Search results for: non-formal learning contexts
4598 Utilization of Cloud-Based Learning Platform for the Enhancement of IT Onboarding System
Authors: Christian Luarca
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The study aims to define the efficiency of e-Trainings by the use of cloud platform as part of the onboarding process for IT support engineers. Traditional lecture based trainings involves human resource to guide and assist new hires as part of onboarding which takes time and effort. The use of electronic medium as a platform for training provides a two-way basic communication that can be done in a repetitive manner. The study focuses on determining the most efficient manner of learning the basic knowledge on IT support in the shortest time possible. This was determined by conducting the same set of knowledge transfer categories in two different approaches, one being the e-Training and the other using the traditional method. Performance assessment will be done by the use of Service Tracker Assessment (STA) Tool and Service Manager. Data gathered from this ongoing study will promote the utilization of e-Trainings in the IT onboarding process.Keywords: cloud platform, e-Training, efficiency, onboarding
Procedia PDF Downloads 1464597 Incorporating Morality Standards in eLearning Process at INU
Authors: Khader Musbah Titi
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In this era, traditional education systems do not meet the new challenges created by emerging technologies. On the other hand, eLearning offers all the necessary tools to meet these challenges. Using the Internet has brought numerous benefits to most educational institutions; it has also stretched traditional problems of plagiarism, cheating, stealing, vandalism, and spying into the cyberspace. This research discusses these issues in an eLearning environment. It attempts to provide suggestions and possible solutions to some of these issues. The main aim of this research is to conduct a survey at Irbid National University (INU), one of the oldest and biggest universities in Jordan, to study information related to moral and ethical issues in e-learning environment that affect the construction of the students’ characters in the future. The study will focus on student’s behavior and actions through the Internet using Learning Management System (LMS). Another aim of this research is to analyze the opinions of the instructors and last year students at INU about ethical behavior and interaction through LMS. The results show that educational institutes that use LMS should focus on student character development along with field knowledge. According to disadvantages, the results of the study showed that most of students behave unethically in their online activities (cheating, plagiarism, copy/paste etc.) while studying online courses through LMS. The result showed that instructors play a major role in the character development of students. The result also showed that academic institute must have variant mechanisms and strict policy in LMS to control unethical actions of students.Keywords: LMS, cyber ethics, e-learning, IT ethics, students’ behaviors
Procedia PDF Downloads 2374596 Investigating the Potential of a Blended Format for the Academic Reading Module Course Redesign
Authors: Reham Niazi, Marwa Helmy, Susanne Rizzo
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This classroom action research is designed to explore the possibility of adding effective online content to supplement and add learning value to the current reading module. The aim of this research was two-fold, first to investigate students’ acceptance of and interactivity with online components, chosen to orient students with the content, and to pave the way for more in-class activities and skill practice. Secondly, the instructor aimed to examine students’ willingness to have the course contact hours remain the same with some online components to be done at home (flipped approach) or if students were open to turn the class into a blended format with two scenarios; either to have the current contact hours and apply the blended and in this case the face to face component will be less or keep the number of face to face classes the same and add more online structured classes as part of the course hours.Keywords: blended learning, flipped classroom, graduate students, education
Procedia PDF Downloads 1714595 The Model of Learning Centre on OTOP Production Process Based on Sufficiency Economic Philosophy for Sustainable Life Quality
Authors: Napasri Suwanajote
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The purposes of this research were to analyse and evaluate successful factors in OTOP production process for the developing of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The research has been designed as a qualitative study to gather information from 30 OTOP producers in Bangkontee District, Samudsongkram Province. They were all interviewed on 3 main parts. Part 1 was about the production process including 1) production 2) product development 3) the community strength 4) marketing possibility and 5) product quality. Part 2 evaluated appropriate successful factors including 1) the analysis of the successful factors 2) evaluate the strategy based on Sufficiency Economic Philosophy and 3) the model of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The results showed that the production did not affect the environment with potential in continuing standard quality production. They used the raw materials in the country. On the aspect of product and community strength in the past 1 year, it was found that there was no appropriate packaging showing product identity according to global market standard. They needed the training on packaging especially for food and drink products. On the aspect of product quality and product specification, it was found that the products were certified by the local OTOP standard. There should be a responsible organization to help the uncertified producers pass the standard. However, there was a problem on food contamination which was hazardous to the consumers. The producers should cooperate with the government sector or educational institutes involving with food processing to reach FDA standard. The results from small group discussion showed that the community expected high education and better standard living. Some problems reported by the community included informal debt and drugs in the community. There were 8 steps in developing the model of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality.Keywords: production process, OTOP, sufficiency economic philosophy, marketing management
Procedia PDF Downloads 2254594 Soybean Seed Composition Prediction From Standing Crops Using Planet Scope Satellite Imagery and Machine Learning
Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi
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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.Keywords: agriculture, computer vision, data science, geospatial technology
Procedia PDF Downloads 1274593 A Stepwise Approach to Automate the Search for Optimal Parameters in Seasonal ARIMA Models
Authors: Manisha Mukherjee, Diptarka Saha
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Reliable forecasts of univariate time series data are often necessary for several contexts. ARIMA models are quite popular among practitioners in this regard. Hence, choosing correct parameter values for ARIMA is a challenging yet imperative task. Thus, a stepwise algorithm is introduced to provide automatic and robust estimates for parameters (p; d; q)(P; D; Q) used in seasonal ARIMA models. This process is focused on improvising the overall quality of the estimates, and it alleviates the problems induced due to the unidimensional nature of the methods that are currently used such as auto.arima. The fast and automated search of parameter space also ensures reliable estimates of the parameters that possess several desirable qualities, consequently, resulting in higher test accuracy especially in the cases of noisy data. After vigorous testing on real as well as simulated data, the algorithm doesn’t only perform better than current state-of-the-art methods, it also completely obviates the need for human intervention due to its automated nature.Keywords: time series, ARIMA, auto.arima, ARIMA parameters, forecast, R function
Procedia PDF Downloads 1564592 Springback Prediction for Sheet Metal Cold Stamping Using Convolutional Neural Networks
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Cold stamping has been widely applied in the automotive industry for the mass production of a great range of automotive panels. Predicting the springback to ensure the dimensional accuracy of the cold-stamped components is a critical step. The main approaches for the prediction and compensation of springback in cold stamping include running Finite Element (FE) simulations and conducting experiments, which require forming process expertise and can be time-consuming and expensive for the design of cold stamping tools. Machine learning technologies have been proven and successfully applied in learning complex system behaviours using presentative samples. These technologies exhibit the promising potential to be used as supporting design tools for metal forming technologies. This study, for the first time, presents a novel application of a Convolutional Neural Network (CNN) based surrogate model to predict the springback fields for variable U-shape cold bending geometries. A dataset is created based on the U-shape cold bending geometries and the corresponding FE simulations results. The dataset is then applied to train the CNN surrogate model. The result shows that the surrogate model can achieve near indistinguishable full-field predictions in real-time when compared with the FE simulation results. The application of CNN in efficient springback prediction can be adopted in industrial settings to aid both conceptual and final component designs for designers without having manufacturing knowledge.Keywords: springback, cold stamping, convolutional neural networks, machine learning
Procedia PDF Downloads 1374591 Using Demonstration Method of Teaching Sewing to Improve the Skills of Form 3 Fashion Designing Students: A Case of Baworo Integrated Community Center for Employable Skills (Bicces)
Authors: Aboagye Boye Gilbert
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Teaching and learning (Education), not only in Ghana but the whole world is regarded as the (Stepping stone) vehicle to accelerate the country’s economy, development and social growth. Basically the ingredients for human development and the country in general is Vocational and Technical education and this has been stressed in Ghana’s education system since Pre-independence. To this effect, this research seeks to determine using demonstration method of Teachings sewing to improve the skills of form 3 Fashion Designing students of Baworo Integrated Community Centre for Employable Skills. In this research, reviewed literature on opinions of other researchers and what other people have done and said on related articles or topics, analyzed the research design used, translate the data gathered in the study. The study was design to gather information from the school on how they use Teaching methods to teach sewing. The targeted respondent contacted to give assistance Consist of students from BICCES, fashion teachers and tailored garment makers. The sample size consisted of 5 teachers, 20 students and 5 tailors were selected to answer questionnaire items that were used to gather the data for the study. The study revealed that most teachers and students agreed to the fact that demonstration, teaching and learning materials had a positive attitude towards the students in learning sewing. The study recommends that there should be more mechanisms in place to serve as a guide.Keywords: VOTEC, BECE, BICCES, SHS
Procedia PDF Downloads 674590 Predictive Analysis of Chest X-rays Using NLP and Large Language Models with the Indiana University Dataset and Random Forest Classifier
Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh
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This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems
Procedia PDF Downloads 334589 Relationship between ISO 14001 and Market Performance of Firms in China: An Institutional and Market Learning Perspective
Authors: Hammad Riaz, Abubakr Saeed
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Environmental Management System (EMS), i.e., ISO 14001 helps to build corporate reputation, legitimacy and can also be considered as firms’ strategic response to institutional pressure to reduce the impact of business activity on natural environment. The financial outcomes of certifying with ISO 14001 are still unclear and equivocal. Drawing on institutional and market learning theories, the impact of ISO 14001 on firms’ market performance is examined for Chinese firms. By employing rigorous event study approach, this paper compared ISO 14001 certified firms with non-certified counterpart firms based on different matching criteria that include size, return on assets and industry. The results indicate that the ISO 14001 has been negatively signed by the investors both in the short and long-run. This paper suggested implications for policy makers, managers, and other nonprofit organizations.Keywords: ISO 14001, legitimacy, institutional forces, event study approach, emerging markets
Procedia PDF Downloads 1534588 Downscaling Seasonal Sea Surface Temperature Forecasts over the Mediterranean Sea Using Deep Learning
Authors: Redouane Larbi Boufeniza, Jing-Jia Luo
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This study assesses the suitability of deep learning (DL) for downscaling sea surface temperature (SST) over the Mediterranean Sea in the context of seasonal forecasting. We design a set of experiments that compare different DL configurations and deploy the best-performing architecture to downscale one-month lead forecasts of June–September (JJAS) SST from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for the period of 1982–2020. We have also introduced predictors over a larger area to include information about the main large-scale circulations that drive SST over the Mediterranean Sea region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results showed that the convolutional neural network (CNN)-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme SST spatial patterns. Besides, the CNN-based downscaling yields a much more accurate forecast of extreme SST and spell indicators and reduces the significant relevant biases exhibited by the raw model predictions. Moreover, our results show that the CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of the Mediterranean Sea. The results demonstrate the potential usefulness of CNN in downscaling seasonal SST predictions over the Mediterranean Sea, particularly in providing improved forecast products.Keywords: Mediterranean Sea, sea surface temperature, seasonal forecasting, downscaling, deep learning
Procedia PDF Downloads 694587 An Exploration of the Integration of Guided Play With Explicit Instruction in Early Childhood Mathematics
Authors: Anne Tan, Kok-Sing Tang, Audrey Cooke
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Play has always been a prominent pedagogy in early childhood. However, there is growing evidence of success in students’ learning using explicit instruction, especially in literacy in the early years. There is also limited research using explicit instruction in early childhood mathematics, and play is usually prominently mentioned. This proposed research aims to investigate the possibilities and benefits of integrating guided play with explicit instruction in early childhood mathematics education. While play has traditionally been a prominent pedagogy in early childhood, there is growing evidence of success in student learning through explicit instruction, particularly in literacy. However, limited research exists on the integration of explicit instruction in early childhood mathematics, where play remains prominently mentioned. This study utilises a multiple case study methodology to gather data and provide immediate opportunities for curriculum improvement. The research will commence with semi-structured interviews to gain insights into educators' background knowledge. Highly structured observations will be conducted to record the frequency and manner in which guided play is integrated with specific elements of explicit instruction during mathematics teaching in early childhood. To enhance the observations, video recordings will be made using cameras with video settings and Microsoft Teams meeting recordings. In addition to interviews and observations, educators will maintain journals and use the Microsoft Teams platform for self-reflection on the integration of guided play and explicit instruction in their classroom practices and experiences. The study participants will include educators with early childhood degrees and students in years one and two. The primary goal of this research is to inform the benefits of integrating two high-impact pedagogies, guided play, and explicit instruction, for enhancing student learning outcomes in mathematics education. By exploring the integration of these pedagogical approaches, this study aims to contribute to the development of effective instructional strategies in early childhood mathematics education.Keywords: early childhood, early childhood mathematics, early childhood numbers, guided play, play-based learning, explicit instruction
Procedia PDF Downloads 574586 Like a Bridge over Troubled Waters: The Value of Joint Learning Programs in Intergroup Identity-Based Conflict in Israel
Authors: Rachelly Ashwall, Ephraim Tabory
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In an attempt to reduce the level of a major identity-based conflict in Israel between Ultra-orthodox and secular Jews, several initiatives in recent years have tried to bring members of the two societies together in facilitated joint discussion forums. Our study analyzes the impact of two types of such programs: joint mediation training classes and confrontation-based learning programs that are designed to facilitate discussions over controversial issues. These issues include claims about an unequal shouldering of national obligations such as military service, laws requiring public observance of the Sabbath, and discrimination against women, among others. The study examines the factors that enabled the two groups to reduce their social distance, and increase their understanding of each other, and develop a recognition and tolerance of the other group's particular social identity. The research conducted over a course of two years involved observations of the activities of the groups, interviews with the participants, and analysis of the social media used by the groups. The findings demonstrate the progression from a mutual initial lack of knowledge about habits, norms, and attitudes of the out-group to an increasing desire to know, understand and more readily accept the identity of a previously rejected outsider. Participants manifested more respect, concern for and even affection for those whose identity initially led them to reject them out of hand. We discuss the implications for seemingly intractable identity-based conflict in fragile societies.Keywords: identity-based conflict, intergroup relations, joint mediation learning, out-group recognition, social identity
Procedia PDF Downloads 2444585 Design of Digital IIR Filter Using Opposition Learning and Artificial Bee Colony Algorithm
Authors: J. S. Dhillon, K. K. Dhaliwal
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In almost all the digital filtering applications the digital infinite impulse response (IIR) filters are preferred over finite impulse response (FIR) filters because they provide much better performance, less computational cost and have smaller memory requirements for similar magnitude specifications. However, the digital IIR filters are generally multimodal with respect to the filter coefficients and therefore, reliable methods that can provide global optimal solutions are required. The artificial bee colony (ABC) algorithm is one such recently introduced meta-heuristic optimization algorithm. But in some cases it shows insufficiency while searching the solution space resulting in a weak exchange of information and hence is not able to return better solutions. To overcome this deficiency, the opposition based learning strategy is incorporated in ABC and hence a modified version called oppositional artificial bee colony (OABC) algorithm is proposed in this paper. Duplication of members is avoided during the run which also augments the exploration ability. The developed algorithm is then applied for the design of optimal and stable digital IIR filter structure where design of low-pass (LP) and high-pass (HP) filters is carried out. Fuzzy theory is applied to achieve maximize satisfaction of minimum magnitude error and stability constraints. To check the effectiveness of OABC, the results are compared with some well established filter design techniques and it is observed that in most cases OABC returns better or atleast comparable results.Keywords: digital infinite impulse response filter, artificial bee colony optimization, opposition based learning, digital filter design, multi-parameter optimization
Procedia PDF Downloads 4684584 Task Validity in Neuroimaging Studies: Perspectives from Applied Linguistics
Authors: L. Freeborn
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Recent years have seen an increasing number of neuroimaging studies related to language learning as imaging techniques such as fMRI and EEG have become more widely accessible to researchers. By using a variety of structural and functional neuroimaging techniques, these studies have already made considerable progress in terms of our understanding of neural networks and processing related to first and second language acquisition. However, the methodological designs employed in neuroimaging studies to test language learning have been questioned by applied linguists working within the field of second language acquisition (SLA). One of the major criticisms is that tasks designed to measure language learning gains rarely have a communicative function, and seldom assess learners’ ability to use the language in authentic situations. This brings the validity of many neuroimaging tasks into question. The fundamental reason why people learn a language is to communicate, and it is well-known that both first and second language proficiency are developed through meaningful social interaction. With this in mind, the SLA field is in agreement that second language acquisition and proficiency should be measured through learners’ ability to communicate in authentic real-life situations. Whilst authenticity is not always possible to achieve in a classroom environment, the importance of task authenticity should be reflected in the design of language assessments, teaching materials, and curricula. Tasks that bear little relation to how language is used in real-life situations can be considered to lack construct validity. This paper first describes the typical tasks used in neuroimaging studies to measure language gains and proficiency, then analyses to what extent these tasks can validly assess these constructs.Keywords: neuroimaging studies, research design, second language acquisition, task validity
Procedia PDF Downloads 1274583 The Impact of Supporting Productive Struggle in Learning Mathematics: A Quasi-Experimental Study in High School Algebra Classes
Authors: Sumeyra Karatas, Veysel Karatas, Reyhan Safak, Gamze Bulut-Ozturk, Ozgul Kartal
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Productive struggle entails a student's cognitive exertion to comprehend mathematical concepts and uncover solutions not immediately apparent. The significance of productive struggle in learning mathematics is accentuated by influential educational theorists, emphasizing its necessity for learning mathematics with understanding. Consequently, supporting productive struggle in learning mathematics is recognized as a high-leverage and effective mathematics teaching practice. In this study, the investigation into the role of productive struggle in learning mathematics led to the development of a comprehensive rubric for productive struggle pedagogy through an exhaustive literature review. The rubric consists of eight primary criteria and 37 sub-criteria, providing a detailed description of teacher actions and pedagogical choices that foster students' productive struggles. These criteria encompass various pedagogical aspects, including task design, tool implementation, allowing time for struggle, posing questions, scaffolding, handling mistakes, acknowledging efforts, and facilitating discussion/feedback. Utilizing this rubric, a team of researchers and teachers designed eight 90-minute lesson plans, employing a productive struggle pedagogy, for a two-week unit on solving systems of linear equations. Simultaneously, another set of eight lesson plans on the same topic, featuring identical content and problems but employing a traditional lecture-and-practice model, was designed by the same team. The objective was to assess the impact of supporting productive struggle on students' mathematics learning, defined by the strands of mathematical proficiency. This quasi-experimental study compares the control group, which received traditional lecture- and practice instruction, with the treatment group, which experienced a productive struggle in pedagogy. Sixty-six 10th and 11th-grade students from two algebra classes, taught by the same teacher at a high school, underwent either the productive struggle pedagogy or lecture-and-practice approach over two-week eight 90-minute class sessions. To measure students' learning, an assessment was created and validated by a team of researchers and teachers. It comprised seven open-response problems assessing the strands of mathematical proficiency: procedural and conceptual understanding, strategic competence, and adaptive reasoning on the topic. The test was administered at the beginning and end of the two weeks as pre-and post-test. Students' solutions underwent scoring using an established rubric, subjected to expert validation and an inter-rater reliability process involving multiple criteria for each problem based on their steps and procedures. An analysis of covariance (ANCOVA) was conducted to examine the differences between the control group, which received traditional pedagogy, and the treatment group, exposed to the productive struggle pedagogy, on the post-test scores while controlling for the pre-test. The results indicated a significant effect of treatment on post-test scores for procedural understanding (F(2, 63) = 10.47, p < .001), strategic competence (F(2, 63) = 9.92, p < .001), adaptive reasoning (F(2, 63) = 10.69, p < .001), and conceptual understanding (F(2, 63) = 10.06, p < .001), controlling for pre-test scores. This demonstrates the positive impact of supporting productive struggle in learning mathematics. In conclusion, the results revealed the significance of the role of productive struggle in learning mathematics. The study further explored the practical application of productive struggle through the development of a comprehensive rubric describing the pedagogy of supporting productive struggle.Keywords: effective mathematics teaching practice, high school algebra, learning mathematics, productive struggle
Procedia PDF Downloads 494582 Comparative Regionalism: The Case of Financial Integration in Association of Southeast Asian Nations
Authors: Sharon Kun-Amornpong
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In this paper, ASEAN financial integration will be discussed from the perspective of the rule of law. The methodology of the paper is comparative regionalism. It will compare the role of the rule of law in ASEAN financial integration with that of the European Union with particular focuses on, for example, institutions and values. The paper argues that in the realm of financial integration, the rule of law is one of the most important factors that could help strengthen and promote financial integration in ASEAN. This is despite the fact that the ‘ASEAN Way’ emphasises non-interference and utilises a consensus-based cooperation rather than formal institutions. Nevertheless, the rule of law for ASEAN financial integration should be situated in its own historical, cultural, and political contexts. In addition, in the case of ASEAN, the rule of law cannot take root if it does not come from the demand of the people in this region. For instance, a reform or creation of legal institutions should not be imposed by international financial institutions. The paper will conclude that law has a normative force. It could shape expectation of market participants and promote deeper financial integration if norms that the law generates have become a significant norm in the society or industry.Keywords: Association of Southeast Asian Nations, ASEAN, comparative regionalism, financial integration, the rule of law
Procedia PDF Downloads 1954581 Multimodal Characterization of Emotion within Multimedia Space
Authors: Dayo Samuel Banjo, Connice Trimmingham, Niloofar Yousefi, Nitin Agarwal
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Technological advancement and its omnipresent connection have pushed humans past the boundaries and limitations of a computer screen, physical state, or geographical location. It has provided a depth of avenues that facilitate human-computer interaction that was once inconceivable such as audio and body language detection. Given the complex modularities of emotions, it becomes vital to study human-computer interaction, as it is the commencement of a thorough understanding of the emotional state of users and, in the context of social networks, the producers of multimodal information. This study first acknowledges the accuracy of classification found within multimodal emotion detection systems compared to unimodal solutions. Second, it explores the characterization of multimedia content produced based on their emotions and the coherence of emotion in different modalities by utilizing deep learning models to classify emotion across different modalities.Keywords: affective computing, deep learning, emotion recognition, multimodal
Procedia PDF Downloads 1464580 Podcasting as an Instructional Method: Case Study of a School Psychology Class
Authors: Jeff A. Tysinger, Dawn P. Tysinger
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There has been considerable growth in online learning. Researchers continue to explore the impact various methods of delivery. Podcasting is a popular method for sharing information. The purpose of this study was to examine the impact of student motivation and the perception of the acquisition of knowledge in an online environment of a skill-based class. 25 students in a school psychology graduate class completed a pretest and posttest examining podcast use and familiarity. In addition, at the completion of the course they were administered a modified version of the Instructional Materials Motivation Survey. The four subscales were examined (attention, relevance, confidence, and satisfaction). Results indicated that students are motivated, they perceive podcasts as positive instructional tools, and students are successful in acquiring the needed information. Additional benefits of using podcasts and recommendations in school psychology training are discussed.Keywords: motivation, online learning, pedagogy, podcast
Procedia PDF Downloads 1254579 Teaching Children With Differential Learning Needs By Understanding Their Talents And Interests
Authors: Eunice Tan
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The purpose of this presentation is to look at an alternative to the approach and methodologies of working with special needs. The strength-based approach to education embodies a paradigm shift. It is a strategy to move away from a deficit-based methodology which inadvertently may lead to an extensive list of things that the child cannot do or is unable to do. Today, many parents of individuals with special needs are focused on the child’s deficits rather than on his or her strengths. Even when parents Recognise and identify their child’s strengths to be valuable and wish to develop their abilities, they face the challenge that there are insufficient programs committed to supporting the development and improvement of such abilities. What is a strength-based approach in education? A strength-based approach in education focuses on students' positive qualities and contributions to class instead of the skills and abilities they may not have. Many schools are focused on the child’s special educational needs rather than the whole child. Parents interviewed have said that they have to engage external tutors to help hone in on their child’s interests and strengths.Keywords: differential learning needs, special needs, instructional style, talents
Procedia PDF Downloads 1874578 Outdoor Thermal Comfort Strategies: The Case of Cool Facades
Authors: Noelia L. Alchapar, Cláudia C. Pezzuto, Erica N. Correa
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Mitigating urban overheating is key to achieving the environmental and energy sustainability of cities. The management of the optical properties of the materials that make up the urban envelope -roofing, pavement, and facades- constitutes a profitable and effective tool to improve the urban microclimate and rehabilitate urban areas. Each material that makes up the urban envelope has a different capacity to reflect received solar radiation, which alters the fraction of solar radiation absorbed by the city. However, the paradigm of increasing solar reflectance in all areas of the city without distinguishing their relative position within the urban canyon can cause serious problems of overheating and discomfort among its inhabitants. The hypothesis that supports the research postulates that not all reflective technologies that contribute to urban radiative cooling favor the thermal comfort conditions of pedestrians to equal measure. The objective of this work is to determine to what degree the management of the optical properties of the facades modifies outdoor thermal comfort, given that the mitigation potential of materials with high reflectance in facades is strongly conditioned by geographical variables and by the geometric characteristics of the urban profile aspect ratio (H/W). This research was carried out under two climatic contexts, that of the city of Mendoza-Argentina and that of the city of Campinas-Brazil, according to the Köppen climate classification: BWk and Cwa, respectively. Two areas in two different climatic contexts (Mendoza - Argentina and Campinas - Brazil) were selected. Both areas have comparable urban morphology patterns. These areas are located in a region with low horizontal building density and residential zoning. The microclimatic conditions were monitored during the summer period with temperature and humidity fixed sensors inside vial channels. The microclimate model was simulated in ENVI-Met V5. A grid resolution of 3.5 x 3.5 x 3.5m was used for both cities, totaling an area of 145x145x30 grids. Based on the validated theoretical model, ten scenarios were simulated, modifying the height of buildings and the solar reflectivity of facades. The solar reflectivity façades ranges were: low (0.3) and high (0.75). The density scenarios range from 1th to the 5th level. The study scenarios' performance was assessed by comparing the air temperature, physiological equivalent temperature (PET), and thermal climate index (UTCI). As a result, it is observed that the behavior of the materials of the urban outdoor space depends on complex interactions. Many urban environmental factors influence including constructive characteristics, urban morphology, geographic locations, local climate, and so forth. The role of the vertical urban envelope is decisive for the reduction of urban overheating. One of the causes of thermal gain is the multiple reflections within the urban canyon, which affects not only the air temperature but also the pedestrian thermal comfort. One of the main findings of this work leads to the remarkable importance of considering both the urban warming and the thermal comfort aspects of pedestrians in urban mitigation strategies.Keywords: materials facades, solar reflectivity, thermal comfort, urban cooling
Procedia PDF Downloads 854577 Churn Prediction for Telecommunication Industry Using Artificial Neural Networks
Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz
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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks
Procedia PDF Downloads 1374576 The Face Sync-Smart Attendance
Authors: Bekkem Chakradhar Reddy, Y. Soni Priya, Mathivanan G., L. K. Joshila Grace, N. Srinivasan, Asha P.
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Currently, there are a lot of problems related to marking attendance in schools, offices, or other places. Organizations tasked with collecting daily attendance data have numerous concerns. There are different ways to mark attendance. The most commonly used method is collecting data manually by calling each student. It is a longer process and problematic. Now, there are a lot of new technologies that help to mark attendance automatically. It reduces work and records the data. We have proposed to implement attendance marking using the latest technologies. We have implemented a system based on face identification and analyzing faces. The project is developed by gathering faces and analyzing data, using deep learning algorithms to recognize faces effectively. The data is recorded and forwarded to the host through mail. The project was implemented in Python and Python libraries used are CV2, Face Recognition, and Smtplib.Keywords: python, deep learning, face recognition, CV2, smtplib, Dlib.
Procedia PDF Downloads 474575 Unsupervised Assistive and Adaptative Intelligent Agent in Smart Enviroment
Authors: Sebastião Pais, João Casal, Ricardo Ponciano, Sérgio Lorenço
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The adaptation paradigm is a basic defining feature for pervasive computing systems. Adaptation systems must work efficiently in a smart environment while providing suitable information relevant to the user system interaction. The key objective is to deduce the information needed information changes. Therefore relying on fixed operational models would be inappropriate. This paper presents a study on developing an Intelligent Personal Assistant to assist the user in interacting with their Smart Environment. We propose an Unsupervised and Language-Independent Adaptation through Intelligent Speech Interface and a set of methods of Acquiring Knowledge, namely Semantic Similarity and Unsupervised Learning.Keywords: intelligent personal assistants, intelligent speech interface, unsupervised learning, language-independent, knowledge acquisition, association measures, symmetric word similarities, attributional word similarities
Procedia PDF Downloads 5494574 Public-Private Partnership for Community Empowerment and Sustainability: Exploring Save the Children’s 'School Me' Project in West Africa
Authors: Gae Hee Song
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This paper aims to address the evolution of public-private partnerships for mainstreaming an evaluation approach in the community-based education project. It examines the distinctive features of Save the Children’s School Me project in terms of empowerment evaluation principles introduced by David M. Fetterman, especially community ownership, capacity building, and organizational learning. School Me is a Save the Children Korea funded-project, having been implemented in Cote d’Ivoire and Sierra Leone since 2016. The objective of this project is to reduce gender-based disparities in school completion and learning outcomes by creating an empowering learning environment for girls and boys. Both quasi-experimental and experimental methods for impact evaluation have been used to explore changes in learning outcomes, gender attitudes, and learning environments. To locate School Me in the public-private partnership framework for community empowerment and sustainability, the data have been collected from School Me progress/final reports, baseline, and endline reports, fieldwork observations, inter-rater reliability of baseline and endline data collected from a total of 75 schools in Cote d’Ivoire and Sierra Leone. The findings of this study show that School Me project has a significant evaluation component, including qualitative exploratory research, participatory monitoring, and impact evaluation. It strongly encourages key actors, girls, boys, parents, teachers, community leaders, and local education authorities, to participate in the collection and interpretation of data. For example, 45 community volunteers collected baseline data in Cote d’Ivoire; on the other hand, three local government officers and fourteen enumerators participated in the follow-up data collection of Sierra Leone. Not only does this public-private partnership improve local government and community members’ knowledge and skills of monitoring and evaluation, but the evaluative findings also help them find their own problems and solutions with a strong sense of community ownership. Such community empowerment enables Save the Children country offices and member offices to gain invaluable experiences and lessons learned. As a result, empowerment evaluation leads to community-oriented governance and the sustainability of the School Me project.Keywords: community empowerment, Cote d’Ivoire, empowerment evaluation, public-private partnership, save the children, school me, Sierra Leone, sustainability
Procedia PDF Downloads 1184573 Date Palm Fruits from Oman Attenuates Cognitive and Behavioral Defects and Reduces Inflammation in a Transgenic Mice Model of Alzheimer's Disease
Authors: M. M. Essa, S. Subash, M. Akbar, S. Al-Adawi, A. Al-Asmi, G. J. Guillemein
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Transgenic (tg) mice which contain an amyloid precursor protein (APP) gene mutation, develop extracellular amyloid beta (Aβ) deposition in the brain, and severe memory and behavioral deficits with age. These mice serve as an important animal model for testing the efficacy of novel drug candidates for the treatment and management of symptoms of Alzheimer's disease (AD). Several reports have suggested that oxidative stress is the underlying cause of Aβ neurotoxicity in AD. Date palm fruits contain very high levels of antioxidants and several medicinal properties that may be useful for improving the quality of life in AD patients. In this study, we investigated the effect of dietary supplementation of Omani date palm fruits on the memory, anxiety and learning skills along with inflammation in an AD mouse model containing the double Swedish APP mutation (APPsw/Tg2576). The experimental groups of APP-transgenic mice from the age of 4 months were fed custom-mix diets (pellets) containing 2% and 4% Date palm fruits. We assessed spatial memory and learning ability, psychomotor coordination, and anxiety-related behavior in Tg and wild-type mice at the age of 4-5 months and 18-19 months using the Morris water maze test, rota rod test, elevated plus maze test, and open field test. Further, inflammatory parameters also analyzed. APPsw/Tg2576 mice that were fed a standard chow diet without dates showed significant memory deficits, increased anxiety-related behavior, and severe impairment in spatial learning ability, position discrimination learning ability and motor coordination along with increased inflammation compared to the wild type mice on the same diet, at the age of 18-19 months In contrast, PPsw/Tg2576 mice that were fed a diet containing 2% and 4% dates showed a significant improvements in memory, learning, locomotor function, and anxiety with reduced inflammatory markers compared to APPsw/Tg2576 mice fed the standard chow diet. Our results suggest that dietary supplementation with dates may slow the progression of cognitive and behavioral impairments in AD. The exact mechanism is still unclear and further extensive research needed.Keywords: Alzheimer's disease, date palm fruits, Oman, cognitive decline, memory loss, anxiety, inflammation
Procedia PDF Downloads 4194572 IEP Curriculum to Include For-Credit University English Classes
Authors: Cheyne Kirkpatrick
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In an attempt to make the university intensive English program more worthwhile for students, many English language programs are redesigning curriculum to offer for-credit English for Academic Purposes classes, sometimes marketed as “bridge” courses. These programs are designed to be accredited to national language standards, provide communicative language learning, and give students the opportunity to simultaneously earn university language credit while becoming proficient in academic English. This presentation will discuss the curriculum design of one such program in the United States at a large private university that created its own for-credit “bridge” program. The planning, development, piloting, teaching, and challenges of designing this type of curriculum will be presented along with the aspects of accreditation, communicative language learning, and integration within various university programs. Attendees will learn about how such programs are created and what types of objectives and outcomes are included in American EAP classes.Keywords: IEP, AEP, Curriculum, CEFR, University Credit, Bridge
Procedia PDF Downloads 4764571 A Program Evaluation of TALMA Full-Year Fellowship Teacher Preparation
Authors: Emilee M. Cruz
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Teachers take part in short-term teaching fellowships abroad, and their preparation before, during, and after the experience is critical to affecting teachers’ feelings of success in the international classroom. A program evaluation of the teacher preparation within TALMA: The Israel Program for Excellence in English (TALMA) full-year teaching fellowship was conducted. A questionnaire was developed that examined professional development, deliberate reflection, and cultural and language immersion offered before, during, and after the short-term experience. The evaluation also surveyed teachers’ feelings of preparedness for the Israeli classroom and any recommendations they had for future teacher preparation within the fellowship program. The review suggests the TALMA program includes integrated professional learning communities between fellows and Israeli co-teachers, more opportunities for immersive Hebrew language learning, a broader professional network with Israelis, and opportunities for guided discussion with the TALMA community continued participation in TALMA events and learning following the full-year fellowship. Similar short-term international programs should consider the findings in the design of their participation preparation programs. The review also offers direction for future program evaluation of short-term participant preparation, including the need for frequent response item updates to match current offerings and evaluation of participant feelings of preparedness before, during, and after the full-year fellowship.Keywords: educational program evaluation, international teaching, short-term teaching, teacher beliefs, teaching fellowship, teacher preparation
Procedia PDF Downloads 1764570 Decoding Kinematic Characteristics of Finger Movement from Electrocorticography Using Classical Methods and Deep Convolutional Neural Networks
Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi
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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex
Procedia PDF Downloads 1674569 Unsupervised Assistive and Adaptive Intelligent Agent in Smart Environment
Authors: Sebastião Pais, João Casal, Ricardo Ponciano, Sérgio Lourenço
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The adaptation paradigm is a basic defining feature for pervasive computing systems. Adaptation systems must work efficiently in smart environment while providing suitable information relevant to the user system interaction. The key objective is to deduce the information needed information changes. Therefore, relying on fixed operational models would be inappropriate. This paper presents a study on developing a Intelligent Personal Assistant to assist the user in interacting with their Smart Environment. We propose a Unsupervised and Language-Independent Adaptation through Intelligent Speech Interface and a set of methods of Acquiring Knowledge, namely Semantic Similarity and Unsupervised Learning.Keywords: intelligent personal assistants, intelligent speech interface, unsupervised learning, language-independent, knowledge acquisition, association measures, symmetric word similarities, attributional word similarities
Procedia PDF Downloads 631