Search results for: non-formal learning contexts
4238 AINA: Disney Animation Information as Educational Resources
Authors: Piedad Garrido, Fernando Repulles, Andy Bloor, Julio A. Sanguesa, Jesus Gallardo, Vicente Torres, Jesus Tramullas
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With the emergence and development of Information and Communications Technologies (ICTs), Higher Education is experiencing rapid changes, not only in its teaching strategies but also in student’s learning skills. However, we have noticed that students often have difficulty when seeking innovative, useful, and interesting learning resources for their work. This is due to the lack of supervision in the selection of good query tools. This paper presents AINA, an Information Retrieval (IR) computer system aimed at providing motivating and stimulating content to both students and teachers working on different areas and at different educational levels. In particular, our proposal consists of an open virtual resource environment oriented to the vast universe of Disney comics and cartoons. Our test suite includes Disney’s long and shorts films, and we have performed some activities based on the Just In Time Teaching (JiTT) methodology. More specifically, it has been tested by groups of university and secondary school students.Keywords: information retrieval, animation, educational resources, JiTT
Procedia PDF Downloads 3364237 Metacognitive Processing in Early Readers: The Role of Metacognition in Monitoring Linguistic and Non-Linguistic Performance and Regulating Students' Learning
Authors: Ioanna Taouki, Marie Lallier, David Soto
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Metacognition refers to the capacity to reflect upon our own cognitive processes. Although there is an ongoing discussion in the literature on the role of metacognition in learning and academic achievement, little is known about its neurodevelopmental trajectories in early childhood, when children begin to receive formal education in reading. Here, we evaluate the metacognitive ability, estimated under a recently developed Signal Detection Theory model, of a cohort of children aged between 6 and 7 (N=60), who performed three two-alternative-forced-choice tasks (two linguistic: lexical decision task, visual attention span task, and one non-linguistic: emotion recognition task) including trial-by-trial confidence judgements. Our study has three aims. First, we investigated how metacognitive ability (i.e., how confidence ratings track accuracy in the task) relates to performance in general standardized tasks related to students' reading and general cognitive abilities using Spearman's and Bayesian correlation analysis. Second, we assessed whether or not young children recruit common mechanisms supporting metacognition across the different task domains or whether there is evidence for domain-specific metacognition at this early stage of development. This was done by examining correlations in metacognitive measures across different task domains and evaluating cross-task covariance by applying a hierarchical Bayesian model. Third, using robust linear regression and Bayesian regression models, we assessed whether metacognitive ability in this early stage is related to the longitudinal learning of children in a linguistic and a non-linguistic task. Notably, we did not observe any association between students’ reading skills and metacognitive processing in this early stage of reading acquisition. Some evidence consistent with domain-general metacognition was found, with significant positive correlations between metacognitive efficiency between lexical and emotion recognition tasks and substantial covariance indicated by the Bayesian model. However, no reliable correlations were found between metacognitive performance in the visual attention span and the remaining tasks. Remarkably, metacognitive ability significantly predicted children's learning in linguistic and non-linguistic domains a year later. These results suggest that metacognitive skill may be dissociated to some extent from general (i.e., language and attention) abilities and further stress the importance of creating educational programs that foster students’ metacognitive ability as a tool for long term learning. More research is crucial to understand whether these programs can enhance metacognitive ability as a transferable skill across distinct domains or whether unique domains should be targeted separately.Keywords: confidence ratings, development, metacognitive efficiency, reading acquisition
Procedia PDF Downloads 1434236 Exploration of Influential Factors on First Year Architecture Students’ Productivity
Authors: Shima Nikanjam, Badiossadat Hassanpour, Adi Irfan Che Ani
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The design process in architecture education is based upon the Learning-by-Doing method, which leads students to understand how to design by practicing rather than studying. First-year design studios, as starting educational stage, provide integrated knowledge and skills of design for newly jointed architecture students. Within the basic design studio environment, students are guided to transfer their abstract thoughts into visual concrete decisions under the supervision of design educators for the first time. Therefore, introductory design studios have predominant impacts on students’ operational thinking and designing. Architectural design thinking is quite different from students’ educational backgrounds and learning habits. This educational challenge at basic design studios creates a severe need to study the reality of design education at foundation year and define appropriate educational methods with convenient project types with the intention of enhancing architecture education quality. Material for this study has been gathered through long-term direct observation at a first year second semester design studio at the faculty of architecture at EMU (known as FARC 102), fall and spring academic semester 2014-15. Distribution of a questionnaire among case study students and interviews with third and fourth design studio students who passed through the same methods of education in the past 2 years and conducting interviews with instructors are other methodologies used in this research. The results of this study reveal a risk of a mismatch between the implemented teaching method, project type and scale in this particular level and students’ learning styles. Although the existence of such risk due to varieties in students’ profiles could be expected to some extent, recommendations can support educators to reach maximum compatibility.Keywords: architecture education, basic design studio, educational method, forms creation skill
Procedia PDF Downloads 3644235 Mediation in Criminal Matters: A Perspective from Kosovo
Authors: Flutura Tahiraj, Emine Abdyli
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As a new alternative, mediation is integrated in the legislation of both developed and developing countries in Europe. Various researches in member states of the Council of Europe revealed obstacles, particularly related to the implementation of mediation in criminal matters. They are addressed through several recommendations and non-binding guidelines. However, there is limited empirical research on how the mediation in criminal matters is being implemented in the contexts of developing countries in South-Eastern Europe. Hence, the purpose of this qualitative study is to assess mediation in criminal matters in Kosovo by exploring how the main stakeholders describe the legal basis and implementation process and what it indicates for future practices. The data were gathered through 11 semi-structured interviews with judges, prosecutors, mediation clerks and mediators. Results show that laws and other guidelines that have been introduced since 2008 constitute a solid legal ground that facilitates mediation in criminal matters. The stakeholders are well aware of benefits mediation brings and express their willingness to advance its application to criminal matters. Results also indicate uncertainty among judges and prosecutors regarding the assessment and referral of certain criminal offences to mediation. To address it, specialized trainings, exchange programs and continuous monitoring and evaluation of the process could be supportive.Keywords: mediation in criminal matters, legislation, implementation of mediation
Procedia PDF Downloads 44234 A Theoretical Framework on International Voluntary Health Networks
Authors: Benet Reid, Nina Laurie, Matt Baillie-Smith
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Trans-national and tropical medicine, historically associated with colonial power and missionary activity, is now central to discourses of global health and development, thrust into mainstream media by events like the 2014 Ebola crisis and enshrined in the Sustainable Development Goals. Research in this area remains primarily the province of health professional disciplines, and tends to be framed within a simple North-to-South model of development. The continued role of voluntary work in this field is bound up with a rhetoric of partnering and partnership. We propose, instead, the idea of International Voluntary Health Networks (IVHNs) as a means to de-centre global-North institutions in these debates. Drawing on our empirical work with IVHNs in countries both North and South, we explore geographical and sociological theories for mapping the multiple spatial and conceptual dynamics of power manifested in these phenomena. We make a radical break from conventional views of health as a de-politicised symptom or corollary of social development. In studying health work as it crosses between cultures and contexts, we demonstrate the inextricably political nature of health and health work everywhere.Keywords: development, global health, power, volunteering
Procedia PDF Downloads 3174233 Collaborative Data Refinement for Enhanced Ionic Conductivity Prediction in Garnet-Type Materials
Authors: Zakaria Kharbouch, Mustapha Bouchaara, F. Elkouihen, A. Habbal, A. Ratnani, A. Faik
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Solid-state lithium-ion batteries have garnered increasing interest in modern energy research due to their potential for safer, more efficient, and sustainable energy storage systems. Among the critical components of these batteries, the electrolyte plays a pivotal role, with LLZO garnet-based electrolytes showing significant promise. Garnet materials offer intrinsic advantages such as high Li-ion conductivity, wide electrochemical stability, and excellent compatibility with lithium metal anodes. However, optimizing ionic conductivity in garnet structures poses a complex challenge, primarily due to the multitude of potential dopants that can be incorporated into the LLZO crystal lattice. The complexity of material design, influenced by numerous dopant options, requires a systematic method to find the most effective combinations. This study highlights the utility of machine learning (ML) techniques in the materials discovery process to navigate the complex range of factors in garnet-based electrolytes. Collaborators from the materials science and ML fields worked with a comprehensive dataset previously employed in a similar study and collected from various literature sources. This dataset served as the foundation for an extensive data refinement phase, where meticulous error identification, correction, outlier removal, and garnet-specific feature engineering were conducted. This rigorous process substantially improved the dataset's quality, ensuring it accurately captured the underlying physical and chemical principles governing garnet ionic conductivity. The data refinement effort resulted in a significant improvement in the predictive performance of the machine learning model. Originally starting at an accuracy of 0.32, the model underwent substantial refinement, ultimately achieving an accuracy of 0.88. This enhancement highlights the effectiveness of the interdisciplinary approach and underscores the substantial potential of machine learning techniques in materials science research.Keywords: lithium batteries, all-solid-state batteries, machine learning, solid state electrolytes
Procedia PDF Downloads 544232 Past, Present, and Future of Robotics Technology in Construction Industry (Literature Review)
Authors: Samira Haghbin, Behnam Daryayelaal, Zeinab Amiri
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As a result of rapid progress of technology in various industries, the only way to survive in a competitive market of business is to update one's situation along with the said developments. During recent decades, Robotics and automation of the construction operation has emerged as one of the important technologies grabbing the attention of various industries and specially the construction industry. Because of the coming labor shortage of the aging society in the near future, robots will be used in construction fields more than ever. By predicting the condition of Robotics in world's future construction industry, we can make necessary preparations to face with needs imposed by the time and stay ahead. This article takes a library study approach and presents a literature review of existing studies with an aim to investigate the use of robotics in past, present and future of construction industry and make predictions on its' growth and change process. Therefore, to make familiar with this kind of technology and its' requirements in the construction industry, the status of Robotics in construction industry of different countries of the world has been studied and necessary context for its' future progress is expressed. It is hoped that identifying needs and required contexts will facilitate further development of advanced technologies such as robotics industry and lead to more preparation for future.Keywords: future of robotics, construction industry, construction automation, trends of automation
Procedia PDF Downloads 3834231 Understanding Context and Its Effects in the Implementation of Modern Foreign Language Curriculum in Vietnam
Authors: Ngoc T. Bui
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The key issue for teachers of a modern foreign language is the creation of a pedagogic environment, and this means that an understanding of context is vital. A pedagogic environment addresses the following: time, feedback, relations with other people, curriculum integration, forms of knowledge, resources and control in the pedagogic relationship. In this light, the multiple case study of the implementation of a modern foreign language curriculum focuses on exploring Vietnamese contexts and participants’ perceptions of factors that may affect their implementation process in order to examine thoroughly how the communicative language teaching (CLT) curriculum is being implemented in second language classrooms. A mixed methods approach is utilized to investigate contextual and personal factors that may affect teachers’ implementation of curriculum and pedagogical reform in Vietnam. This project therefore has the capability to inform stakeholders of useful information and identify further changes and measures to solve potential problems to ensure the achievement of the curriculum goals. The expected outcomes may also lead to intercultural language teaching guidelines to support english as a foreign language (EFL) teachers with curriculum design, planning and how to create pedagogic environment to best implement it.Keywords: communicative language teaching, context, curriculum implementation, modern foreign language, pedagogic environment
Procedia PDF Downloads 2614230 Design-Based Elements to Sustain Participant Activity in Massive Open Online Courses: A Case Study
Authors: C. Zimmermann, E. Lackner, M. Ebner
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Massive Open Online Courses (MOOCs) are increasingly popular learning hubs that are boasting considerable participant numbers, innovative technical features, and a multitude of instructional resources. Still, there is a high level of evidence showing that almost all MOOCs suffer from a declining frequency of participant activity and fairly low completion rates. In this paper, we would like to share the lessons learned in implementing several design patterns that have been suggested in order to foster participant activity. Our conclusions are based on experiences with the ‘Dr. Internet’ MOOC, which was created as an xMOOC to raise awareness for a more critical approach to online health information: participants had to diagnose medical case studies. There is a growing body of recommendations (based on Learning Analytics results from earlier xMOOCs) as to how the decline in participant activity can be alleviated. One promising focus in this regard is instructional design patterns, since they have a tremendous influence on the learner’s motivation, which in turn is a crucial trigger of learning processes. Since Medieval Age storytelling, micro-learning units and specific comprehensible, narrative structures were chosen to animate the audience to follow narration. Hence, MOOC participants are not likely to abandon a course or information channel when their curiosity is kept at a continuously high level. Critical aspects that warrant consideration in this regard include shorter course duration, a narrative structure with suspense peaks (according to the ‘storytelling’ approach), and a course schedule that is diversified and stimulating, yet easy to follow. All of these criteria have been observed within the design of the Dr. Internet MOOC: 1) the standard eight week course duration was shortened down to six weeks, 2) all six case studies had a special quiz format and a corresponding resolution video which was made available in the subsequent week, 3) two out of six case studies were split up in serial video sequences to be presented over the span of two weeks, and 4) the videos were generally scheduled in a less predictable sequence. However, the statistical results from the first run of the MOOC do not indicate any strong influences on the retention rate, so we conclude with some suggestions as to why this might be and what aspects need further consideration.Keywords: case study, Dr. internet, experience, MOOCs, design patterns
Procedia PDF Downloads 2574229 Machine Learning Techniques in Bank Credit Analysis
Authors: Fernanda M. Assef, Maria Teresinha A. Steiner
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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines
Procedia PDF Downloads 974228 Mentees’ Agency in Practicum: A Qualitative Study of Two Teacher Education Programs in Vietnam
Authors: Tien Nguyen
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The relationship between mentors and mentees in teaching practicum has received the attention of researchers and been widely investigated. Mentors’ authority and power have captured a large and growing body of the literature in the field of teaching practicum. This article revisits mentor-mentee relationship and shifts the focus to mentees’ agency in planning and delivering lessons, an area which has been under-researched. Drawing on Vygotsky’s Zone of Proximal Development and Harré’s Positioning Theory, this qualitative study examines how mentees responded to mentors’ instructions in practicum. Interviews and classroom observations were conducted with 20 participants including both mentors and mentees across two English language teacher education programs in two different geographical locations in Vietnam. The result indicates that regardless of the similarities and/or differences of the programs, mentees’ agency varied in accordance with their identities in specific contexts. Specifically, mentees follow or resist to mentors’ feedback and instruction in revising their lesson plans and delivery these lessons, depending on their professional identities and institutional conditions. This study contributes to the importance of supporting the agency of mentees in teacher education.Keywords: mentors, mentees, relationship, agency, professional identity, teacher education
Procedia PDF Downloads 1374227 TutorBot+: Automatic Programming Assistant with Positive Feedback based on LLMs
Authors: Claudia Martínez-Araneda, Mariella Gutiérrez, Pedro Gómez, Diego Maldonado, Alejandra Segura, Christian Vidal-Castro
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The purpose of this document is to showcase the preliminary work in developing an EduChatbot-type tool and measuring the effects of its use aimed at providing effective feedback to students in programming courses. This bot, hereinafter referred to as tutorBot+, was constructed based on chatGPT and is tasked with assisting and delivering timely positive feedback to students in the field of computer science at the Universidad Católica de Concepción. The proposed working method consists of four stages: (1) Immersion in the domain of Large Language Models (LLMs), (2) Development of the tutorBot+ prototype and integration, (3) Experiment design, and (4) Intervention. The first stage involves a literature review on the use of artificial intelligence in education and the evaluation of intelligent tutors, as well as research on types of feedback for learning and the domain of chatGPT. The second stage encompasses the development of tutorBot+, and the final stage involves a quasi-experimental study with students from the Programming and Database labs, where the learning outcome involves the development of computational thinking skills, enabling the use and measurement of the tool's effects. The preliminary results of this work are promising, as a functional chatBot prototype has been developed in both conversational and non-conversational versions integrated into an open-source online judge and programming contest platform system. There is also an exploration of the possibility of generating a custom model based on a pre-trained one tailored to the domain of programming. This includes the integration of the created tool and the design of the experiment to measure its utility.Keywords: assessment, chatGPT, learning strategies, LLMs, timely feedback
Procedia PDF Downloads 614226 Indigenous Engagement: Towards a Culturally Sensitive Approach for Inclusive Economic Development
Authors: Karla N. Penna, Eloise J. Hoffman, Tonya R. Carter
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This paper suggests that effective cultural landscape management plans in an Indigenous context should be undertaken using multidisciplinary approach taken into consideration context-related social and cultural aspects. In relation to working in Indigenous and mining contexts, we draw upon and contribute to International policies on human rights that promote the development of management plans on that are co-designed through genuine engagement processes. We suggest that the production of management plans that are built upon culturally relevant frameworks, lead to more inclusive economic development, a greater sense of trust, and shared managerial responsibilities. In this paper, three issues related to Indigenous engagement and cultural landscape management plans will be addressed: (1) the need for effective communication channels between proponents and Traditional Owners (Australian original Aboriginal peoples who inhabited specific regions), (2) the use of a culturally sensitive approach to engage local representatives in the decision making processes, and (3) how design of new management plans can help in establishing shared management.Keywords: culture-centred approach, Holons’ hierarchy, inclusive economic development, indigenous engagement
Procedia PDF Downloads 1934225 Use of Machine Learning Algorithms to Pediatric MR Images for Tumor Classification
Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos
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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology
Procedia PDF Downloads 1434224 Generating Swarm Satellite Data Using Long Short-Term Memory and Generative Adversarial Networks for the Detection of Seismic Precursors
Authors: Yaxin Bi
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Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors
Procedia PDF Downloads 244223 Corporate Cautionary Statement: A Genre of Professional Communication
Authors: Chie Urawa
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Cautionary statements or disclaimers in corporate annual reports need to be carefully designed because clear cautionary statements may protect a company in the case of legal disputes and may undermine positive impressions. This study compares the language of cautionary statements using two corpora, Sony’s cautionary statement corpus (S-corpus) and Panasonic’s cautionary statement corpus (P-corpus), illustrating the differences and similarities in relation to the use of meaningful cautionary statements and critically analyzing why practitioners use the way. The findings describe the distinct differences between the two companies in the presentation of the risk factors and the way how they make the statements. The word ability is used more for legal protection in S-corpus whereas the word possibility is used more to convey a better impression in P-corpus. The main similarities are identified in the use of lexical words and pronouns, and almost the same wordings for eight years. The findings show how they make the statements unique to the company in the presentation of risk factors, and the characteristics of specific genre of professional communication. Important implications of this study are that more comprehensive approach can be applied in other contexts, and be used by companies to reflect upon their cautionary statements.Keywords: cautionary statements, corporate annual reports, corpus, risk factors
Procedia PDF Downloads 1624222 People Who Live in Poverty Usually Do So Due to Circumstances Far Beyond Their Control: A Multiple Case Study on Poverty Simulation Events
Authors: Tracy Smith-Carrier
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Burgeoning research extols the benefits of innovative experiential learning activities to increase participants’ engagement, enhance their individual learning, and bridge the gap between theory and practice. This presentation discusses findings from a multiple case study on poverty simulation events conducted with two samples: undergraduate students and community participants. After exploring the nascent research on the benefits and limitations of poverty simulation activities, the study explores whether participating in a poverty simulation resulted in changes to participants’ beliefs about the causes and effects of poverty, as well as shifts in their attitudes and actions toward people experiencing poverty. For the purposes of triangulation, quantitative and qualitative data from a variety of sources were analyzed: participant feedback surveys, qualitative responses, and pre, post, and follow-up questionnaires. Findings show statistically significant results (p<.05) from both samples on cumulative scores of the modified Attitudes Toward Poverty Scale, indicating an improvement in participants’ attitudes toward poverty. Although generally positive about their experiences, participating in the simulation did not appear to have prompted participants to take specific actions to reduce poverty. Conclusions drawn from the research study suggest that poverty simulation planners should be wary of adopting scenarios that emphasize, or fail to adequately contextualize, behaviours or responses that might perpetuate individual explanations of poverty. Moreover, organizers must carefully consider how to ensure participants in their audience currently experiencing low-income do not become emotionally distressed, triggered or further marginalized in the process. While overall participants were positive about their experiences in the simulation, the events did not appear to have prompted them to action. Moving beyond the goal of increasing participants’ understandings of poverty, interventions that foster greater engagement in poverty issues over the long-term are necessary.Keywords: empathy, experiential learning, poverty awareness, poverty simulation
Procedia PDF Downloads 2624221 Use of Social Networks and Mobile Technologies in Education
Authors: Václav Maněna, Roman Dostál, Štěpán Hubálovský
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Social networks play an important role in the lives of children and young people. Along with the high penetration of mobile technologies such as smartphones and tablets among the younger generation, there is an increasing use of social networks already in elementary school. The paper presents the results of research, which was realized at schools in the Hradec Králové region. In this research, the authors focused on issues related to communications on social networks for children, teenagers and young people in the Czech Republic. This research was conducted at selected elementary, secondary and high schools using anonymous questionnaires. The results are evaluated and compared with the results of the research, which has been realized in 2008. The authors focused on the possibilities of using social networks in education. The paper presents the possibility of using the most popular social networks in education, with emphasis on increasing motivation for learning. The paper presents comparative analysis of social networks, with regard to the possibility of using in education as well.Keywords: social networks, motivation, e-learning, mobile technology
Procedia PDF Downloads 3054220 The Development of Home-Based Long Term Care Model among Thai Elderly Dependent
Authors: N. Uaphongsathorn, C. Worawong, S. Thaewpia
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Background and significance: The population is aging in Thai society, the elderly dependent is at great risk of various functional, psychological, and socio-economic problems as well as less access to health care. They may require long term care at home to maximize their functional abilities and activities of daily living and to improve their quality of life during their own age. Therefore, there is a need to develop a home-based long term care to meet the long term care needs of elders dependent. Methods: The research purpose was to develop long term care model among the elderly dependent in Chaiyaphum province in Northeast region of Thailand. Action Research which is composing of planning, action, observation, and reflection phases was used. Research was carried out for 12 months in all sub-districts of 6 districts in Chaiyaphum province. Participants (N = 1,010) participating in the processes of model development were comprised of 3 groups: a) a total of 110 health care professionals, b) a total of 600 health volunteers and family caregivers and c) a total of 300 the elderly dependent with chronically medical illnesses or disabilities. Descriptive statistics and content analysis were used to analyze data. Findings: Results have shown that the most common health problems among elders dependent with physical disabilities to function independently were cardiovascular disease, dementia, and traffic injuries. The development of home-based long term care model among elders dependent in Chaiyaphum province was composed of six key steps. They are: a) initiating policies supporting formal and informal caregivers for the elder dependent in all sub-districts, b) building network and multidisciplinary team, c) developing 3-day care manager training program and 3-day care provider training program d) training case managers and care providers for the elderly dependent through team and action learning, e) assessing, planning and providing care based on care individual’s needs of the elderly dependent, and f) sharing experiences for good practice and innovation for long term care at homes in district urban and rural areas. Among all care managers and care providers, the satisfaction level for training programs was high with a mean score of 3.98 out of 5. The elders dependent and family caregivers addressed that long term care at home could contribute to improving life’s daily activities, family relationship, health status, and quality of life. Family caregivers and volunteers have feeling a sense of personal satisfaction and experiencing providing meaningful care and support for elders dependent. Conclusion: In conclusion, a home-based long term care is important to Thai elders dependent. Care managers and care providers play a large role and responsibility to provide appropriate care to meet the elders’ needs in both urban and rural areas in Thai society. Further research could be rigorously studied with a larger group of populations in similar socio-economic and cultural contexts.Keywords: elderly people, care manager, care provider, long term care
Procedia PDF Downloads 2974219 Machine Learning Techniques for COVID-19 Detection: A Comparative Analysis
Authors: Abeer A. Aljohani
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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network
Procedia PDF Downloads 864218 Applying Epistemology to Artificial Intelligence in the Social Arena: Exploring Fundamental Considerations
Authors: Gianni Jacucci
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Epistemology traditionally finds its place within human research philosophies and methodologies. Artificial intelligence methods pose challenges, particularly given the unresolved relationship between AI and pivotal concepts in social arenas such as hermeneutics and accountability. We begin by examining the essential criteria governing scientific rigor in the human sciences. We revisit the three foundational philosophies underpinning qualitative research methods: empiricism, hermeneutics, and phenomenology. We elucidate the distinct attributes, merits, and vulnerabilities inherent in the methodologies they inspire. The integration of AI, e.g., deep learning algorithms, sparks an interest in evaluating these criteria against the diverse forms of AI architectures. For instance, Interpreted AI could be viewed as a hermeneutic approach, relying on a priori interpretations, while straight AI may be perceived as a descriptive phenomenological approach, processing original and uncontaminated data. This paper serves as groundwork for such explorations, offering preliminary reflections to lay the foundation and outline the initial landscape.Keywords: artificial intelligence, deep learning, epistemology, qualitative research, methodology, hermeneutics, accountability
Procedia PDF Downloads 184217 Perceived Influence of Information Communication Technology on Empowerment Amongst the College of Education Physical and Health Education Students in Oyo State
Authors: I. O. Oladipo, Olusegun Adewale Ajayi, Omoniyi Oladipupo Adigun
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Information Communication Technology (ICT) have the potential to contribute to different facets of educational development and effective learning; expanding access, promoting efficiency, improve the quality of learning, enhancing the quality of teaching and provide important mechanism for the economic crisis. Considering the prevalence of unemployment among the higher institution graduates in this nation, in which much seems not to have been achieved in this direction. In view of this, the purpose of this study is to create an awareness and enlightenment of ICT for empowerment opportunities after school. A self-developed modified 4-likert scale questionnaire was used for data collection among Colleges of Education, Physical and Health Education students in Oyo State. Inferential statistical analysis of chi-square set at 0.05 alpha levels was used to analyze the stated hypotheses. The study concludes that awareness and enlightenment of ICT significantly influence empowerment opportunities and recommended that college of education students should be encouraged on the application of ICT for job opportunity after school.Keywords: employment, empowerment, information communication technology, physical education
Procedia PDF Downloads 3834216 Difficulties Faced by the Sports Clubs in the Sultanate of Oman
Authors: Majid Al-Busafi, Almur Al-Hashmi
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The aim of this study was to identify the difficulties of planning and organizational, technical and finance facing sports clubs in the Sultanate of Oman. To answer the research questions, the researchers set up and developed a questionnaire as a major tool for the study. The researchers also conducted field visits to targeted clubs, collecting documents and publications related to the current situation of these clubs. The study sample (totaling 231) was selected of boards members of sports clubs and federations, executive staff of the Ministry of Sports Affairs, sports clubs and federations, and the media. The results indicated that the order of the difficulties faced by sports clubs is as follows: planning difficulties, the financial difficulties, technical difficulties and fourth and finally organizational difficulties. The study recommended the need to develop long-term plans and a timetable for the activities of the federations or the clubs. It is also important not to ignore to plan the qualification activities for the workers in the federations and clubs. Finally, there is a need to develop regulated forms of relations between members in the clubs. This study makes an original contribution to knowledge as it addresses needs in a country with no clear clubs systems and is informed by case studies from other countries, two of which have similar cultural contexts.Keywords: sports club, sports federation, difficulties, plans
Procedia PDF Downloads 4644215 Emotional Intelligence and Age in Open Distance Learning
Authors: Naila Naseer
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Emotional Intelligence (EI) concept is not new yet unique and interesting. EI is a person’s ability to be aware of his/her own emotions and to manage, handle and communicate emotions with others effectively. The present study was conducted to assess the relationship between emotional intelligence and age of graduate level students at Allama Iqbal Open University (AIOU). Population consisted of Allama Iqbal Open University students (B.Ed 3rd Semester, Autumn 2007) from Rawalpindi and Islamabad regions. Total number of sample consisted of 469 participants was randomly drawn out by using table of random numbers. Bar-On EQ-i was administered on the participants through personal contact. The instrument was also validated through pilot study on a random sample of 50 participants (B.Ed students Spring 2006), who had completed their B.Ed degree successfully. Data was analyzed and tabulated in percentages, frequencies, mean, standard deviation, correlation, and scatter gram in SPSS (version 16.0 for windows). The results revealed that students with higher age group had scored low on the scale (Bar-On EQ-i). Moreover, the students in low age groups exhibited higher levels of EI as compared with old age students.Keywords: emotional intelligence, age level, learning, emotion-related feelings
Procedia PDF Downloads 3244214 New Advanced Medical Software Technology Challenges and Evolution of the Regulatory Framework in Expert Software, Artificial Intelligence, and Machine Learning
Authors: Umamaheswari Shanmugam, Silvia Ronchi, Radu Vornicu
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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that are able to use the large amount and variety of data generated during healthcare services every day. As we read the news, over 500 machine learning or other artificial intelligence medical devices have now received FDA clearance or approval, the first ones even preceding the year 2000. One of the big advantages of these new technologies is the ability to get experience and knowledge from real-world use and to continuously improve their performance. Healthcare systems and institutions can have a great benefit because the use of advanced technologies improves the same time efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and also to protect patients’ safety. The evolution and the continuous improvement of software used in healthcare must take into consideration the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device approval, but they are necessary to ensure performance, quality, and safety, and at the same time, they can be a business opportunity if the manufacturer is able to define in advance the appropriate regulatory strategy. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems.
Procedia PDF Downloads 864213 A Machine Learning-Based Model to Screen Antituberculosis Compound Targeted against LprG Lipoprotein of Mycobacterium tuberculosis
Authors: Syed Asif Hassan, Syed Atif Hassan
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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction
Procedia PDF Downloads 3844212 Virtual Academy Next: Addressing Transition Challenges Through a Gamified Virtual Transition Program for Students with Disabilities
Authors: Jennifer Gallup, Joel Bocanegra, Greg Callan, Abigail Vaughn
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Students with disabilities (SWD) engaged in a distance summer program delivered over multiple virtual mediums that used gaming principles to teach and practice self-regulated learning (SRL) through the process of exploring possible jobs. Gaming quests were developed to explore jobs and teach transition skills. Students completed specially designed quests that taught and reinforced SRL and problem-solving through individual, group, and teacher-led experiences. SRL skills learned were reinforced through guided job explorations over the context of MinecraftEDU, zoom with experts in the career, collaborations with a team over Marco Polo, and Zoom. The quests were developed and laid out on an accessible web page, with active learning opportunities and feedback conducted within multiple virtual mediums including MinecraftEDU. Gaming mediums actively engage players in role-playing, problem-solving, critical thinking, and collaboration. Gaming has been used as a medium for education since the inception of formal education. Games, and specifically board games, are pre-historic, meaning we had board games before we had written language. Today, games are widely used in education, often as a reinforcer for behavior or for rewards for work completion. Games are not often used as a direct method of instruction and assessment; however, the inclusion of games as an assessment tool and as a form of instruction increases student engagement and participation. Games naturally include collaboration, problem-solving, and communication. Therefore, our summer program was developed using gaming principles and MinecraftEDU. This manuscript describes a virtual learning summer program called Virtual Academy New and Exciting Transitions (VAN) that was redesigned from a face-to-face setting to a completely online setting with a focus on SWD aged 14-21. The focus of VAN was to address transition planning needs such as problem-solving skills, self-regulation, interviewing, job exploration, and communication for transition-aged youth diagnosed with various disabilities (e.g., learning disabilities, attention-deficit hyperactivity disorder, intellectual disability, down syndrome, autism spectrum disorder).Keywords: autism, disabilities, transition, summer program, gaming, simulations
Procedia PDF Downloads 694211 Elaboration and Validation of a Survey about Research on the Characteristics of Mentoring of University Professors’ Lifelong Learning
Authors: Nagore Guerra Bilbao, Clemente Lobato Fraile
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This paper outlines the design and development of the MENDEPRO questionnaire, designed to analyze mentoring performance within a professional development process carried out with professors at the University of the Basque Country, Spain. The study took into account the international research carried out over the past two decades into teachers' professional development, and was also based on a thorough review of the most common instruments used to identify and analyze mentoring styles, many of which fail to provide sufficient psychometric guarantees. The present study aimed to gather empirical data in order to verify the metric quality of the questionnaire developed. To this end, the process followed to validate the theoretical construct was as follows: The formulation of the items and indicators in accordance with the study variables; the analysis of the validity and reliability of the initial questionnaire; the review of the second version of the questionnaire and the definitive measurement instrument. Content was validated through the formal agreement and consensus of 12 university professor training experts. A reduced sample of professors who had participated in a lifelong learning program was then selected for a trial evaluation of the instrument developed. After the trial, 18 items were removed from the initial questionnaire. The final version of the instrument, comprising 33 items, was then administered to a sample group of 99 participants. The results revealed a five-dimensional structure matching theoretical expectations. Also, the reliability data for both the instrument as a whole (.98) and its various dimensions (between .91 and .97) were very high. The questionnaire was thus found to have satisfactory psychometric properties and can therefore be considered apt for studying the performance of mentoring in both induction programs for young professors and lifelong learning programs for senior faculty members.Keywords: higher education, mentoring, professional development, university teaching
Procedia PDF Downloads 1744210 Digital Platform of Crops for Smart Agriculture
Authors: Pascal François Faye, Baye Mor Sall, Bineta Dembele, Jeanne Ana Awa Faye
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In agriculture, estimating crop yields is key to improving productivity and decision-making processes such as financial market forecasting and addressing food security issues. The main objective of this paper is to have tools to predict and improve the accuracy of crop yield forecasts using machine learning (ML) algorithms such as CART , KNN and SVM . We developed a mobile app and a web app that uses these algorithms for practical use by farmers. The tests show that our system (collection and deployment architecture, web application and mobile application) is operational and validates empirical knowledge on agro-climatic parameters in addition to proactive decision-making support. The experimental results obtained on the agricultural data, the performance of the ML algorithms are compared using cross-validation in order to identify the most effective ones following the agricultural data. The proposed applications demonstrate that the proposed approach is effective in predicting crop yields and provides timely and accurate responses to farmers for decision support.Keywords: prediction, machine learning, artificial intelligence, digital agriculture
Procedia PDF Downloads 744209 Evaluation of the CRISP-DM Business Understanding Step: An Approach for Assessing the Predictive Power of Regression versus Classification for the Quality Prediction of Hydraulic Test Results
Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter
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Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.Keywords: classification, CRISP-DM, machine learning, predictive quality, regression
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