Search results for: feature learning

Authors: Rendani Mercy Makhwathana

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This paper intends to explore the experiences and views of Foundation Phase teachers when teaching English First Additional Language in rural public schools. Teachers all over the world are pillars of any education system. Consequently, any education transformation should start with teachers as critical role players in the education system. As a result, teachers’ experiences and views are worth consideration, for they impact on learners learning and the wellbeing of education in general. An exploratory qualitative approach with the use of phenomenological research design was used in this paper. The population for this paper comprised all Foundation Phase teachers in the district. Purposive sampling technique was used to select a sample of 15 Foundation Phase teachers from five rural-based schools. Data was collected through classroom observation and individual face-to-face interviews. Data were categorised, analysed and interpreted. The findings revealed that from time-to-time teachers experiences one or more challenging situations, learners’ low participation in the classroom to lack of resources. This paper recommends that teachers should be provided with relevant resources and support to effectively teach English First Additional Language.

Keywords: the education system, first additional language, foundation phase, intermediate phase, language of learning and teaching, medium of instruction, teacher professional development

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Authors: Pierre Brocas, Konstantinos Rizos

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This study was conducted to test the effects of introducing a consistent pace and volume of reading whole narratives on adolescents' reading comprehension with a diagnosis of autism spectrum disorder (ASD). The study was inspired by previous studies conducted on poorer adolescent readers in English schools. The setting was a Free Special Education Needs school in England. Nine male and one female student, between 11-13 years old, across two classrooms participated in the study. All students had a diagnosis of ASD, and all were classified as advanced learners. The classroom teachers introduced reading a whole challenging novel in 12 weeks with consistency as the independent variable. The study used a before-and-after design of testing the participants’ reading comprehension using standardised tests. The participants made a remarkable 1.8 years’ mean progress on the standardised tests of reading comprehension, with three participants making 4+ years progress. The researchers hypothesise that reading novels aloud and at a fast pace in each lesson, that are challenging but appropriate to the participants’ learning level, may have a beneficial effect on the reading comprehension of adolescents with learning difficulties, giving them a more engaged uninterrupted reading experience over a sustained period. However, more studies need to be conducted to test the independent variable across a bigger and more diverse population with a stronger design.

Keywords: autism, reading comprehension, developmental disabilities, narratives

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Authors: Oibar Martinez, Clara Oliver, Jose Miguel Miranda

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In this paper, we discuss the standard improvements which can be made to reduce the earth resistance in difficult terrains for optimum lightning protection, what are the practical limitations, and how the modeling can be refined for accurate diagnostics and ground resistance minimization. Ground resistance minimization can be made via three different approaches: burying vertical electrodes connected in parallel, burying horizontal conductive plates or meshes, or modifying the own terrain, either by changing the entire terrain material in a large volume or by adding earth-enhancing compounds. The use of vertical electrodes connected in parallel pose several practical limitations. In order to prevent loss of effectiveness, it is necessary to keep a minimum distance between each electrode, which is typically around five times larger than the electrode length. Otherwise, the overlapping of the local equipotential lines around each electrode reduces the efficiency of the configuration. The addition of parallel electrodes reduces the resistance and facilitates the measurement, but the basic parallel resistor formula of circuit theory will always underestimate the final resistance. Numerical simulation of equipotential lines around the electrodes overcomes this limitation. The resistance of a single electrode will always be proportional to the soil resistivity. The electrodes are usually installed with a backfilling material of high conductivity, which increases the effective diameter. However, the improvement is marginal, since the electrode diameter counts in the estimation of the ground resistance via a logarithmic function. Substances that are used for efficient chemical treatment must be environmentally friendly and must feature stability, high hygroscopicity, low corrosivity, and high electrical conductivity. A number of earth enhancement materials are commercially available. Many are comprised of carbon-based materials or clays like bentonite. These materials can also be used as backfilling materials to reduce the resistance of an electrode. Chemical treatment of soil has environmental issues. Some products contain copper sulfate or other copper-based compounds, which may not be environmentally friendly. Carbon-based compounds are relatively inexpensive and they do have very low resistivities, but they also feature corrosion issues. Typically, the carbon can corrode and destroy a copper electrode in around five years. These compounds also have potential environmental concerns. Some earthing enhancement materials contain cement, which, after installation acquire properties that are very close to concrete. This prevents the earthing enhancement material from leaching into the soil. After analyzing different configurations, we conclude that a buried conductive ring with vertical electrodes connected periodically should be the optimum baseline solution for the grounding of a large size structure installed on a large resistivity terrain. In order to show this, a practical example is explained here where we simulate the ground resistance of a conductive ring buried in a terrain with a resistivity in the range of 1 kOhm·m.

Keywords: grounding improvements, large scale scientific instrument, lightning risk assessment, lightning standards

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Authors: Yuyan Yi, Jingyi Zheng, Nedret Billor

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Independent component analysis (ICA) is one of the most commonly used blind source separation (BSS) techniques for signal pre-processing, such as noise reduction and feature extraction. The main parameter in the ICA method is the number of independent components (IC). Although there have been several methods for the determination of the number of ICs, it has not been given sufficient attentionto this important parameter. In this study, wereview the mostused methods fordetermining the number of ICs and providetheir advantages and disadvantages. Further, wepropose an improved version of column-wise ICAByBlock method for the determination of the number of ICs.To assess the performance of the proposed method, we compare the column-wise ICAbyBlock with several existing methods through different ICA methods by using simulated and real signal data. Results show that the proposed column-wise ICAbyBlock is an effective and stable method for determining the optimal number of components in ICA. This method is simple, and results can be demonstrated intuitively with good visualizations.

Keywords: independent component analysis, optimal number, column-wise, correlation coefficient, cross-validation, ICAByblock

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Authors: Dhanuj M. Gandikota

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Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

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Authors: Cem Bulut, Zeynep B. Kocoglu

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Flipped classroom (FC) method has gained popularity, specifically in higher education, in recent years with the idea that it is possible to use the time spent in classrooms more effectively by simply flipping the passive lecturing parts with the homework exercises. Accordingly, the present study aims to investigate whether using FC method is more effective than the non-flipped method in teaching grammar to English as a Foreign Language (EFL) learners. An experimental research was conducted with the participants of two intact classes having A2 level English courses (N=39 in total) in a vocational school in Kocaeli, Turkey. Results from the post-test indicated that the flipped group achieved higher scores than the non-flipped group did. Additionally, independent samples t-test analysis in SPSS revealed that the difference between two groups was statistically significant. On the other hand, even if the factors that lie beneath this improvement are likely to be attributed to the teaching method, which is also supported by the answers given to the FC perception survey and interview, participants in both groups developed statistically significant positive attitudes towards learning grammar regardless of the method used. In that sense, this result was considered to be related to the level of the course, which was quite low in English level. In sum, the present study provides additional findings to the literature for FC methodology from a different perspective.

Keywords: flipped classroom, learning management system, English as a foreign language

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Authors: Jennifer Sturgeon

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Stable relationships provide a predictable and trusting environment and are essential for early development, but high teacher turnover rates in childcare settings make it challenging for infants and toddlers to form stable relationships with their teachers. This can have an adverse effect on development and learning. The qualitative study discussed in this article draws from the experiences of early Head Start teachers and administrators to describe both the impact of teacher turnover and the motivational factors that contribute to teacher retention. A case study approach was used and included classroom observations, a review of exit interviews, and perceptions from focus groups of early Head Start staff in an urban early Head Start childcare center. Emerging from the case study was the discovery that teacher turnover has an impact on the social-emotional development of toddlers, particularly in self-regulation. Additional key findings that emerged include teacher turnover leading to negative effects on learning, a decrease in preschool preparation, and increased chaos in the classroom and center. Motivational factors that contributed to teacher retention included positive leadership, the mission to make a difference, and fair compensation.

Keywords: early childhood, teacher turnover, continuity of care, early head start

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Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

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Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

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Authors: Helga Mayr

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In our ongoing effort to address urgent global challenges related to sustainability, higher education institutions play a central role in raising a generation of informed and empowered citizens committed to sustainable development. This paper presents the preliminary results of the so far realized evaluation of a compulsory module on education for sustainable development (ESD) offered to students in the bachelor's program in elementary education at the University College of Teacher Education Tyrol (PH Tirol), Austria. The module includes a lecture on sustainability and education as well as a project-based seminar that aims to foster a deep understanding of ESD and its application in pedagogical practice. The study examines various dimensions related to the module's impact on participating students, focusing on prevalent sustainability concepts, intentions, actions, general and sustainability-related self-efficacy, perceived competence related to ESD, and ESD-related self-efficacy. In addition, the research addresses assessment of the learning process. To obtain a comprehensive overview of the effectiveness of the module, a mixed methods approach was/is used in the evaluation. Quantitative data was/is collected through surveys and self-assessment instruments, while qualitative findings were/will be obtained through focus group interviews and reflective analysis. The PH Tirol is collaborating with another University College of Teacher Education (Styria) and a university of applied sciences in Switzerland (UAS of the Grisons) to broaden the scope of the analysis and allow for comparative findings. Preliminary results indicate that students have a relatively rudimentary understanding of sustainability. The extent to which completion of the module influences understanding of sustainability, awareness, intentions, and actions, as well as self-efficacy, is currently under investigation. The results will be available at the time of the conference and will be presented there. In terms of learning, the project-based seminar, which promotes hands-on engagement with ESD, was evaluated for its effectiveness in fostering key sustainability competencies as well as sustainability-related and ESD-related self-efficacy. The research not only provides insights into the effectiveness of the compulsory module ESD at the PH Tirol but also contributes to the broader discourse on integrating ESD into teacher education.

Keywords: education for sustainable development, teacher education, project-based learning, effectiveness measurements

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Authors: Geeta Goyal

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Teaching-learning of English as a second language is a challenge for the learner as well as the teacher. Whereas a student may find it hard and get demotivated while communicating in a language other than mother tongue, a teacher, too, finds it difficult to integrate necessary teaching material in lesson plans to maximize the outcome. Studies reveal that task-based teaching can be useful in diverse contexts in a second language classroom as it helps in creating opportunities for language exposure as per learners' interest and capability levels, which boosts their confidence and learning efficiency. The present study has analysed the impact of various activities carried out in a heterogenous group of second language learners at tertiary level in a semi-urban area in Haryana state of India. Language tasks were specifically planned with a focus on engaging groups of twenty-five students for a period of three weeks. These included language games such as spell-well, cross-naught besides other communicative and interactive tasks like mock-interviews, role plays, sharing experiences, storytelling, simulations, scene-enact, video-clipping, etc. Tools in form of handouts and cue cards were also used as per requirement. This experiment was conducted for ten groups of students taking bachelor’s courses in different streams of humanities, commerce, and sciences. Participants were continuously supervised, monitored, and guided by the respective teacher. Feedback was collected from the students through classroom observations, interviews, and questionnaires. Students' responses revealed that they felt comfortable and got plenty of opportunities to communicate freely without being afraid of making mistakes. It was observed that even slow/timid/shy learners got involved by getting an experience of English language usage in friendly environment. Moreover, it helped the teacher in establishing a trusting relationship with students and encouraged them to do the same with their classmates. The analysis of the data revealed that majority of students demonstrated improvement in their interest and enthusiasm in the class. The study revealed that task-based teaching was an effective method to improve the teaching-learning process under the given conditions.

Keywords: communication skills, English, second language, task-based teaching

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Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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Authors: Mara J. Richman, Zsolt Unoka, Robert Dudas, Zsolt Demetrovics

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Borderline personality disorder (BPD) is characterized by deficits in emotion regulation and effective liability. Of this domain, ruminative behaviors have been considered a core feature of emotion dysregulation difficulties. Taking this into consideration, a meta-analysis was performed to assess how BPD symptoms correlate with rumination, while also considering clinical moderator variables such as comorbidity, GAF score, and type of BPD symptom and demographic moderator variables such as age, gender, and education level. Analysis of correlation across rumination domains for the entire sample revealed a medium overall correlation. When assessing types of rumination, the largest correlation was among pain rumination followed by anger, depressive, and anxious rumination. Furthermore, affective instability had the strongest correlation with increased rumination, followed by unstable relationships, identity disturbance, and self-harm/ impulsivity, respectively. Demographic variables showed no significance. Clinical implications are considered and further therapeutic interventions are discussed in the context of rumination.

Keywords: borderline personality disorder, meta-analysis, rumination, symptoms

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Authors: Anita Yus

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Several Elementary Schools (SD) ‘favored’ by parents, especially those live in big cities in Indonesia, implicitly demand each child enrolled in the first grade of SD to be able to read, write and calculate. This condition urges the parents to push the teachers in PAUD (Kindergarten) to train their children to read, write, and calculate so they have a set of knowledge. According to Piaget, each child is capable of acquiring knowledge when he is given the opportunity to interact with his environment (things, people, and atmosphere). Teachers can make the interaction occur. There are several learning approaches suitable for the characteristics and needs of child’s growth. This paper talks about a research result conducted to investigate how twelve teachers of early childhood program comprehend the constructivist theory of Piaget, and how they inquire, how the children acquire and construct a number of knowledge through occurred interactions. This is a qualitative research with an observation method followed up by a focus group discussion (FGD). The research result shows that there is a reciprocal interaction between the behaviors of teachers and children affected by the size of the classroom and learning source, teaching experiences, education background, teachers’ attitude and motivation, as well as the way the teachers interpret and support the children’s needs. The teachers involved in this research came up with varied perspective on how knowledge acquired by children at first and how they construct it. This research brings a new perspective in understanding children as scientists.

Keywords: constructivist approach, young children as a scientist, teacher practice, teacher education

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Authors: Hyun-Woo Cho

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Detection of incipient abnormal events in production processes is important to improve safety and reliability of manufacturing operations and reduce losses caused by failures. The construction of calibration models for predicting faulty conditions is quite essential in making decisions on when to perform preventive maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of process measurement data. The calibration model is used to predict faulty conditions from historical reference data. This approach utilizes variable selection techniques, and the predictive performance of several prediction methods are evaluated using real data. The results shows that the calibration model based on supervised probabilistic model yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: calibration model, monitoring, quality improvement, feature selection

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Authors: Gul Rahim Rahimi

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The IoT-based soil moisture monitoring system for indoor plants is designed to address the challenges of maintaining optimal moisture levels in soil for plant growth and health. The system utilizes sensor technology to collect real-time data on soil moisture levels, which is then processed and analyzed using machine learning algorithms. This allows for accurate and timely monitoring of soil moisture levels, ensuring plants receive the appropriate amount of water to thrive. The main objectives of the system are twofold: to keep plants fresh and healthy by preventing water deficiency and to provide users with comprehensive insights into the water content of the soil on a daily and hourly basis. By monitoring soil moisture levels, users can identify patterns and trends in water consumption, allowing for more informed decision-making regarding watering schedules and plant care. The scope of the system extends to the agriculture industry, where it can be utilized to minimize the efforts required by farmers to monitor soil moisture levels manually. By automating the process of soil moisture monitoring, farmers can optimize water usage, improve crop yields, and reduce the risk of plant diseases associated with over or under-watering. Key technologies employed in the system include the Capacitive Soil Moisture Sensor V1.2 for accurate soil moisture measurement, the Node MCU ESP8266-12E Board for data transmission and communication, and the Arduino framework for programming and development. Additionally, machine learning algorithms are utilized to analyze the collected data and provide actionable insights. Cloud storage is utilized to store and manage the data collected from multiple sensors, allowing for easy access and retrieval of information. Overall, the IoT-based soil moisture monitoring system offers a scalable and efficient solution for indoor plant care, with potential applications in agriculture and beyond. By harnessing the power of IoT and machine learning, the system empowers users to make informed decisions about plant watering, leading to healthier and more vibrant indoor environments.

Keywords: IoT-based, soil moisture monitoring, indoor plants, water management

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Authors: Yash Goyal, Manish Korde, Juned Siddiqui

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Today online education has gained more popularity so that people can easily complete their curriculum on their own time. Virtual learning has been widely used by many educators, especially in higher education institutions due to its benefits to students and faculty. A good knowledge of teaching theory and instructional design systems is required to experience meaningful learning. However, most educational websites are not designed to adapt to all screen sizes. Making the website accessible on all screen sizes is our main objective, so we have created a website that is readily accessible across all screen sizes and accepts all types of payment methods. And we see generally educational websites interface is simple and unexciting. So, we have made a user interface attractive and user friendly. It is not enough for a website to be user-friendly, but also to be familiar to admins and to reduce the workload of the admin as well. We visited so many popular websites under development that they all had issues like responsiveness, simple interface, security measures, payment methods, etc. To overcome this limitation, we have created a website which has taken care of security issues that is why we have created only one admin id and it can be control from that only. And if the user has successfully done the payment, then the admin can send him a username and password through mail individually so there will no fraud in the payment of the course.

Keywords: responsive, accessible, attractive, interface, objective, security.

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Authors: Maha Rashid Al-Azib, Almuzn Qasem Alqathradi, Amal Munir Alshahrani, Bilqis Mohammed Assiri, Ali Almuflih

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The Occupational Safety and Health Management System in one of the largest Saudi companies has been experiencing in the last 10 years extensive direct and indirect expenses due to lack of proactive leading indicators and safety leadership effective procedures. And since there are no studies that are associated with this department of safety in the company, this research has been conducted. In this study we used a mixed method approach containing a literature review and experts input, then a qualitative questionnaire provided by Institute for Work and Health related to determining the company’s occupational safety and health management system level out from three levels (Compliance - Improvement - Continuous Learning) and the output regarding the company’s level was in Continuous Learning. After that Deming cycle was employed to create a set of proactive leading indicators and analyzed using the SMART method to make sure of its effectiveness and suitability to the company. The objective of this research is to provide a set of proactive indicators to contribute in making an efficient occupational safety and health management system that has less accidents which results in less expenses. Therefore, we provided the company with a prototype of an APP, designed and empowered with our final results to contribute in supporting decisions making processes.

Keywords: proactive leading indicators, OSH MS, safety leadership, accidents reduction

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Authors: Floriane Shala, Evangeline Wagner, Yichun Zhao

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Background: Proper educational training and support are proven to be major components of EHR (Electronic Health Record) implementation and use. However, the majority of health providers are not sufficiently trained in EHR use, leading to adverse events, errors, and decreased quality of care. In response to this, students studying Health Information Science, Public Health, Nursing, and Medicine should all gain a thorough understanding of EHR use at different levels for different purposes. The design of a usable and safe EHR system that accommodates the needs and workflows of different users, user groups, and disciplines is required for EHR learning to be efficient and effective. Objectives: This project builds several artifacts which seek to address both the educational and usability aspects of an educational EHR. The artifacts proposed are models for and examples of such an EHR with a configurable UI to be learned by students who need a background in EHR use during their degrees. Methods: Review literature and gather professional opinions from domain experts on usability, the use of workflow patterns, UI configurability and design, and the educational aspect of EHR use. Conduct interviews in a semi-casual virtual setting with open discussion in order to gain a deeper understanding of the principal aspects of EHR use in educational settings. Select a specific task and user group to illustrate how the proposed solution will function based on the current research. Develop three artifacts based on the available research, professional opinions, and prior knowledge of the topic. The artifacts capture the user task and user’s interactions with the EHR for learning. The first generic model provides a general understanding of the EHR system process. The second model is a specific example of performing the task of MRI ordering with a configurable UI. The third artifact includes UI mock-ups showcasing the models in a practical and visual way. Significance: Due to the lack of educational EHRs, medical professionals do not receive sufficient EHR training. Implementing an educational EHR with a usable and configurable interface to suit the needs of different user groups and disciplines will help facilitate EHR learning and training and ultimately improve the quality of patient care.

Keywords: education, EHR, usability, configurable

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Authors: Renu Tomar, Hitendra K. Malik, Raj P. Dahiya

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Similar to the sound waves in air, plasmas support the propagation of ion waves, which evolve into the solitary structures when the effect of non linearity and dispersion are balanced. The ion acoustic solitary waves have been investigated in details in homogeneous plasmas, inhomogeneous plasmas, and magnetized plasmas. The ion acoustic solitary waves are also found to reflect from a density gradient or boundary present in the plasma after propagating. Another interesting feature of the solitary waves is their collision. In the present work, we carry out analytical calculations for the head-on collision of solitary waves in a magnetized plasma which has dust grains in addition to the ions and electrons. For this, we employ Poincar´e-Lighthill-Kuo (PLK) method. To lowest nonlinear order, the problem of colliding solitary waves leads to KdV (modified KdV) equations and also yields the phase shifts that occur in the interaction. These calculations are accomplished for the uniform and nonuniform plasmas, and the results on the soliton properties are discussed in detail.

Keywords: inhomogeneous magnetized plasma, dust charging, soliton collisions, magnetized plasma

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Authors: Stijn Horck

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Background: The value of lessons learned from failure increases when these insights can be put to use by those who did not experience the failure. While learning from others has mostly been researched between individuals or teams within the same environment, transferring knowledge from the person who experienced the failure to an outsider comes with extra challenges. As sense-making of failure is an individual process leading to different learning experiences, the potential of lessons learned from failure is highly variable depending on who is transferring the lessons learned. Using an integrated framework of linguistic aspects related to attributional egotism, this study aims to offer a complete explanation of the challenges in transferring lessons learned from failures that are experienced by others. Method: A case study of a failed foundation established to address the information needs for GPs in times of COVID-19 has been used. An overview of failure causes and lessons learned were made through a preliminary analysis of data collected in two phases with metaphoric examples of failure types. This was followed up by individual narrative interviews with the board members who have all experienced the same events to analyse the individual variance of lessons learned through discourse analysis. This research design uses the researcher-as-instrument approach since the recipient of these lessons learned is the author himself. Results: Thirteen causes were given why the foundation has failed, and nine lessons were formulated. Based on the individually emphasized events, the explanation of the failure events mentioned by all or three respondents consisted of more linguistic aspects related to attributional egotism than failure events mentioned by only one or two. Moreover, the learning events mentioned by all or three respondents involved lessons learned that are based on changed insight, while the lessons expressed by only one or two are more based on direct value. Retrospectively, the lessons expressed as a group in the first data collection phase seem to have captured some but not all of the direct value lessons. Conclusion: Individual variance in expressing lessons learned to outsiders can be reduced using metaphoric or analogical explanations from a third party. In line with the attributional egotism theory, individuals separated from a group that has experienced the same failure are more likely to refer to failure causes of which the chances to be contradicted are the smallest. Lastly, this study contributes to the academic literature by demonstrating that the use of linguistic analysis is suitable for investigating the knowledge transfer from lessons learned after failure.

Keywords: failure, discourse analysis, knowledge transfer, attributional egotism

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Authors: Ayola D. N. Jayamaha, Gihan V. Dias, Surangika Ranathunga

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Currently there are lot of educational tools designed for mathematics. Open source software such as GeoGebra and Octave are bulky in their architectural structure. In addition, there is MathLab software, which facilitates much more than what we ask for. Many of the computer aided online grading and assessment tools require integrating editors to their software. However, there are not exist suitable editors that cater for all their needs in editing equations and geometrical diagrams and graphs. Some of the existing software for editing equations is Alfred’s Equation Editor, Codecogs, DragMath, Maple, MathDox, MathJax, MathMagic, MathFlow, Math-o-mir, Microsoft Equation Editor, MiraiMath, OpenOffice, WIRIS Editor and MyScript. Some of them are commercial, open source, supports handwriting recognition, mobile apps, renders MathML/LaTeX, Flash / Web based and javascript display engines. Some of the diagram editors are GeoKone.NET, Tabulae, Cinderella 1.4, MyScript, Dia, Draw2D touch, Gliffy, GeoGebra, Flowchart, Jgraph, JointJS, J painter Online diagram editor and 2D sketcher. All these software are open source except for MyScript and can be used for editing mathematical diagrams. However, they do not fully cater the needs of a typical computer aided assessment tool or Educational Platform for Mathematics. This solution provides a Web based, lightweight, easy to implement and integrate solution of an html5 canvas that renders on all of the modern web browsers. The scope of the project is an editor that covers equations and mathematical diagrams and drawings on the O/L Mathematical Exam Papers in Sri Lanka. Using the tool the students can enter any equation to the system which can be on an online remote learning platform. The users can also create and edit geometrical drawings, graphs and do geometrical constructions that require only Compass and Ruler from the Editing Interface provided by the Software. The special feature of this software is the geometrical constructions. It allows the users to create geometrical constructions such as angle bisectors, perpendicular lines, angles of 600 and perpendicular bisectors. The tool correctly imitates the functioning of rulers and compasses to create the required geometrical construction. Therefore, the users are able to do geometrical drawings on the computer successfully and we have a digital format of the geometrical drawing for further processing. Secondly, we can create and edit Venn Diagrams, color them and label them. In addition, the students can draw probability tree diagrams and compound probability outcome grids. They can label and mark regions within the grids. Thirdly, students can draw graphs (1st order and 2nd order). They can mark points on a graph paper and the system connects the dots to draw the graph. Further students are able to draw standard shapes such as circles and rectangles by selecting points on a grid or entering the parametric values.

Keywords: geometrical drawings, html5 canvas, mathematical equations, toolbox

Procedia PDF Downloads 366

Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

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Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

Procedia PDF Downloads 408

Authors: Antoni Ivanov, Nikolay Dandanov, Nicole Christoff, Vladimir Poulkov

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Spectrum underutilization has made cognitive radio a promising technology both for current and future telecommunications. This is due to the ability to exploit the unused spectrum in the bands dedicated to other wireless communication systems, and thus, increase their occupancy. The essential function, which allows the cognitive radio device to perceive the occupancy of the spectrum, is spectrum sensing. In this paper, the performance of modern adaptations of the four most widely used spectrum sensing techniques namely, energy detection (ED), cyclostationary feature detection (CSFD), matched filter (MF) and eigenvalues-based detection (EBD) is compared. The implementation has been accomplished through the PlutoSDR hardware platform and the GNU Radio software package in very low Signal-to-Noise Ratio (SNR) conditions. The optimal detection performance of the examined methods in a realistic implementation-oriented model is found for the common relevant parameters (number of observed samples, sensing time and required probability of false alarm).

Keywords: cognitive radio, dynamic spectrum access, GNU Radio, spectrum sensing

Procedia PDF Downloads 235

Authors: Francisca Margarita Ocran

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Underwear, and more particularly bras and panties, are defined as intimate clothing. Strictly speaking, they enhance the place of women in the public or private satchel. Therefore, women's lingerie is a complex garment with a high involvement profile, motivating consumers to buy it not only by its functional utility but also by the multisensory experience it provides them. Customer behavior models are generally based on customer data mining, and each model is designed to answer questions at a specific time. Predicting the customer experience is uncertain and difficult. Thus, knowledge of consumers' tastes in lingerie deserves to be treated as an experiential product, where the dimensions of the experience motivating consumers to buy a lingerie product and to remain faithful to it must be analyzed in detail by the manufacturers and retailers to engage and retain consumers, which is why this research aims to identify the variables that push consumers to choose their lingerie product, based on an in-depth analysis of the types of fabrics used to make lingerie. The data used in this study comes from online purchases. Machine learning approach with the use of Python programming language and Pycaret gives us a precision of 86.34%, 85.98%, and 84.55% for the three algorithms to use concerning the preference of a buyer in front of a range of lingerie. Gradient Boosting, random forest, and K Neighbors were used in this study; they are very promising and rich in the classification of preference in the textile industry.

Keywords: consumer behavior, data mining, lingerie, machine learning, preference

Procedia PDF Downloads 77

Authors: Susan Catapano, Meredith Jones, Carol McNulty

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Analysis of one state’s collaborative effort to support teachers, in both public and private schools, as they moved from face-to-face teaching to remote teaching during the Covid pandemic to identify lessons learned and materials put into place to support teachers and families. Surveys were created, distributed, and analyzed throughout the three months of remote teaching, documents and lesson plans were developed, and training materials were created. All data collected and materials developed were analyzed to identify supports teachers used and needed for successful remote teaching. Researchers found most teachers easily moved to online teaching; however, many families did not have access to technology, so teachers needed to develop non-technology-based access and support for remote teaching. Teachers also reported the need to prepare to teach remotely as part of their teaching training, so they were prepared in the future. Finally, data indicated teachers were able to establish stronger relationships with families than usual as a result of remote teaching. The lessons learned and support developed are part of the state’s ongoing policy for online teaching in the event of disasters and pandemics in the future.

Keywords: remote learning, teacher education, pandemic, families

Procedia PDF Downloads 152

Authors: A. Gonzalez Aguna, J. M. Santamaria Garcia, J. L. Gomez Gonzalez, R. Barchino Plata, M. Fernandez Batalla, S. Herrero Jaen

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Background: Computer Science is a field that transcends other disciplines of knowledge because it allows to support all kinds of physical and mental tasks. Health centres have a greater number and complexity of technological devices and the population consume and demand services derived from technology. Also, nursing education plans have included competencies related to and, even, courses about new technologies are offered to health professionals. However, nurses still limit their performance to the use and evaluation of products previously built. Objective: Develop a teaching-learning methodology for acquiring skills on designing applications for care. Methodology: Blended learning teaching with a group of graduate nurses through official training within a Master's Degree. The study sample was selected by intentional sampling without exclusion criteria. The study covers from 2015 to 2017. The teaching sessions included a four-hour face-to-face class and between one and three tutorials. The assessment was carried out by written test consisting of the preparation of an IEEE 830 Standard Specification document where the subject chosen by the student had to be a problem in the area of care. Results: The sample is made up of 30 students: 10 men and 20 women. Nine students had a degree in nursing, 20 diploma in nursing and one had a degree in Computer Engineering. Two students had a degree in nursing specialty through residence and two in equivalent recognition by exceptional way. Except for the engineer, no subject had previously received training in this regard. All the sample enrolled in the course received the classroom teaching session, had access to the teaching material through a virtual area and maintained at least one tutoring. The maximum of tutorials were three with an hour in total. Among the material available for consultation was an example of a document drawn up based on the IEEE Standard with an issue not related to care. The test to measure competence was completed by the whole group and evaluated by a multidisciplinary teaching team of two computer engineers and two nurses. Engineers evaluated the correctness of the characteristics of the document and the degree of comprehension in the elaboration of the problem and solution elaborated nurses assessed the relevance of the chosen problem statement, the foundation, originality and correctness of the proposed solution and the validity of the application for clinical practice in care. The results were of an average grade of 8.1 over 10 points, a range between 6 and 10. The selected topic barely coincided among the students. Examples of care areas selected are care plans, family and community health, delivery care, administration and even robotics for care. Conclusion: The applied methodology of learning-teaching for the design of technologies demonstrates the success in the training of nursing professionals. The role of expert is essential to create applications that satisfy the needs of end users. Nursing has the possibility, the competence and the duty to participate in the process of construction of technological tools that are going to impact in care of people, family and community.

Keywords: care, learning, nursing, technology

Procedia PDF Downloads 131

Authors: Soumya Mishra, Elena Neiterman

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Aligned with the growing interest in the impact of the pandemic on academic experiences of university students, this study aimed to examine the challenges Canadian undergraduate students experienced during the university closures due to COVID-19. Using qualitative methodological approach, the study utilized semi-structured interviews conducted with 20 undergraduate students enrolled in an Ontario university to explore their thoughts and experience regarding online learning during the peak of the COVID-19 pandemic, from January 2021 to March 2021. The interviews yielded four major themes with the following associated subthemes: Personal Challenges Associated with Adapting to the Pandemic (Change in the Type of Stress Experienced, Unique Impact on Certain Groups of Students, Decreased Motivation, Crucial Role of Resilience), Social Challenges Associated with Adapting to the Pandemic (Increased Loneliness, Challenges Faced while Communicating, Perception of Group work, Role of Living Conditions), Challenges associated with Accessing University Resources (Crucial Role of Professors, Perception of Virtual Events, Importance of Physical Spaces). Overall, the analysis showed that the COVID-19 pandemic fostered resilience and psychological flexibility amongst all students. However, the mental health and social wellbeing of students deteriorated during the COVID-19 pandemic and they reported experiencing chronic stress, anxiety and loneliness. International students, first year and final year students experienced a unique set of challenges. It was hard for participants in our study to make strong new connections with their classmates and maintain existing friendships with their peers. The importance of professors in facilitating learning was amplified in the online environment due to the lack of in-person interaction with other students. Despite these challenges, most participants reported that they received high grades during online learning. The findings from this study could be helpful for organizations and individuals working towards fostering the wellbeing of undergraduate students. They can also help in making post-secondary institutions more resilient to future emergencies by creating contingency plans regarding online instructions and risk management techniques.

Keywords: Canadian, COVID-19, university students, wellbeing

Procedia PDF Downloads 89

Authors: Zdeněk Veselý, Milan Honner, Jiří Mach

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The aim of the performed work is to establish the 2D and 3D model of direct unsteady task of sample heat treatment by moving source employing computer model on the basis of finite element method. The complex boundary condition on heat loaded sample surface is the essential feature of the task. Computer model describes heat treatment of the sample during heat source movement over the sample surface. It is started from the 2D task of sample cross section as a basic model. Possibilities of extension from 2D to 3D task are discussed. The effect of the addition of third model dimension on the temperature distribution in the sample is showed. Comparison of various model parameters on the sample temperatures is observed. Influence of heat source motion on the depth of material heat treatment is shown for several velocities of the movement. Presented computer model is prepared for the utilization in laser treatment of machine parts.

Keywords: computer simulation, unsteady model, heat treatment, complex boundary condition, moving heat source

Procedia PDF Downloads 383

Authors: Isaac Benning

Abstract:

Using technology to expand the learning space is critical for a productive mathematical discourse. This is a case study of two teachers who developed and enacted GeoGebra-based mathematics lessons following their engagement in a two-year professional development. The didactical and semiotic affordance of GeoGebra in widening the learning space for a productive mathematical discourse was explored. The approach of thematic analysis was used for lesson artefact, lesson observation, and interview data. The results indicated that constructing tools in GeoGebra provided a didactical milieu where students used them to explore mathematical concepts with little or no support from their teacher. The prompt feedback from the GeoGebra motivated students to practice mathematical concepts repeatedly in which they privately rethink their solutions before comparing their answers with that of their colleagues. The constructing tools enhanced self-discovery, team spirit, and dialogue among students. With regards to the semiotic construct, the tools widened the physical and psychological atmosphere of the classroom by providing animations that served as virtual concrete to enhance the recording, manipulation, testing of a mathematical idea, construction, and interpretation of geometric objects. These findings advance the discussion of widening the classroom for a productive mathematical discourse within the context of the mathematics curriculum of Ghana and similar Sub-Saharan African countries.

Keywords: GeoGebra, theory of didactical situation, semiotic mediation, mathematics laboratory, mathematical discussion

Procedia PDF Downloads 116

Authors: Jiangbo Hu, Frances Hoyte, Haiquan Huang

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Research identifies that mealtime talk can be a significant learning context that provides children with rich experiences to foster their language and cognitive development. Middle-classed parents create an extended learning discourse for their children through sophisticated vocabulary, narrative and explanation genres at dinner table. However, mealtime opportunities vary with some parents having little interaction with their children and some parents focusing on directive of children’s behaviors. This study investigated five Chinese families’ parent-child interaction during mealtime that was rarely reported in the literature. The five families differ in terms of their living styles. Three families are from professional background where both mothers the fathers work in Australian companies and both of them present at dinner time. The other two families own business. The mothers are housemakers and the fathers are always absent at dinner time due to their busy business life. Employing case study method, the five Chinese families’ parent-child interactions at dinner table were recorded using a video camera. More than 3000 clauses were analyzed with the framework of 'systems of clause complexing' from systemic functional linguistic theory. The finding shows that mothers played a critical role in the interaction with their children by initiating most conversations. The three mothers from professional background tended to use more language in extending and expanding pattern that is beneficial for children’s language development and high level of thinking (e.g., logical thinking). The two house making mothers’ language focused more on the directive of their children’s social manners and dietary behaviors. The fathers though seemed to be less active, contributing to the richness of the conversation through their occasional props such as asking open questions or initiating a new topic. In general, the families from professional background were more advantaged in providing learning opportunities for their children at dinner table than the families running business were. The home experiences of Chinese children is an important topic in research due to the rapidly increasing number of Chinese children in Australia and other English speaking countries. Such research assist educators in the education of Chinese children with more awareness of Chinese children experiences at home that could be very unlike the settings in English schools. This study contributes to the research in this area through the analysis of language in parent-child interaction during mealtime, which is very different from previous research that mainly investigated Chinese families through survey and interview. The finding of different manners in language use between the professional families and business families has implication for the understanding of the variation of Chinese children’s home experiences that is influenced not only by parents’ socioeconomic status but their lifestyles.

Keywords: Chinese children, Chinese parents, mealtime talk, parent-child interaction

Procedia PDF Downloads 227