Search results for: meaningful learning

Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

Procedia PDF Downloads 135

Authors: Brady, Imelda Katherine, Da Cunha Fanego, Iria

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Today's university students are considered digital natives and the use of Information Technologies (ITs) forms a large part of their study and learning. In the context of language studies, applications that help with revisions of grammar or vocabulary are particularly useful, especially if they are open access. There are studies that show the effectiveness of this type of application in the learning of English as a foreign language and that using IT can help learners become more autonomous in foreign language acquisition, given that these applications can enhance awareness of the learning process; this means that learners are less dependent on the teacher for corrective feedback. We also propose that the exploitation of these technologies also enhances the work of the language instructor wishing to incorporate IT into his/her practice. In this context, the aim of this paper is to present the creation of a repository of tools that provide support in the writing and correction of texts in English and the assessment of their usefulness on behalf of university students enrolled in the English Studies Degree. The project seeks to encourage the development of autonomous learning through the acquisition of skills linked to the self-correction of written work in English. To comply with the above, our methodology follows five phases. First of all, a selection of the main open-access online applications available for the correction of written texts in English is made: AutoCrit, Hemingway, Grammarly, LanguageTool, OutWrite, PaperRater, ProWritingAid, Reverso, Slick Write, Spell Check Plus and Virtual Writing Tutor. Secondly, the functionalities of each of these tools (spelling, grammar, style correction, etc.) are analyzed. Thirdly, explanatory materials (texts and video tutorials) are prepared on each tool. Fourth, these materials are uploaded into a repository of our university in the form of an institutional blog, which is made available to students and the general public. Finally, a survey was designed to collect students’ feedback. The survey aimed to analyse the usefulness of the blog and the quality of the explanatory materials as well as the degree of usefulness that students assigned to each of the tools offered. In this paper, we present the results of the analysis of data received from 33 students in the 1st semester of the 21-22 academic year. One result we highlight in our paper is that the students have rated this resource very highly, in addition to offering very valuable information on the perceived usefulness of the applications provided for them to review. Our work, carried out within the framework of a teaching innovation project funded by our university, emphasizes that teachers need to design methodological strategies that help their students improve the quality of their productions written in English and, by extension, to improve their linguistic competence.

Keywords: academic blog, open access tools, online self-correction, written production in English, university learning

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Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

Procedia PDF Downloads 105

Authors: Felix Boehnisch, Alexander Lutz

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Based on the number of users and the amount of content posted daily, Facebook is considered the largest social network in the world. This content includes images or text posts from companies but also private persons, which are also commented on by other users. However, it can sometimes be difficult for companies to keep track of all the posts and the reactions to them, especially when there are several posts a day that contain hundreds to thousands of comments. To facilitate this, the following paper deals with the possible applications of sentiment analysis to social media comments in order to be able to support the work in social media marketing. In a first step, post comments were divided into positive and negative by a subjective rating, then the same comments were checked for their polarity value by the two german python libraries TextBlobDE and SentiWS and also grouped into positive, negative, or even neutral. As a control, the subjective classifications were compared with the machine-generated ones by a confusion matrix, and relevant quality criteria were determined. The accuracy of both libraries was not really meaningful, with 60% to 66%. However, many words or sentences were not evaluated at all, so there seems to be room for optimization to possibly get more accurate results. In future studies, the use of these specific German libraries can be optimized to gain better insights by either applying them to stricter cleaned data or by adding a sentiment value to emojis, which have been removed from the comments in advance, as they are not contained in the libraries.

Keywords: Facebook, German libraries, polarity, sentiment analysis, social media comments

Procedia PDF Downloads 160

Authors: Zebider Asire Munyelet, Yibeltal Chanie Manie

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In the modern digital landscape, the increase of social media platforms has become identical to the evolution of online consumer behavior. This study investigates the complicated relationship between social media and the purchasing decisions of online buyers. Through an extensive review of existing literature and empirical research, the aim is to comprehensively analyze the multidimensional impact that social media exerts on the various stages of the online buyer's journey. The investigation encompasses the exploration of how social media platforms serve as influential channels for information dissemination, product discovery, and consumer engagement. Additionally, the study investigates into the psychological aspects underlying the role of social media in shaping buyer preferences, perceptions, and trust in online transactions. The methodologies employed include both quantitative and qualitative analyses, incorporating surveys, interviews, and data analytics to derive meaningful insights. Statistical models are applied to distinguish patterns in online buyer behavior concerning product awareness, brand loyalty, and decision-making processes. The expected outcomes of this research contribute not only to the academic understanding of the dynamic interplay between social media and online buyer behavior but also offer practical implications for marketers, e-commerce platforms, and policymakers.

Keywords: consumer Behavior, social media, online purchasing, online transaction

Procedia PDF Downloads 46

Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Hamid Ebrahimy

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Large-scale land cover mapping has become a new challenge in land change and remote sensing field because of involving a big volume of data. Moreover, selecting the right classification method, especially when there are different types of landscapes in the study area is quite difficult. This paper is an attempt to compare the performance of different machine learning (ML) algorithms for generating a land cover map of the China-Central Asia–West Asia Corridor that is considered as one of the main parts of the Belt and Road Initiative project (BRI). The cloud-based Google Earth Engine (GEE) platform was used for generating a land cover map for the study area from Landsat-8 images (2017) by applying three frequently used ML algorithms including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The selected ML algorithms (RF, SVM, and ANN) were trained and tested using reference data obtained from MODIS yearly land cover product and very high-resolution satellite images. The finding of the study illustrated that among three frequently used ML algorithms, RF with 91% overall accuracy had the best result in producing a land cover map for the China-Central Asia–West Asia Corridor whereas ANN showed the worst result with 85% overall accuracy. The great performance of the GEE in applying different ML algorithms and handling huge volume of remotely sensed data in the present study showed that it could also help the researchers to generate reliable long-term land cover change maps. The finding of this research has great importance for decision-makers and BRI’s authorities in strategic land use planning.

Keywords: land cover, google earth engine, machine learning, remote sensing

Procedia PDF Downloads 101

Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

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The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

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Authors: Bruna Azzari Puga, Richard Roe, Andre Pagani de Souza

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abstract outlines a proposed study to examine how and to what extent interactive, experiential, learner centered methodology develops learning of basic civic and democratic competencies among young children. It stems from the Literacy and Law course taught at Georgetown University Law Center in Washington, DC, since 1998. Law students, trained in best literacy practices and legal cases affecting literacy development, read “law related” children’s books and engage in interactive and extension activities with emerging readers. The law students write a monthly journal describing their experiences and a final paper: a conventional paper or a children’s book illuminating some aspect of literacy and law. This proposal is based on the recent adaptation of Literacy and Law to Brazil at Mackenzie Presbyterian University in São Paulo in three forms: first, a course similar to the US model, often conducted jointly online with Brazilian and US law students; second, a similar course that combines readings of children’s literature with activity based learning, with law students from a satellite Mackenzie campus, for young children from a vulnerable community near the city; and third, a course taught by law students at the main Mackenzie campus for 4th grade students at the Mackenzie elementary school, that is wholly activity and discourse based. The workings and outcomes of these courses are well documented by photographs, reports, lesson plans, and law student journals. The authors, faculty who teach the above courses at Mackenzie and Georgetown, observe that literacy, broadly defined as cognitive and expressive development through reading and discourse-based activities, can be influential in developing democratic civic skills, identifiable by explicit civic competencies. For example, children experience justice in the classroom through cooperation, creativity, diversity, fairness, systemic thinking, and appreciation for rules and their purposes. Moreover, the learning of civic skills as well as the literacy skills is enhanced through interactive, learner centered practices in which the learners experience literacy and civic development. This study will develop rubrics for individual and classroom teaching and supervision by examining 1) the children’s books and students diaries of participating law students and 2) the collection of photos and videos of classroom activities, and 3) faculty and supervisor observations and reports. These rubrics, and the lesson plans and activities which are employed to advance the higher levels of performance outcomes, will be useful in training and supervision and in further replication and promotion of this form of teaching and learning. Examples of outcomes include helping, cooperating and participating; appreciation of viewpoint diversity; knowledge and utilization of democratic processes, including due process, advocacy, individual and shared decision making, consensus building, and voting; establishing and valuing appropriate rules and a reasoned approach to conflict resolution. In conclusion, further development and replication of the learner centered literacy and law practices outlined here can lead to improved qualities of democratic teaching and learning supporting mutual respect, positivity, deep learning, and the common good – foundation qualities of a sustainable world.

Keywords: democracy, law, learner-centered, literacy

Procedia PDF Downloads 97

Authors: Yaw Akoto

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The social construction of disability in a Ghanaian society has created a restriction on the development of the academic potentials of persons with disabilities. Ghanaian societal perceptions position persons with disabilities as needy, evil, feeble and 'abnormal' that a person with disability cannot contribute anything meaningful to their own development, society, and the nation as well. Almost all Ghanaian cultures believe the Gods visit evil people with disability as such they erect barriers that limit them to select and enroll in education. The few people with disabilities who gain admission to schools drop out due to these barriers erected by the society and institutions. However, there are very few of these students who are able to pursue their education at the higher education level despite these challenges. This qualitative study explores the motivation of students with disabilities to select and enroll in a Ghanaian university. The study used semi-structured interview to solicit information from students with disabilities in a Ghanaian university. Although the quality of students with disabilities experience was affected by culture, discrimination, marginalisation, and lack of support, the prospect of using themselves as role models, employment opportunities and family impingement were among others that pushed them to embark on their educational journey. The findings of this study have implications for societal and institutional levels for restructuring and refining societal perception and institutional policies on disabilities.

Keywords: beliefs, Ghanaian university, social construction, students with disabilities

Procedia PDF Downloads 125

Authors: Duangkamol Thitivesa

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This paper focuses on the use of project work as a pretext for applying the conventions of writing, or the correctness of mechanics, usage, and sentence formation, in a content-based class in a Rajabhat University. Its aim was to explore to what extent the student teachers’ academic achievement of the basic writing features against the 70% attainment target after the use of project is. The organization of work around an agreed theme in which the students reproduce language provided by texts and instructors is expected to enhance students’ correct writing conventions. The sample of the study comprised of 38 fourth-year English major students. The data was collected by means of achievement test and student writing works. The scores in the summative achievement test were analyzed by mean score, standard deviation, and percentage. It was found that the student teachers do more achieve of practicing mechanics and usage, and less in sentence formation. The students benefited from the exposure to texts during conducting the project; however, their automaticity of how and when to form phrases and clauses into simple/complex sentences had room for improvement.

Keywords: project-based learning, project work, writing conventions, academic achievement

Procedia PDF Downloads 314

Authors: Yan Zhang

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With the development of commercial movies, more and more instructors are drawn to adapt film pedagogy to teach history and culture. By challenging traditional standards of classroom culture, instruction through film represents an intersection of modernity and adaptability which is no longer optional but essential to maintaining educational accessibility. First, this presentation describes special features of the film that can be used in the classroom and help students acquire intercultural communicative competence (ICC) and achieve the learning goal. Second, the author brings forward the 5 A STAIRCASE model (Acknowledge-Adjust-Acculturate-Act-Assess) to explore how students acquire international communicative competence. Third, this article presents the intersections between new digital environments and classroom practice, such as how films can contribute to combining classical and contemporary Chinese cultures seamlessly and how film pedagogy can be an effective way to get students to engage in deeper critical thinking by exposing them to visuals, music, language, and styling which do not exist in traditional learning formats. Last, the student’s final video project will be exemplified at the end, demonstrating how to engage students in the analysis and experience of history and culture.

Keywords: intercultural education, curriculum, media, history

Procedia PDF Downloads 59

Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Seongryong Kang, Woonjin Kim, Sungjoo Lee

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Under the environment of fierce competition and globalized economy, international technology collaboration has gained increasing attention as a way to improve innovation efficiency. While international technology transfer helps a firm to acquire necessary technology in a short period of time, it also has a risk; embedding external technology from overseas partners may cause a transaction cost due to the regional, cultural and language barriers, which tend to offset the benefits of such transfer. Though a number of previous studies have focused on the effects of technology in-transfer on firm performance, few have conducted in the context of international technology transfer. To fill this gap, this study aims to investigate the impact of international technology in-transfer on firm performance – both innovation and financial performance, with a particular emphasis on the diversity and intensity of such transfer. To do this, we adopted technology balance payment (TBP) data of Korean firms from 2010 to 2011, where an intermediate regression analysis was used to identify the intermediate effects of absorptive capacity. The analysis results indicate that i) the diversity and intensity of international technology transfer influence innovation performance by improving R&D capability positively; and ii) the diversity has a positive impact but the intensity has a negative impact on financial performance through the intermediation of R&D intensity. The research findings are expected to provide meaningful implications for establishing global technology strategy and developing policy programs to facilitate technology transfer.

Keywords: diversity, intensity, international technology acquisition, performance, technology transfer

Procedia PDF Downloads 348

Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: Mojtaba Esmaeilzad Limoudehi, Ebrahim Amiri

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Water scarcity is now one of the leading challenges for human societies. In this regard, recognizing the relationship between soil, water, plant growth, and plant response to stress is very significant. In this paper, considering the importance of drought stress and the role of choosing suitable cultivars in resistance against drought, a split-plot experiment using early, intermediate, and late-maturing cultivars was carried out in Katul filed, Golestan province during two cultivation years of 2015 and 2016. The main factor was irrigation intervals at four levels, including 7 days, 14 days, 21 days, and 28 days. The subfactor was the subplot of six maize cultivars (two early maturing cultivars, two medium maturing cultivars, and two late-maturing cultivars). The results of variance analysis have revealed that irrigation interval and cultivars treatment have significant effects on the number of grain in each corn, number of rows in each corn, number of grain per row, the weight of 1000 grains, grain yield, and biomass yield. Although, the interaction of these two factors on the mentioned attributes was meaningful. The best grain yield was achieved at 7 days irrigation interval and late maturing maize cultivars treatment, which was equal to 12301 kg/ha.

Keywords: corn, growth period, optimization, stress

Procedia PDF Downloads 122

Authors: Sandesh Achar

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Artificial intelligence is being increasingly incorporated into many applications across various sectors such as health, education, security, and agriculture. Recently, there has been rapid development in cloud computing technology, resulting in AI’s implementation into cloud computing to enhance and optimize the technology service rendered. The deployment of AI in cloud-based applications has brought about autonomous computing, whereby systems achieve stated results without human intervention. Despite the amount of research into autonomous computing, work incorporating AI/ML into cloud computing to enhance its performance and resource allocation remain a fundamental challenge. This paper highlights different manifestations, roles, trends, and challenges related to AI-based cloud computing models. This work reviews and highlights excellent investigations and progress in the domain. Future directions are suggested for leveraging AI/ML in next-generation computing for emerging computing paradigms such as cloud environments. Adopting AI-based algorithms and techniques to increase operational efficiency, cost savings, automation, reducing energy consumption and solving complex cloud computing issues are the major findings outlined in this paper.

Keywords: artificial intelligence, cloud computing, deep learning, machine learning, internet of things

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Authors: Samantha Rix

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The purpose of this research paper is to investigate the cognitive processing of learners who receive input but produce very little or no output, and who, when they do produce output, exhibit a similar language proficiency as do those learners who produced output more regularly in the language classroom. Previous studies have investigated the benefits of output (with somewhat differing results); therefore, the presentation will begin with an investigation of what may underlie gains in proficiency without output. Consequently, a pilot study was designed and conducted to gain insight into the cognitive processing of low-output language learners looking, for example, at quantity and quality of noticing. This will be carried out within the paradigm of action classroom research, observing and interviewing low-output language learners in an intensive English program at a small Midwest university. The results of the pilot study indicated that autonomy in language learning, specifically utilizing strategies such self-monitoring, self-talk, and thinking 'out-loud', were crucial in the development of language proficiency for academic-level performance. The presentation concludes with an examination of pedagogical implication for classroom use in order to aide students in their language development.

Keywords: cognitive processing, language learners, language proficiency, learning strategies

Procedia PDF Downloads 453

Authors: A. Eiguren-Munitis, N. Berasategi, J. M. Correa

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Changes in family structures, immigration, economic crisis, among others, hinder the connection between different generations. This situation gives rise to a greater lack of social protection of the groups in vulnerable situations, such as the elderly and children. There is a growing need to search for shared spaces where different generations manage to break negative stereotypes and interact with each other. The school environment provides a favourable context in which the approach of different generations can be worked on. The intergenerational experiences that take place within the school context help to introduce the educational ideology for a lifetime. This induces bilateral learning, which encourages citizen participation. For this reason, the general objective of this research is to deepen the impact that intergenerational experiences have on participating students. The research is carried out based on mixed methods. The qualitative and quantitative evaluation included pre-test and post-test questionnaires (n=148) and group interviews (n=43). The results indicate that the intergenerational experiences influence different levels, on the one hand, help to promote school motivation and on the other hand, help to reduce negative stereotypes towards older people thus contributing to greater social cohesion.

Keywords: intergenerational learning, school, stereotypes, social cohesion

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Authors: Muhammad Ramzan

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STEM Education and Critical Thinking Skills are important 21st-century skills. STEM Education is necessary to promote secondary school students’ critical thinking skills. These skills are critical for teachers to respond to students. Pakistan is in the preliminary stages of integrating STEM Education in institutions like other developing countries. Unfortunately, most secondary school students in Pakistan are unaware of STEM Education and teachers are not applying critical thinking skills in classrooms. The study's objectives mainly deal with; to identify the importance of STEM Education in the teaching-learning process; to find out the factors affecting critical thinking skills that can develop interest in students in STEM Education and suggestions on how to improve critical thinking skills in students regarding STEM Education. This study was descriptive. The population of the study was secondary school students. Data was collected from 200 secondary school students through a questionnaire. The research results show that critical thinking skills develop interest in students towards STEM Education.

Keywords: STEM education, teachers, students, critical thinking skills, teaching and learning process

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Authors: Ahmed Abdelrahman, Ahmed Abdelraheem

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There are many essential factors affecting the integration of information and communication technology (ICT) into teaching and learning, including technology infrastructure, institutional support, professional development, and faculty members’ beliefs regarding ICT integration. The present research project investigated the current status of integrating ICT into teaching and learning at Sultan Qaboos University (SQU). A sample of 220 faculty members from six different colleges and four administrators from the Center of Educational Technology (CET) and the Center for Information Systems (CIS) at SQU in Oman were chosen, and quantitative, qualitative design using a semi-structured questionnaire, interviews and checklists were employed. The findings show that SQU had a high availability of ICT infrastructure in terms of hardware, software, and support services, as well as adequate computer labs for educational purposes. However, the results also indicated that, although SQU provided a series of professional development workshops related to using ICT in teaching, few faculty members were interested. Furthermore, the finding indicated that the degree of ICT integration into teaching at SQU was at a medium level.

Keywords: information and communication technology, integration, professional development, teaching

Procedia PDF Downloads 146

Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: Brenda Ravenscroft, Ulemu Luhanga, Bev King

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A comprehensive evaluation of online learning needs to include a blend of educational design, technology use, and online instructional practices that integrate technology appropriately for developing and delivering quality online courses. Research shows that classroom-based evaluation tools do not adequately capture the dynamic relationships between content, pedagogy, and technology in online courses. Furthermore, studies suggest that using classroom evaluations for online courses yields lower than normal scores for instructors, and may affect faculty negatively in terms of administrative decisions. In 2014, the Faculty of Arts and Science at Queen’s University responded to this evidence by seeking an alternative to the university-mandated evaluation tool, which is designed for classroom learning. The Faculty is deeply engaged in e-learning, offering large variety of online courses and programs in the sciences, social sciences, humanities and arts. This paper describes the process by which a new student survey instrument for online courses was developed and piloted, the methods used to analyze the data, and the ways in which the instrument was subsequently adapted based on the results. It concludes with a critical reflection on the challenges of evaluating e-learning. The Student Evaluation of Online Teaching Effectiveness (SEOTE), developed by Arthur W. Bangert in 2004 to assess constructivist-compatible online teaching practices, provided the starting point. Modifications were made in order to allow the instrument to serve the two functions required by the university: student survey results provide the instructor with feedback to enhance their teaching, and also provide the institution with evidence of teaching quality in personnel processes. Changes were therefore made to the SEOTE to distinguish more clearly between evaluation of the instructor’s teaching and evaluation of the course design, since, in the online environment, the instructor is not necessarily the course designer. After the first pilot phase, involving 35 courses, the results were analyzed using Stobart's validity framework as a guide. This process included statistical analyses of the data to test for reliability and validity, student and instructor focus groups to ascertain the tool’s usefulness in terms of the feedback it provided, and an assessment of the utility of the results by the Faculty’s e-learning unit responsible for supporting online course design. A set of recommendations led to further modifications to the survey instrument prior to a second pilot phase involving 19 courses. Following the second pilot, statistical analyses were repeated, and more focus groups were used, this time involving deans and other decision makers to determine the usefulness of the survey results in personnel processes. As a result of this inclusive process and robust analysis, the modified SEOTE instrument is currently being considered for adoption as the standard evaluation tool for all online courses at the university. Audience members at this presentation will be stimulated to consider factors that differentiate effective evaluation of online courses from classroom-based teaching. They will gain insight into strategies for introducing a new evaluation tool in a unionized institutional environment, and methodologies for evaluating the tool itself.

Keywords: evaluation, online courses, student survey, teaching effectiveness

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Authors: Mohamad Abdullah

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Recent Advancements in technology have stimulated deep change in the language learning classroom. Flipped classroom as a new pedagogical method is at the center of this change. It turns the classroom into a student-centered environment and promotes interactive and autonomous learning. The present study is an attempt to examine the effectiveness of the Flipped Classroom Model (FCM) on students’ motivation level in English speaking performance. This study was carried out with 27 undergraduate female English majors who enrolled in the course of Advanced Communication Skills (ENGL 154) at Buraimi University College (BUC). Data was collected through Motivation in English Speaking Performance Questionnaire (MESPQ) which has been distributed among the participants of this study pre and post the implementation of FCM. SPSS was used for analyzing data. The Paired T-Test which was carried out on the pre-post of (MESPQ) showed a significant difference between them (p < .009) that revealed participants’ tendency to increase their motivation level in English speaking performance after the application of FCM. In addition, respondents of the current study reported positive views about the implementation of FCM.

Keywords: english speaking performance, motivation, flipped classroom model, learner-contentedness

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Authors: Eman Mohamed Abdelwahab, Badreyya Alkhanbooli

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This study focuses on exploring the challenges encountered by English language teachers in same-ability English language classrooms in the United Arab Emirates public schools. This qualitative study uses open-ended questions for data collection from teacher participants. The study sample includes the participation of 60 English language teachers from 8 public schools across 4 emirates/cities in the United Arab Emirates. The study results highlight a number of challenges that are mostly encountered by English language teachers in their classrooms while teaching in same-ability classrooms, including lack of diversity in abilities, class-time limitation, difficulty in engaging all students (especially lower-achieving students), limited opportunities for peer learning and limited linguistic diversity. A set of suggestions is to be provided by participating teachers and researchers to improve the same-ability teaching and learning experience in English language classrooms.

Keywords: English language teaching, same ability grouping, ESL, English language learners

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Authors: Shanxiong Chen, Xu Han, Xiaolong Wang, Hui Ma

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Yi is an ethnic group mainly living in mainland China, with its own spoken and written language systems, after development of thousands of years. Ancient Yi is one of the six ancient languages in the world, which keeps a record of the history of the Yi people and offers documents valuable for research into human civilization. Recognition of the characters in ancient Yi helps to transform the documents into an electronic form, making their storage and spreading convenient. Due to historical and regional limitations, research on recognition of ancient characters is still inadequate. Thus, deep learning technology was applied to the recognition of such characters. Five models were developed on the basis of the four-layer convolutional neural network (CNN). Alpha-Beta divergence was taken as a penalty term to re-encode output neurons of the five models. Two fully connected layers fulfilled the compression of the features. Finally, at the softmax layer, the orthographic features of ancient Yi characters were re-evaluated, their probability distributions were obtained, and characters with features of the highest probability were recognized. Tests conducted show that the method has achieved higher precision compared with the traditional CNN model for handwriting recognition of the ancient Yi.

Keywords: recognition, CNN, Yi character, divergence

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Keywords: Levy flight, distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: Farzaneh Sarbandi Farahani

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Drought and decrease in the level of lakes in recent years due to global warming and excessive use of water resources feeding lakes are of great importance, and this research has provided a structure to investigate this issue. First, the information required for simulating lake drought is provided with strong references and necessary assumptions. Entity-Component-System (ECS) structure has been used for simulation, which can consider assumptions flexibly in simulation. Three major users (i.e., Industry, agriculture, and Domestic users) consume water from groundwater and surface water (i.e., streams, rivers and lakes). Lake Mead has been considered for simulation, and the information necessary to investigate its drought has also been provided. The results are presented in the form of a scenario-based design and optimal strategy selection. For optimal strategy selection, a deep reinforcement algorithm is developed to select the best set of strategies among all possible projects. These results can provide a better view of how to plan to prevent lake drought.

Keywords: drought simulation, Mead lake, entity component system programming, deep reinforcement learning

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Authors: Sibel Yoleri

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In this research it is aimed to determine the adjustment of students who attend the first class at elementary school to school in terms of several variables. The study group of the research consists of 286 students (131 female, 155 male) who continue attending the first class of elementary school in 2013-2014 academic year, in the city center of Uşak. In the research, ‘Personal Information Form’ and ‘Walker-Mcconnell Scale of Social Competence and School Adjustment’ have been used as data collection tools. In the analysis of data, the t-test has been applied in the independent groups to determine whether the sampling group students’ scores of school adjustment differ according to the sex variable or not. For the evaluation of data identified as not showing normal distribution, Mann Whitney U test has been applied for paired comparison, Kruskal Wallis H test has been used for multiple comparisons. In the research, all the statistical processes have been evaluated bidirectional and the level of significance has been accepted as .05. According to the results gathered from the research, a meaningful difference could not been identified in the level of students’ adjustment to school in terms of sex variable. At the end of the research, it is identified that the adjustment level of the students who have started school at the age of seven is higher than the ones who have started school at the age of five and the adjustment level of the students who have preschool education before the elementary school is higher than the ones who have not taken.

Keywords: starting school, preschool education, school adjustment, Walker-Mcconnell Scale

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Authors: Fakieh Alrabai

Abstract:

This paper presents an experimental study designed to explore some of the learner’s individual and interpersonal factors (e.g. persistence, interest, regulation, satisfaction, appreciation, etc.) that Saudi learners experience when learning English as a Foreign Language and how learners’ perceptions of these factors influence various aspects of their motivation to learn English language. As part of the study, a 27-item structured survey was administered to a randomly selected sample of 105 Saudi learners from public schools and universities. Data collected through the survey were subjected to some basic statistical analyses, such as "mean" and "standard deviation". Based on the results from the analysis, a number of generalizations and conclusions are made in relation to how these inherent factors affect Saudi learners’ motivation to learn English as a foreign language. In addition, some recommendations are offered to Saudi academics on how to effectively make use of such factors, which may enable Saudi teachers and learners of English as a foreign language to achieve better learning outcomes in an area widely associated by Saudis with lack of success.

Keywords: persistence, interest, appreciation, satisfaction, SL/FL motivation

Procedia PDF Downloads 400