school principal an extraordinary influence towards school achievements. On the other hand, the situation in which all employees in the school are leaders is a utopia par excellence. Although leadership obviously can be efficiently distributed across the school, there are few findings that speak about sources of this distribution and factors making it sustainable. Another idea that is not particularly new, but has only recently gained in importance is related to the fact that the collective capacity of the school is an important resource that often remains under-cultivated. To understand the nature and power of collaborative school cultures, it is necessary to know that these operate in a way that they make their all collective members' tacit knowledge explicit. In this sense, the question is how leaders in schools can shape collaborative culture and create social capital in the school. Pressure exerted on schools to systematically collect and use the data has been accompanied by the need for school leaders to develop new competencies. The role of school leaders is critical in the process of assessing what data are needed and for what purpose. Different types of data are important: test results, data on student’s absenteeism, satisfaction with school, teacher motivation, etc. One of the most important tasks of school leaders are data-driven decision making as well as ensuring transparency of the decision-making process. Finally, the question arises whether the existing models of school leadership are compatible with the current social and economic trends. It is necessary to examine whether and under what conditions schools are in need for forms of leadership that are different from those that currently prevail. Closely related to this issue is also to analyze the adequacy of different approaches to leadership development in the school.

Keywords: educational changes, leaders, leadership, school

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Authors: Nadia Selene Molina-Moreno, Maria Susana Avila-Garcia, Marco Bianchetti, Marcelina Pantoja-Flores

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Visual Programming Tools available are a great tool for introducing children to programming and to develop a skill set for algorithmic thinking. On the other hand, collaborative learning and pair programming within the context of programming activities, has demonstrated to have social and learning benefits. However, some of the online tools available for programming for children are not designed to allow simultaneous and equitable participation of the team members since they allow only for a single control point. In this paper, a report the work conducted with children playing a user role is presented. A preliminary study to cull ideas, insights, and design considerations for a formal programming course for children aged 8-10 using collaborative learning as a pedagogical approach was conducted. Three setups were provided: 1) lo-fi prototype, 2) PC, 3) a 46' multi-touch single display groupware limited by the application to a single touch entry. Children were interviewed at the end of the sessions in order to know their opinions about teamwork and the different setups defined. Results are mixed regarding the setup, but they agree to like teamwork.

Keywords: children, collaborative programming, visual programming, multi-touch tabletop, lo-fi prototype

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Authors: Amedeo D'Angiulli, Kylie Schibli

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Traditionally, children’s brain development research has emphasized the limitative aspects of disability and impairment, electing as an explanatory model the classical clinical notions of brain lesion or functional deficit. In contrast, Positive Educational Neuroscience (PEN) is a new approach that emphasizes strengths and human flourishing related to the brain by exploring how learning practices have the potential to enhance neurocognitive flexibility through neuroplastic overcompensation. This mini-review provides an overview of PEN and shows how it links to the concept of neurocognitive flexibility. We provide examples of how the present concept of neurocognitive flexibility can be applied to special education by exploring examples of neuroplasticity in the learning domain, including: (1) learning to draw in congenitally totally blind children, and (2) music training in children from disadvantaged neighborhoods. PEN encourages educators to focus on children’s strengths by recognizing the brain’s capacity for positive change and to incorporate activities that support children’s individual development.

Keywords: neurocognitive development, positive educational neuroscience, sociocultural approach, special education

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Authors: Rakesh Prajapati, Purvik Patel, Hardik Patel

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Productivity and quality are two important aspects that have become great concerns in today’s competitive global market. Every production/manufacturing unit mainly focuses on these areas in relation to the process, as well as the product developed. The electrical discharge machining (EDM) process, even now it is an experience process, wherein the selected parameters are still often far from the maximum, and at the same time selecting optimization parameters is costly and time consuming. Material Removal Rate (MRR) during the process has been considered as a productivity estimate with the aim to maximize it, with an intention of minimizing surface roughness taken as most important output parameter. These two opposites in nature requirements have been simultaneously satisfied by selecting an optimal process environment (optimal parameter setting). Objective function is obtained by Regression Analysis and Analysis of Variance. Then objective function is optimized using Genetic Algorithm technique. The model is shown to be effective; MRR and Surface Roughness improved using optimized machining parameters.

Keywords: MMR, TWR, OC, DOE, ANOVA, minitab

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Authors: Isam A. H. Al Zubaidy

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A quick process may be needed to get the benefit the big generated quantity of waste oil sludge (WOS). The process includes the mixing process of WOS with commercial diesel fuel. Different ratios of WOS to diesel fuel were prepared ranging 1:1 to 20:1 by mass. The mixture was continuously mixing for 10 minutes using bench type overhead stirrer and followed by filtration process to separate the soil waste from filtrate oil product. The quantity and the physical properties of the oil filtrate were measured. It was found that the addition of up to 15% WOS to diesel fuel was accepted without dramatic changes to the properties of diesel fuel. The amount of waste oil sludge was decreased by about 60% by mass. This means that about 60 % of the mass of sludge was recovered as light fuel oil. The physical properties of the resulting fuel from 10% sludge mixing ratio showed that the specific gravity, ash content, carbon residue, asphaltene content, viscosity, diesel index, cetane number, and calorific value were affected slightly. The color was changed to light black color. The sulfur content was increased also. This requires other processes to reduce the sulfur content of the resulting light fuel. A new desulfurization process was achieved using adsorption techniques with activated biomaterial to reduce the sulfur content to acceptable limits. Adsorption process by ZnCl₂ activated date palm kernel powder was effective for improvement of the physical properties of diesel like fuel. The final sulfur content was increased to 0.185 wt%. This diesel like fuel can be used in all tractors, buses, tracks inside and outside the refineries. The solid remaining seems to be smooth and can be mixed with asphalt mixture for asphalting the roads or can be used with other materials as an asphalt coating material for constructed buildings. Through this process, valuable fuel has been recovered, and the amount of waste material had decreased.

Keywords: oil sludge, diesel fuel, blending process, filtration process

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Authors: Fernando Nobre Cavalcante

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In Brazil, and more recently in the city of Fortaleza, there is a new form of entrepreneurship that is focused on the information and communication technology service sector and that draws the attention of young people, investors, governments, authors and media companies: it is known as the start-up movement. Today, it is considered to be a driving force behind the creative economy. Rooted on progressive discourse, the words enterprise and innovation seduce new economic agents motivated by success stories from Silicon Valley in America along with increasing commercial activity for digital goods and services. This article assesses, from a sociological point of view, the new productive wave problematized by the light of Manuel Castells’ informational capitalism. Considering the skeptical as well as the optimistic opinions about the impact of this new entrepreneurial rearrangement, the following question is asked: How Brazilian entrepreneurs evaluate public policy incentives for startups Brazilian Federal Government? The raised hypotheses are based on employability factors as well as cultural, economical, and political matters related to innovation and technology. This study has produced a nationwide quantitative assessment with a special focus on the reality of these Ceará firms; as well as comparative qualitative interviews on Brazilian experiences lived by identified agents. This article outlines the public incentive policy of the federal government, the Start-up Brasil Program, from the perspective of these companies and provides details as to the discipline methods of the new enterprising way born in the United States. The startups are very young companies that are headed towards the economic sustainment of the productive sector services. These companies are dropping the seeds that will produce the re-enchantment of young people and bring them back to participation in political debate; they provide relief and reheats the job market; and they produce a democratization of the entrepreneurial ‘Do-It-Yourself’ culture. They capitalize the pivot of the wall street wolves and of agents being charged for new masks. There are developmental logic’s prophylaxis in the face of dreadful innovation stagnation. The lack of continuity in Brazilian governmental politics and cultural nuances related to entrepreneurship are barring the desired regional success of this ecosystem.

Keywords: creative economy, entrepreneurship, informationalism, innovation, startups, start-up brasil program

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Authors: T. Wejrzanowski, M. Grybczuk, E. Tymicki, K. J. Kurzydlowski

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In the present study, numerical simulations of heat and mass transfer in Physical Vapor Transport reactor during silicon carbide single crystal growth are addressed. Silicon carbide is a wide bandgap material with unique properties making it highly applicable for high power electronics applications. Because of high manufacturing costs improvements of SiC production process are required. In this study, numerical simulations were used as a tool of process optimization. Computer modeling allows for cost and time effective analysis of processes occurring during SiC single crystal growth and provides essential information needed for improvement of the process. Quantitative relationship between process conditions, such as temperature or pressure, and crystal growth rate and shape of crystallization front have been studied and verified using experimental data. Basing on modeling results, several process improvements were proposed and implemented.

Keywords: Finite Volume Method, semiconductors, Physica Vapor Transport, silicon carbide

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: Arun Sanjel, Greg Speegle

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Every second, massive amounts of data are generated, and Data Intensive Scalable Computing (DISC) frameworks have evolved into effective tools for analyzing such massive amounts of data. Since the underlying architecture of these distributed computing platforms is often new to users, building a DISC application can often be time-consuming and prone to errors. The automated conversion of a sequential program to a DISC program will consequently significantly improve productivity. However, synthesizing a user’s intended program from an input specification is complex, with several important applications, such as distributed program synthesizing and code refactoring. Existing works such as Tyro and Casper rely entirely on deductive synthesis techniques or similar program synthesis approaches. Our approach is to develop a data-driven synthesis technique to identify sequential components and translate them to equivalent distributed operations. We emphasize using reinforcement learning and unit testing as feedback mechanisms to achieve our objectives.

Keywords: program synthesis, distributed computing, reinforcement learning, unit testing, DISC

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Authors: Said Ahmed Zohairy

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Significant shifts in the theory and practice of teaching vocabulary affect teachers’ decisions about learning materials’ design. Relevant literature supports teaching specialised, authentic, and multi-word lexical items rather than focusing on single-word vocabulary lists. Corpora, collections of texts stored in a database, presents a reliable source of teaching and learning materials. Although corpus-informed studies provided guidance for teachers to identify useful language chunks and phraseological units, there is a scarcity in the literature discussing the use of corpora in teaching English for academic and specific purposes (EASP). The aim of this study is to improve teaching practices and provide a description of the pedagogical choices and procedures of an EASP tutor in an attempt to offer guidance for novice corpus users. It draws on the researcher’s experience of utilising corpus linguistic tools to design vocabulary learning activities without focusing on students’ learning outcomes. Hence, it adopts a self-study research methodology which is based on five methodological components suggested by other self-study researchers. The findings of the study noted that designing specialised and corpus-informed vocabulary learning activities could be challenging for teachers, as they require technical knowledge of how to navigate corpora and utilise corpus analysis tools. Findings also include a description of the researcher’s approach to building and analysing a specialised corpus for the benefit of novice corpus users; they should be able to start their own journey of designing corpus-based activities.

Keywords: corpora, corpus linguistics, corpus-informed, English for academic and specific purposes, agribusiness, vocabulary, phraseological units, materials design

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Authors: Francisco Ben, Tracey Price, Chad Morrison, Victoria Warren, Willy Gollan, Robyn Dunbar, Frank Davies, Mark Sorrell

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This paper focuses particularly on the educational aspects that contribute to the overall educational satisfaction rated by Tabor School of Education students who participated in the Australasian Survey of Student Engagement (AUSSE) conducted by the Australian Council for Educational Research (ACER) in 2010, 2012 and 2013. In all three years of participation, Tabor ranked first especially in the area of overall student satisfaction. By using a single level path analysis in relation to the AUSSE datasets collected using the Student Engagement Questionnaire (SEQ) for Tabor School of Education, seven aspects that contribute to overall student satisfaction have been identified. There appears to be a direct causal link between aspects of the Supportive Learning Environment, Work Integrated Learning, Career Readiness, Academic Challenge, and overall educational satisfaction levels. A further three aspects, being Student and Staff Interactions, Active Learning, and Enriching Educational Experiences, indirectly influence overall educational satisfaction levels.

Keywords: attrition, retention, educational experience, pre-service teacher education, student satisfaction

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Authors: Liat Shamri-Zeevi, Neta Ram-Vlasov

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Today the world as a whole is attempting to cope with the COVID-19, which has affected all facets of personal and social life from country-wide confinement to maintaining social distance and taking protective measures to maintain hygiene. One of the populations faced with the most severe restrictions is seniors. Various studies have shown that creativity plays a crucial role in dealing with crisis events. Painting - regardless of media - allows for emotional and cognitive processing of these situations, and enables the expression of experiences in a tangible creative way that conveys and endows meaning to the artwork. The current study was conducted in Israel immediately after a 6-week lockdown. It was designed to specifically examine the impact of the COVID-19 pandemic on the quality of life of elderly women as reflected in their artworks. The sample was composed of 21 Israeli women aged 60-90, in good mental health (without diagnosed dementia or Alzheimer's), all of whom were Hebrew-speaking, and retired with an extended family, who indicated that they painted and had engaged in artwork on an ongoing basis throughout the lockdown (from March 12 to May 30, 2020). The participants' artworks were collected, and a semi-structured in-depth interview was conducted that lasted one to two hours. The participants were asked about their feelings during the pandemic and the artworks they produced during this time, and completed a questionnaire on well-being and mental health. The initial analysis of the interviews and artworks revealed themes related to the specific role of each piece of artwork. The first theme included notions that the artwork was an activity and a framework for doing, which supported positive emotions, and provided a sense of vitality during the closure. Most of the participants painted images of nature and growth which were ascribed concrete and symbolic meaning. The second theme was that the artwork enabled the processing of difficult and /or conflicting emotions related to the situation, including anxiety about death and loneliness that were symbolically expressed in the artworks, such as images of the Corona virus and the respiratory machines. The third theme suggested that the time and space prompted by the lockdown gave the participants time for a gathering together of the self, and freed up time for creative activities. Many participants stated that they painted more and more frequently during the Corona lockdown. At the conference, additional themes and findings will be presented.

Keywords: Corona virus, artwork, quality of life of elderly

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Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

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The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

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Authors: Gabriela Steffen

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Learning in a bilingual classroom not only implies learning in two languages or in an L2, it also means learning content subjects through the means of bilingual or plurilingual resources, which is of a qualitatively different nature than ‘monolingual’ learning. These resources form elements of a didactics of plurilingualism, aiming not only at the development of a plurilingual competence, but also at drawing on plurilingual resources for nonlinguistic subject learning. Applying a didactics of plurilingualism allows for taking account of the specificities of bilingual content subject learning in bilingual education classrooms. Bilingual education is used here as an umbrella term for different programs, such as bilingual education, immersion, CLIL, bilingual modules in which one or several non-linguistic subjects are taught partly or completely in an L2. This paper aims at discussing first results of a study on pupil group work in bilingual classrooms in several Swiss primary schools. For instance, it analyses two bilingual classes in two primary schools in a French-speaking region of Switzerland that follows a part of their school program through German in addition to French, the language of instruction in this region. More precisely, it analyses videotaped classroom interaction and in situ classroom practices of pupil group work in a mathematics lessons. The ethnographic observation of pupils’ group work and the analysis of their interaction (analytical tools of conversational analysis, discourse analysis and plurilingual interaction) enhance the description of whole-class interaction done in the same (and several other) classes. While the latter are teacher-student interactions, the former are student-student interactions giving more space to and insight into pupils’ talk. This study aims at the description of the linguistic and multimodal resources (in German L2 and/or French L1) pupils mobilize while carrying out a mathematical task. The analysis shows that the accomplishment of the mathematical task takes place in a bilingual mode, whether the whole-class interactions are conducted rather in a bilingual (German L2-French L1) or a monolingual mode in L2 (German). The pupils make plenty of use of German L2 in a setting that lends itself to use French L1 (peer groups with French as a dominant language, in absence of the teacher and a task with a mathematical aim). They switch from French to German and back ‘naturally’, which is regular for bilingual speakers. Their linguistic resources in German L2 are not sufficient to allow them to (inter-)act well enough to accomplish the task entirely in German L2, despite their efforts to do so. However, this does not stop them from carrying out the task in mathematics adequately, which is the main objective, by drawing on the bilingual resources at hand.

Keywords: bilingual content subject learning, bilingual primary education, bilingual pupil group work, bilingual teaching/learning resources, didactics of plurilingualism

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Authors: Ji-Seok Koo, Hee‑Yong Kwon, Hui-Young Yun, Kyung-Hui Wang, Youn-Seo Koo

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This paper presents a forecasting system for PM2.5 levels in Seoul, South Korea, leveraging a combination of chemical transport modeling and ConvLSTM-DNN machine learning technology. Exposure to PM2.5 has known detrimental impacts on public health, making its prediction crucial for establishing preventive measures. Existing forecasting models, like the Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF), are hindered by their reliance on uncertain input data, such as anthropogenic emissions and meteorological patterns, as well as certain intrinsic model limitations. The system we've developed specifically addresses these issues by integrating machine learning and using carefully selected input features that account for local and distant sources of PM2.5. In South Korea, the PM2.5 concentration is greatly influenced by both local emissions and long-range transport from China, and our model effectively captures these spatial and temporal dynamics. Our PM2.5 prediction system combines the strengths of advanced hybrid machine learning algorithms, convLSTM and DNN, to improve upon the limitations of the traditional CMAQ model. Data used in the system include forecasted information from CMAQ and WRF models, along with actual PM2.5 concentration and weather variable data from monitoring stations in China and South Korea. The system was implemented specifically for Seoul's PM2.5 forecasting.

Keywords: PM2.5 forecast, machine learning, convLSTM, DNN

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Authors: Uma Maheshwari V., Rajanikanth Aluvalu, Kumar Gautam

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The COVID-19 disease is a highly contagious viral infection with major worldwide health implications. The global economy suffers as a result of COVID. The spread of this pandemic disease can be slowed if positive patients are found early. COVID-19 disease prediction is beneficial for identifying patients' health problems that are at risk for COVID. Deep learning and machine learning algorithms for COVID prediction using X-rays have the potential to be extremely useful in solving the scarcity of doctors and clinicians in remote places. In this paper, a convolutional neural network (CNN) with deep layers is presented for recognizing COVID-19 patients using real-world datasets. We gathered around 6000 X-ray scan images from various sources and split them into two categories: normal and COVID-impacted. Our model examines chest X-ray images to recognize such patients. Because X-rays are commonly available and affordable, our findings show that X-ray analysis is effective in COVID diagnosis. The predictions performed well, with an average accuracy of 99% on training photographs and 88% on X-ray test images.

Keywords: deep CNN, COVID–19 analysis, feature extraction, feature map, accuracy

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Authors: Anna-Maria Ramezanzadeh

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Demand for Arabic language is growing worldwide, driven by increased interest in the multifarious purposes the language serves, both for the population of heritage learners and those studying Arabic as a foreign language. The diglossic, or indeed multiglossic nature of the language as used in Arabic speaking communities however, is seldom represented in the content of classroom courses. This disjoint between the nature of provision and students’ expectations can severely impact their engagement with course material, and their motivation to either commence or continue learning the language. The nature of motivation and its relationship to multiglossia is sparsely explored in current literature on Arabic. The theoretical framework here proposed aims to address this gap by presenting a model and instruments for the measurement of Arabic learners’ motivation in relation to the multiple strands of the language. It adopts and develops the Second Language Motivation Self-System model (L2MSS), originally proposed by Zoltan Dörnyei, which measures motivation as the desire to reduce the discrepancy between leaners’ current and future self-concepts in terms of the second language (L2). The tripartite structure incorporates measures of the Current L2 Self, Future L2 Self (consisting of an Ideal L2 Self, and an Ought-To Self), and the L2 Learning Experience. The strength of the self-concepts is measured across three different domains of Arabic: Classical, Modern Standard and Colloquial. The focus on learners’ self-concepts allows for an exploration of the effect of multiple factors on motivation towards Arabic, including religion. The relationship between Islam and Arabic is often given as a prominent reason behind some students’ desire to learn the language. Exactly how and why this factor features in learners’ L2 self-concepts has not yet been explored. Specifically designed surveys and interview protocols are proposed to facilitate the exploration of these constructs. The L2 Learning Experience component of the model is operationalized as learners’ task-based engagement. Engagement is conceptualised as multi-dimensional and malleable. In this model, situation-specific measures of cognitive, behavioural, and affective components of engagement are collected via specially designed repeated post-task self-report surveys on Personal Digital Assistant over multiple Arabic lessons. Tasks are categorised according to language learning skill. Given the domain-specific uses of the different varieties of Arabic, the relationship between learners’ engagement with different types of tasks and their overall motivational profiles will be examined to determine the extent of the interaction between the two constructs. A framework for this data analysis is proposed and hypotheses discussed. The unique combination of situation-specific measures of engagement and a person-oriented approach to measuring motivation allows for a macro- and micro-analysis of the interaction between learners and the Arabic learning process. By combining cross-sectional and longitudinal elements with a mixed-methods design, the model proposed offers the potential for capturing a comprehensive and detailed picture of the motivation and engagement of Arabic learners. The application of this framework offers a number of numerous potential pedagogical and research implications which will also be discussed.

Keywords: Arabic, diglossia, engagement, motivation, multiglossia, sociolinguistics

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Authors: Roli Saini, Pradeep Kumar

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A coagulation/flocculation process was adopted for the reduction of carbamate insecticide (carbofuran) from aqueous solution. Ferric chloride (FeCl₃) was used as a coagulant to treat the carbofuran. To exploit the reduction efficiency of pesticide concentration and COD, the jar-test experiments were carried out and process was optimized through response surface methodology (RSM). The effects of two independent factors; i.e., FeCl₃ dosage and pH on the reduction efficiency were estimated by using central composite design (CCD). The initial COD of the 30 mg/L concentrated solution was found to be 510 mg/L. Results exposed that the maximum reduction occurred at an optimal condition of FeCl₃ = 80 mg/L, and pH = 5.0, from which the reduction of concentration and COD 75.13% and 65.34%, respectively. The present study also predicted that the obtained regression equations could be helpful as the theoretical basis for the coagulation process of pesticide wastewater.

Keywords: carbofuran, coagulation, optimization, response surface methodology

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Authors: Mesfin Getu Woldetensay

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Representatives and commercially viable natural gas liquids (NGLs) recovery processes were studied under various feed conditions that are classified as lean and rich. The conventional turbo- expander process scheme (ISS) was taken as a base case. The performance of this scheme was compared against with the gas sub-cooled process (GSP), cold residue-gas (CRR) and recycle split-vapor (RSV), enhanced NGL recovery process (IPSI-1) and enhanced NGL recovery process with internal refrigeration (IPSI-2). The development made for the GSP, CRR and RSV are at the top section of the demethanizer column whereas the IPSI-1 and IPSI-2 improvement focus in the lower section. HYSYS process flowsheet was initially developed for all the processes including the ISS under a common criteria that could help to demonstrate the performance comparison. Accordingly, a number of simulation runs were made for the selected eight types of feed. Results show that the reboiler duty requirement using rich feeds for GSP, CRR and RSV is quite high compared to IPSI-1 and IPSI-2. The latter shows relatively lower duty due to the presence of self-refrigeration system that allows the inlet feed to be used for achieving cooling without the need to use propane refrigerant. The energy consumption for lean feed is much lower than that of the rich feed in all process schemes.

Keywords: composition, lean, rich, duty

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Authors: Karl Baker-Green, Ian Woolsey

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Historically, a variety of methods have been used to access the student voice within higher education, including module evaluations and informal classroom feedback. However, currently, the views articulated in student-staff-committee meetings bear the most weight and can therefore have the most significant impact on departmental policy. Arguably, these forums are exclusionary as several students, including those who experience severe anxiety, might feel unable to participate in this face-to-face (large) group activities. Similarly, students who declare a disability, but are not in possession of a learning contract, are more likely to withdraw from their studies than those whose additional needs have been formally recognised. It is also worth noting that whilst the number of disabled students in Higher Education has increased in recent years, the percentage of those who have been issued a learning contract has decreased. These issues foreground the need to explore the educational experiences of students with or without a learning contract in order to identify their respective aspirations and needs and therefore help shape education policy. This is in keeping with the ‘Nothing about us without us’, agenda, which recognises that disabled individuals are best placed to understand their own requirements and the most effective strategies to meet these.

Keywords: education, student voice, student experience, student retention

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Authors: Tsega Asresa Mengistu, Getahun Tigistu

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Computer vision is an artificial intelligence subfield that allows computers and systems to retrieve meaning from digital images. It is applied in various fields of study self-driving cars, video surveillance, agriculture, Quality control, Health care, construction, military, and everyday life. Aromatic and medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, and other natural health products for therapeutic and Aromatic culinary purposes. Herbal industries depend on these special plants. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs, and going to export not only industrial raw materials but also valuable foreign exchange. There is a lack of technologies for the classification and identification of Aromatic and medicinal plants in Ethiopia. The manual identification system of plants is a tedious, time-consuming, labor, and lengthy process. For farmers, industry personnel, academics, and pharmacists, it is still difficult to identify parts and usage of plants before ingredient extraction. In order to solve this problem, the researcher uses a deep learning approach for the efficient identification of aromatic and medicinal plants by using a convolutional neural network. The objective of the proposed study is to identify the aromatic and medicinal plant Parts and usages using computer vision technology. Therefore, this research initiated a model for the automatic classification of aromatic and medicinal plants by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides the root, flower and fruit, latex, and barks. The study was conducted on aromatic and medicinal plants available in the Ethiopian Institute of Agricultural Research center. An experimental research design is proposed for this study. This is conducted in Convolutional neural networks and Transfer learning. The Researcher employs sigmoid Activation as the last layer and Rectifier liner unit in the hidden layers. Finally, the researcher got a classification accuracy of 66.4 in convolutional neural networks and 67.3 in mobile networks, and 64 in the Visual Geometry Group.

Keywords: aromatic and medicinal plants, computer vision, deep convolutional neural network

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Authors: Mei Lin, Lana Yj Liu, Thin Ngoc Pham

Abstract:

Postgraduate taught (PGT) students’ learning strategies align with what the learning task constitutes and the environment that the task creates. Cognitively, they can discover new perspectives, challenge general assumptions, establish clear connections, and synthesise information. Metacognitively, their engagement is conducive to the development of planning, monitoring, and evaluating strategies. Given that there has been a lack of longitudinal insights into international PGT students’ experiences of the cognitive and metacognitive space created in the tasks, this paper presentation aims to fill the gaps by longitudinally exploring (1) the fundamentals of task designs to create cognitive and metacognitive space and (2) the opportunities and challenges of multicultural group discussions as a pedagogical approach for the implementation of cognitive and metacognitive space in the learning tasks. Data were collected from the two rounds of semi-structured interviews with 11 international PGT students in two programmes at a UK university -at the end of semester one and at the end of semester two. The findings show that the task designs, to create cognitive and metacognitive space, need to include four interconnected factors: clarity, relevance, motivation, and practicality. In addition, international PGT students perceived that they practised and developed their cognitive and metacognitive abilities while getting immersed in multicultural group discussions. The findings, from the learners’ point of view, make some pedagogy-related suggestions to the task designs at the master’s level, particularly how to engage students in learning during their transition into higher education in a different cultural setting.

Keywords: cognitive space, master students, metacognitive space, task design

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Authors: Daniel Tochwin

Abstract:

Continuous improvement is an essential part of effective process performance management. In order to achieve continuous quality improvement, each organization must use the appropriate selection of tools and techniques. The basic condition for success is a proper understanding of the business need faced by the company and the selection of appropriate methods to improve a given production process. The main aim of this article is to analyze the methods of conduct which are popular in practice when implementing process improvements and then to determine whether the tested methods include repetitive systematics of the approach, i.e., a similar sequence of the same or similar actions. Based on an extensive literature review, 4 methods of continuous improvement of production processes were selected: A3 report, Gemba Kaizen, PDCA cycle, and Deming cycle. The research shows that all frequently used improvement methods are generally based on the PDCA cycle, and the differences are due to "(re)interpretation" and the need to adapt the continuous improvement approach to the specific business process. The research shows that all the frequently used improvement methods are generally based on the PDCA cycle, and the differences are due to "(re) interpretation" and the need to adapt the continuous improvement approach to the specific business process.

Keywords: continuous improvement, lean methods, process improvement, PDCA

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Authors: Lai Wenfang

Abstract:

Tthis study will describe how to use artificial intelligence (AI) technology to build the user-oriented platform for integrated archival service. The platform will be launched in 2020 by the National Archives Administration (NAA) in Taiwan. With the progression of information communication technology (ICT) the NAA has built many systems to provide archival service. In order to cope with new challenges, such as new ICT, artificial intelligence or blockchain etc. the NAA will try to use the natural language processing (NLP) and machine learning (ML) skill to build a training model and propose suggestions based on the data sent to the platform. NAA expects the platform not only can automatically inform the sending agencies’ staffs which records catalogues are against the transfer or destroy rules, but also can use the model to find the details hidden in the catalogues and suggest NAA’s staff whether the records should be or not to be, to shorten the auditing time. The platform keeps all the users’ browse trails; so that the platform can predict what kinds of archives user could be interested and recommend the search terms by visualization, moreover, inform them the new coming archives. In addition, according to the Archives Act, the NAA’s staff must spend a lot of time to mark or remove the personal data, classified data, etc. before archives provided. To upgrade the archives access service process, the platform will use some text recognition pattern to black out automatically, the staff only need to adjust the error and upload the correct one, when the platform has learned the accuracy will be getting higher. In short, the purpose of the platform is to deduct the government digital transformation and implement the vision of a service-oriented smart government.

Keywords: artificial intelligence, natural language processing, machine learning, visualization

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Authors: Le Chen

Abstract:

This paper presents an efficient tool platform scheme to ensure quality protection throughout the software development process. The main principle is to manage the information of requirements, design, development, testing, operation and maintenance process with proper tools, and to set up the quality standards of each process. Through the tools’ display and summary of quality standards, the quality standards can be visualizad and ready for policy decision, which is called Quality Gate in this paper. In addition, the tools are also integrated to achieve the exchange and relation of information which highly improving operational efficiency. In this paper, the feasibility of the scheme is verified by practical application of development projects, and the overall information display and data mining are proposed to be further improved.

Keywords: efficiency, quality gate, software process, tools

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Authors: Montserrrat Dopico Gonzalez, Ramon Lopez Facal

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The development of a critical, open and plural citizenship constitutes one of the main challenges of the school institution in the present multicultural societies. Didactics in Social Sciences has conducted important contributions to the development of active methodologies to promote the development of the intercultural competencies of the student body. Research in intercultural education has demonstrated the efficiency of the cooperative learning techniques to improve the intercultural relations in the classroom. Our study proposes to check the effect that, concerning the development of intercultural competencies of the student body, the emotional education can have in the context of the use of active methodologies such as the learning by projects and the cooperative learning. To that purpose, a programme of intervention based on activities focussed on controversial issues related to cultural diversity has been implemented in several secondary schools. Through a methodology which combines intercultural competence scales with interviews and also with the analysis of the school body’s productions, the persistence of stereotypes against immigration and the efficacy of the introduction of emotional education elements in the development of intercultural competencies have both been observed.

Keywords: active methodologies, didactics in social sciences, intercultural competences, intercultural education

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Authors: Christina Largent, Tazley Hobbs

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Objective: A literature review was performed to examine the existing research on COVID-19 lockdown as it relates to ADHD child/adolescent individuals, media use, and impact on educational performance/learning. It was surmised that with the COVID-19 shut-down and transition to remote learning, a less structured learning environment, increased screen time, in addition to potential difficulty accessing school resources would impair ADHD individuals’ performance and learning. A resulting increase in the number of youths diagnosed and treated for ADHD would be expected. As of yet, there has been little to no published data on the incidence of ADHD as it relates to COVID-19 outside of reports from several nonprofit agencies such as CHADD (Children and Adults with Attention-Deficit/Hyperactivity Disorder ), who reported an increased number of calls to their helpline, The New York based Child Mind Institute, who reported an increased number of appointments to discuss medications, and research released from Athenahealth showing an increase in the number of patients receiving new diagnosis of ADHD and new prescriptions for ADHD medications. Methods: A literature search for articles published between 2020 and 2021 from Pubmed, Google Scholar, PsychInfo, was performed. Search phrases and keywords included “covid, adhd, child, impact, remote learning, media, screen”. Results: Studies primarily utilized parental reports, with very few from the perspective of the ADHD individuals themselves. Most findings thus far show that with the COVID-19 quarantine and transition to online learning, ADHD individuals’ experienced decreased ability to keep focused or adhere to the daily routine, as well as increased inattention-related problems, such as careless mistakes or lack of completion in homework, which in turn translated into overall more difficulty with remote learning. To add further injury, one study showed (just on evaluation of two different sites within the US) that school based services for these individuals decreased with the shift to online-learning. Increased screen time, television, social media, and gaming were noted amongst ADHD individuals. One study further differentiated the degree of digital media, identifying individuals with “problematic “ or “non-problematic” use. ADHD children with problematic digital media use suffered from more severe core symptoms of ADHD, negative emotions, executive function deficits, damage to family environment, pressure from life events, and a lower motivation to learn. Conclusions and Future Considerations: Studies found not only was online learning difficult for ADHD individuals but it, in addition to greater use of digital media, was associated with worsening ADHD symptoms impairing schoolwork, in addition to secondary findings of worsening mood and behavior. Currently, data on the number of new ADHD cases, in addition to data on the prescription and usage of stimulants during COVID-19, has not been well documented or studied; this would be well-warranted out of concern for over diagnosing or over-prescribing our youth. It would also be well-worth studying how reversible or long-lasting these negative impacts may be.

Keywords: COVID-19, remote learning, media use, ADHD, child, adolescent

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

Abstract:

This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

Procedia PDF Downloads 340