Search results for: observational learning

Authors: Caridad Maylin-Aguilar, Beatriz Duarte-Monedero

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Problem-based learning (PBL) is a useful tool for adult and practice oriented audiences, as University students. As a consequence of the huge disruption caused by the COVID pandemic in the hospitality industry, hotels of all categories closed down in Spain from March 2020. Since that moment, the luxury segment was blooming with optimistic prospects for new openings. Hence, Hospitality students were expecting a positive situation in terms of employment and career development. By the beginning of the 2020-21 academic year, these expectations were seriously harmed. By October 2020, only 9 of the 32 hotels in the luxury segment were opened with an occupation rate of 9%. Shortly after, the evidence of a second wave affecting especially Spain and the homelands of incoming visitors bitterly smashed all forecasts. In accordance with the situation, a team of four professors and practitioners, from four different subject areas, developed a real case, inspired in one of these hotels, the 5-stars Emperatriz by Barceló. Students in their 2nd course were provided with real information as marketing plans, profit and losses and operational accounts, employees profiles and employment costs. The challenge for them was to act as consultants, identifying potential courses of action, related to best, base and worst case. In order to do that, they were organized in teams and supported by 4th course students. Each professor deployed the problem in their subject; thus, research on the customers behavior and feelings were necessary to review, as part of the marketing plan, if the current offering of the hotel was clear enough to guarantee and to communicate a safe environment, as well as the ranking of other basic, supporting and facilitating services. Also, continuous monitoring of competitors’ activity was necessary to understand what was the behavior of the open outlets. The actions designed after the diagnose were ranked in accordance with their impact and feasibility in terms of time and resources. Also they must be actionable by the current staff of the hotel and their managers and a vision of internal marketing was appreciated. After a process of refinement, seven teams presented their conclusions to Emperatriz general manager and the rest of professors. Four main ideas were chosen, and all the teams, irrespectively of authorship, were asked to develop them to the state of a minimum viable product, with estimations of impacts and costs. As the process continues, students are nowadays accompanying the hotel and their staff in the prudent reopening of facilities, almost one year after the closure. From a professor’s point of view, key learnings were 1.- When facing a real problem, a holistic view is needed. Therefore, the vision of subjects as silos collapses, 2- When educating new professionals, providing them with the resilience and resistance necessaries to deal with a problem is always mandatory, but now seems more relevant and 3.- collaborative work and contact with real practitioners in such an uncertain and changing environment is a challenge, but it is worth when considering the learning result and its potential.

Keywords: problem-based learning, hospitality recovery, collaborative learning, resilience

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Authors: Mirza Mujtaba Baig

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Artificial Intelligence (AI) is evolving rapidly, and one of the areas in which this field has influenced is automation. The automobile, healthcare, education, and robotic industries deploy AI technologies constantly, and the automation of tasks is beneficial to allow time for knowledge-based tasks and also introduce convenience to everyday human endeavors. The paper reviews the challenges faced with the current implementations of autonomous self-driving cars by exploring the machine learning, robotics, and artificial intelligence techniques employed for the development of this innovation. The controversy surrounding the development and deployment of autonomous machines, e.g., vehicles, begs the need for the exploration of the configuration of the programming modules. This paper seeks to add to the body of knowledge of research assisting researchers in decreasing the inconsistencies in current programming modules. Blockchain is a technology of which applications are mostly found within the domains of financial, pharmaceutical, manufacturing, and artificial intelligence. The registering of events in a secured manner as well as applying external algorithms required for the data analytics are especially helpful for integrating, adapting, maintaining, and extending to new domains, especially predictive analytics applications.

Keywords: artificial intelligence, automation, big data, self-driving cars, machine learning, neural networking algorithm, blockchain, business intelligence

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Authors: Maria Giovanna Lupo, Marina Camera, Marcello Rattazzi, Nicola Ferri

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A complex interplay among chronic kidney disease, lipid metabolism and aortic calcification has been recognized starting from results of many clinical and experimental studies. Here we investigated the influence of kidney function on PCSK9 levels, both in uremic rats and in clinical observation study, and its potential direct action on cultured smooth muscle cells (SMCs) calcification. In a cohort of 594 subjects enrolled in a single centre, observational, cross-sectional and longitudinal study, a negative association between GFR and plasma PCSK9 was found. Atherosclerotic cardiovascular disease (ASCVD), as co-morbidity, further increased PCSK9 plasma levels. Diet-induced uremic condition in rats, induced aortic calcification and increased total cholesterol and PCSK9 levels in plasma, livers and kidneys. Immunohistochemical analysis confirmed PCSK9 expression in aortic SMCs. SMCs overexpressing PCSK9 (SMCsPCSK9), cultured for 7-days in a pro-calcification environment (2.0mM or 2.4mM inorganic phosphate, Pi) showed a significantly higher extracellular calcium (Ca2+) deposition compared to mocked SMCs. Under the same experimental conditions, the addition of exogenous recombinant PCSK9 did not increase the extracellular calcification of SMCs. By flow cytometry analysis we showed that SMCsPCSK9, in response to 2.4mM Pi, released higher number of extracellular vesicles (EVs) positive for three tetraspanin molecules, such as CD63, CD9, and CD81. EVs derived from SMCsPCSK9 tended to be more enriched in calcium and alkaline phosphatase (ALPL), compared to EVs from mocks SMCs. In conclusion, our study reveals a direct role of PCSK9 on vascular calcification induced by higher inorganic phosphate levels associated to CKD condition. This effect appears to be mediated by a positive effect of endogenous PCSK9 on the release of EVs containing Ca2+ and ALP, which facilitate the deposition inorganic calcium phosphate crystals.

Keywords: PCSK9, calcification, extracellular vesicles, chronic kidney disease

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Authors: Munir Majdalawieh, Adam Marks

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In today’s very competitive higher education industry (HEI), HEIs are faced with the primary concern of developing, deploying, and sustaining high quality academic programs. Today, the HEI has well-established accreditation systems endorsed by a country’s legislation and institutions. The accreditation system is an educational pathway focused on the criteria and processes for evaluating educational programs. Although many aspects of the accreditation process highlight both the past and the present (prove), the “program review” assessment is "forward-looking assessment" (improve) and thus transforms the process into a continuing assessment activity rather than a periodic event. The purpose of this study is to propose a conceptual measurement framework for program review to be used by HEIs to undertake a robust and targeted approach to proactively and continuously review their academic programs to evaluate its practicality and effectiveness as well as to improve the education of the students. The proposed framework consists of two main components: program review principles and the program review measurement matrix.

Keywords: academic program, program review principles, curriculum development, accreditation, evaluation, assessment, review measurement matrix, program review process, information technologies supporting learning, learning/teaching methodologies and assessment

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Authors: Leila Goosen

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The research problem reported on in this paper relates to improving success in Computer Science and Information Technology subjects where students are learning applications, especially when teaching occurs in a distance education context. An investigation was launched in order to address students’ struggles with applications, and improve their assessment in such subjects. Some of the main arguments presented centre on formulating and situating significant concepts within an appropriate conceptual framework. The paper explores the experiences and perceptions of computing instructors, teaching assistants, students and higher education institutions on how they are empowered by using technologies such as myITLab. They also share how they are working with the available features to successfully teach applications to their students. The data collection methodology used is then described. The paper includes discussions on how myITLab empowers instructors, teaching assistants, students and higher education institutions. Conclusions are presented on the way in which this paper could make an original and significant contribution to the promotion and development of knowledge in fields related to successfully teaching applications for student learning, including in a distance education context. The paper thus provides a forum for practitioners to highlight and discuss insights and successes, as well as identify new technical and organisational challenges, lessons and concerns regarding practical activities related to myITLab as an implementation instance of distance education technologies.

Keywords: distance, education, myITLab, technologies

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Authors: Emily Jin, Harry Sharples, Anne Weist

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Introduction: NICE Evidence Search Student Champion Scheme is a peer-assisted learning scheme that aims to improve the routine use of evidence-based information by future health and social care staff. The focus is on the NICE evidence search portal that provides selected information from more than 800 reliable health, social care, and medicines sources, including up-to-date guidelines and information for the public. This paper aims to evaluate the effectiveness of the scheme when implemented in Liverpool School of Medicine and to understand the experiences of those attending. Methods: Twelve student champions were recruited and trained in February 2020 as peer tutors during a workshop facilitated by NICE. Cascade sessions were then organised and delivered on an optional basis for students, in small groups of < 10 to approximately 70 attendees. Surveys were acquired immediately before and 8-12 weeks after cascade sessions (n=47 and 45 respectively). Data from these surveys facilitated the analysis of the scheme. Results: Surveys demonstrated 74% of all attendees frequently searched for health and social care information online as a part of their studies. However, only 15% of attendees reported having prior formal training on searching for health information, despite receiving such training earlier on in the curriculum. After attending cascade sessions, students reported a 58% increase in confidence when searching for information using evidence search, from a pre-session a baseline of 36%. Conclusion: NICE Evidence Search Student Champion Scheme provided clear benefits for attending students, increasing confidence in searching for peer-reviewed, mainly secondary sources of health information. The lack of reported training represents the unmet need that the champion scheme satisfies, and this likely benefits student champions as well as attendees. Increasing confidence in searching for healthcare information online may support future evidence-based decision-making.

Keywords: evidence-based medicine, NICE, medical education, medical school, peer-assisted learning

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Authors: David Chung

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The development of seventeenth-century French harpsichord music is enigmatic in several respects. Although little is known about the formation of this style before 1650 (we have names of composers, but no surviving music), the style has attained a high degree of refinement and sophistication in the music of the earliest known masters (e.g. Chambonnières, Louis Couperin and D’Anglebert). In fact, how the seventeenth-century musicians acquired the skills of their art remains largely steeped in mystery, as the earliest major treatise on French keyboard pedagogy was not published until 1702 by Saint Lambert. This study fills this lacuna by surveying some twenty recently-rediscovered manuscripts, which offer ample materials for revisiting key issues pertaining to seventeenth-century harpsichord pedagogy. By analyzing the musical contents, the verbal information and explicit notation (such as written-out ornaments and rhythmic effects), this study provides a rich picture of the process of learning at the time, with engaging details of performance nuances often lacking in tutors and treatises. Of even greater significance, that creative skills (such as continuo and ornamentation) were taught alongside fundamental knowledge (solfèges, note values, etc.) at the earliest stage of learning offers fresh challenge for modern pedagogues to rethink how harpsichord pedagogy can be revamped to cater for our own pedagogical and aesthetic needs.

Keywords: French, harpsichord, pedagogy, seventeenth century

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Authors: Mitzi S. Brammer

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Given that the cultural make-up of programs of study in institutions of higher learning is becoming increasingly diverse, much has been written about cultural diversity from a university-level perspective. However, there are little data in the way of specific programs and how they address inclusive practices when teaching and working with marginalized populations. This research study aimed to discover baseline knowledge and attitudes of health sciences faculty, instructional staff, and students related to inclusive teaching/learning and interactions. Quantitative data were collected via an anonymous online survey (one designed for students and another designed for faculty/instructional staff) using a web-based program called Qualtrics. Quantitative data were analyzed amongst the faculty/instructional staff and students, respectively, using descriptive and comparative statistics (t-tests). Additionally, some participants voluntarily engaged in a focus group discussion in which qualitative data were collected around these same variables. Collecting qualitative data to triangulate the quantitative data added trustworthiness to the overall data. The research team analyzed collected data and compared identified categories and trends, comparing those data between faculty/staff and students, and reported results as well as implications for future study and professional practice.

Keywords: inclusion, higher education, pedagogy, equity, diversity

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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: 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: Subham Ghosh, Banani Basu, Marami Das

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Background: The development of compact, low-power antenna sensors has resulted in hardware restructuring, allowing for wireless ubiquitous sensing. The antenna sensors can create wireless body-area networks (WBAN) by linking various wireless nodes across the human body. WBAN and IoT applications, such as remote health and fitness monitoring and rehabilitation, are becoming increasingly important. In particular, Parkinson’s disease (PD), a common neurodegenerative disorder, presents clinical features that can be easily misdiagnosed. As a mobility disease, it may greatly benefit from the antenna’s nearfield approach with a variety of activities that can use WBAN and IoT technologies to increase diagnosis accuracy and patient monitoring. Methodology: This study investigates the feasibility of leveraging a single patch antenna mounted (using cloth) on the wrist dorsal to differentiate actual Parkinson's disease (PD) from false PD using a small hardware platform. The semi-flexible antenna operates at the 2.4 GHz ISM band and collects reflection coefficient (Γ) data from patients performing five exercises designed for the classification of PD and other disorders such as essential tremor (ET) or those physiological disorders caused by anxiety or stress. The obtained data is normalized and converted into 2-D representations using the Gabor wavelet transform (GWT). Data augmentation is then used to expand the dataset size. A lightweight deep-learning (DL) model is developed to run on the GPU-enabled NVIDIA Jetson Nano platform. The DL model processes the 2-D images for feature extraction and classification. Findings: The DL model was trained and tested on both the original and augmented datasets, thus doubling the dataset size. To ensure robustness, a 5-fold stratified cross-validation (5-FSCV) method was used. The proposed framework, utilizing a DL model with 1.356 million parameters on the NVIDIA Jetson Nano, achieved optimal performance in terms of accuracy of 88.64%, F1-score of 88.54, and recall of 90.46%, with a latency of 33 seconds per epoch.

Keywords: antenna, deep-learning, GPU-hardware, Parkinson’s disease

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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: Ishith Seth, Lyndel Hewitt, Takako Yabe, James Wykes, Jonathan Clark, Bruce Ashford

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Background: Vastus lateralis (VL) can be used to reconstruct defects of the head and neck. Whilst the advantages are documented, donor-site morbidity is not well described. This study aimed to assess donor-site morbidity after VL flap harvest. The results will determine future directions for preventative and post-operative care to improve patient health outcomes. Methods: Ten participants (mean age 55 years) were assessed for the presence of donor-site morbidity after VL harvest. Musculoskeletal (pain, muscle strength, muscle length, tactile sensation), quality of life (SF-12), and lower limb function (lower extremity function, gait (function and speed), sit to stand were assessed using validated and standardized procedures. Outcomes were compared to age-matched healthy reference values or the non-operative side. Analyses were conducted using descriptive statistics and non-parametric tests. Results: There was no difference in muscle strength (knee extension), muscle length, ability to sit-to-stand, or gait function (all P > 0.05). Knee flexor muscle strength was significantly less on the operated leg compared to the non-operated leg (P=0.02) and walking speed was slower than age-matched healthy values (P<0.001). Thigh tactile sensation was impaired in 89% of participants. Quality of life was significantly less for the physical health component of the SF-12 (P<0.001). The mental health component of the SF-12 was similar to healthy controls (P=0.26). Conclusion: There was no effect on donor site morbidity with regards to knee extensor strength, pain, walking function, ability to sit-to-stand, and muscle length. VL harvest affected donor-site knee flexion strength, walking speed, tactile sensation, and physical health-related quality of life.

Keywords: vastus lateralis, morbidity, head and neck, surgery, donor-site morbidity

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Authors: Mardiana Madjid, Nurpudji Astuti Taslim, Suryani As'ad, Haerani Rasyid, Agussalim Bukhari

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The high level of Urine Urea Nitrogen (UUN) indicates hypercatabolism occurs in hospitalized patients. High levels of Total Lymphocyte Count (TLC) indicates the immune system condition, adequate wound healing, and limit complication. Adequate dietary intake affects to decrease of hypercatabolism status in treated patient’s hospitals. Nitrogen Balance (NB) is simply the difference between nitrogen (N₂) intake and output. If more N₂ intake than output, then positive NB or anabolic will occur. This study aims to evaluate the effect of dietary intake in influencing balance nitrogen and total lymphocyte count. Method: A total of 43 patients admitted to a Wahidin Sudirohusodo Hospital between 2018 and 2019 for 10 days' treats are included. The inclusion criteria were patients who were treated for 10 days and receives food from the hospital orally. Patients did not experience gastrointestinal disorders such as vomiting and diarrhea and experience impair kidney function and liver function and expressed approval to participate in this study. During hospitalization, food intake, UUN, albumin serum, balance nitrogen, and TLC was assessed twice on day 1 and day 10. There is no Physician Clinical Nutritional intervention to correct food intake. UUN is 24 hours of urine collected on the second day after admission and the tenth day. Statistical analysis uses SPSS 24 with observational cohort methods. Result: The Forty-three participants completed the follow-up (27 men and 18 women). The age of fewer than 4 years is 22 people, 45 to 60 years is 16 people, and over 60 years is 4 people. The result of the study on day 1 obtained SGA score A, SGA score B, SGA score C are 8, 32, 3 until day 10 are 8, 31, 4, respectively. According to 24h dietary recalls, the energy intake during observation was from 522.5 ± 400.4 to 1011.9 ± 545.1 kcal/day P < 0.05, protein intake from 20.07 ± 17.2 to 40.3 ± 27.3 g/day P < 0.05, carbohydrates from 92.5 ± 71.6 to 184.8 ± 87.4 g/day, and fat from 5.5 ± 3.86 to 13.9 ± 13.9 g/day. The UUN during the observation was from 6.6 ± 7.3 to 5.5 ± 3.9 g/day, TLC decreased from 1622.9 ± 897.2 to 1319.9 ± 636.3/mm³ value target 1800/mm³, albumin serum from 3.07 ± 0.76 to 2.9 ± 0.57 g/day, and BN from -7.5 ± 7.2 to -3.1 ± 4.86. Conclusion: The high level of UUN needs to correct adequate dietary intake to improve NB and TLC status on hospitalized patients.

Keywords: adequate dietary intake, balance nitrogen, total lymphocyte count, urine urea nitrogen

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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: Saeed Farhadypour, Majid Azadi, Habibolla Sayyari, Mahmood Mosavi, Shahram Irani, Aliakbar Bidokhti, Omid Alizadeh Choobari, Ziba Hamidi

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In the recent decades, frequencies of dust events have increased significantly in west and south west of Iran. First, a survey on the dust events during the period (1990-2013) is investigated using historical dust data collected at 6 weather stations scattered over west and south-west of Iran. After statistical analysis of the observational data, one of the most severe dust storm event that occurred in the region from 3rd to 6th July 2009, is selected and analyzed. WRF-Chem model is used to simulate the amount of PM10 and how to transport it to the areas. The initial and lateral boundary conditions for model obtained from GFS data with 0.5°×0.5° spatial resolution. In the simulation, two aerosol schemas (GOCART and MADE/SORGAM) with 3 options (chem_opt=106,300 and 303) were evaluated. Results of the statistical analysis of the historical data showed that south west of Iran has high frequency of dust events, so that Bushehr station has the highest frequency between stations and Urmia station has the lowest frequency. Also in the period of 1990 to 2013, the years 2009 and 1998 with the amounts of 3221 and 100 respectively had the highest and lowest dust events and according to the monthly variation, June and July had the highest frequency of dust events and December had the lowest frequency. Besides, model results showed that the MADE / SORGAM scheme has predicted values and trends of PM10 better than the other schemes and has showed the better performance in comparison with the observations. Finally, distribution of PM10 and the wind surface maps obtained from numerical modeling showed that the formation of dust plums formed in Iraq and Syria and also transportation of them to the West and Southwest of Iran. In addition, comparing the MODIS satellite image acquired on 4th July 2009 with model output at the same time showed the good ability of WRF-Chem in simulating spatial distribution of dust.

Keywords: dust storm, MADE/SORGAM scheme, PM10, WRF-Chem

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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: Adriana Rivera Vega, Micheal McCaurhty, Christina Cipriano

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The world is becoming increasingly more diverse- ethnically, racially, and socially. Additionally, racial/ethnic minority students with intellectual and developmental disabilities (IDD) tend to be disproportionately represented in more restrictive special education classrooms than in general education classrooms. Inclusive practices play a significant role in the lives of individuals with IDD). A student's cultural identity also plays a salient role in teaching, learning, and student outcomes. It is, however, unclear whether and how the cultural identities of students with IDD are reflected in terminology, definitions, and practices related to inclusive education. As a part of a larger scoping review investigating inclusive practices for youth with IDD, this secondary study examines one facet of inclusion: cultural identity. Previous research suggests that students with IDD benefit from interventions based on their cultural background. A review of the limited peer-reviewed and grey literature on this subject revealed that the terminology, definitions, and practices around inclusive education tend to overlook students’ cultural identity in the teaching and learning processes for this population. Implications for future research are presented and recommendations for inclusive-based theoretical frameworks and inclusive practices using a cultural identity perspective are discussed.

Keywords: education, Psychology, policy, Multicultural Psychology

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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: Marcos Silva Tavares, Jamile Raquel Regazzo, Edson José de Souza Sardinha, Murilo Mesquita Baesso

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Common beans are a widely cultivated and consumed legume globally, serving as a staple food for humans, especially in developing countries, due to their nutritional characteristics. Nitrogen (N) is the most limiting nutrient for productivity, and foliar analysis is crucial to ensure balanced nitrogen fertilization. Excessive N applications can cause, either isolated or cumulatively, soil and water contamination, plant toxicity, and increase their susceptibility to diseases and pests. However, the quantification of N using conventional methods is time-consuming and costly, demanding new technologies to optimize the adequate supply of N to plants. Thus, it becomes necessary to establish constant monitoring of the foliar content of this macronutrient in plants, mainly at the V4 stage, aiming at precision management of nitrogen fertilization. In this work, the objective was to evaluate the performance of a deep learning model, Resnet-50, in the classification of foliar nitrogen in common beans using RGB images. The BRS Estilo cultivar was sown in a greenhouse in a completely randomized design with four nitrogen doses (T1 = 0 kg N ha-1, T2 = 25 kg N ha-1, T3 = 75 kg N ha-1, and T4 = 100 kg N ha-1) and 12 replications. Pots with 5L capacity were used with a substrate composed of 43% soil (Neossolo Quartzarênico), 28.5% crushed sugarcane bagasse, and 28.5% cured bovine manure. The water supply of the plants was done with 5mm of water per day. The application of urea (45% N) and the acquisition of images occurred 14 and 32 days after sowing, respectively. A code developed in Matlab© R2022b was used to cut the original images into smaller blocks, originating an image bank composed of 4 folders representing the four classes and labeled as T1, T2, T3, and T4, each containing 500 images of 224x224 pixels obtained from plants cultivated under different N doses. The Matlab© R2022b software was used for the implementation and performance analysis of the model. The evaluation of the efficiency was done by a set of metrics, including accuracy (AC), F1-score (F1), specificity (SP), area under the curve (AUC), and precision (P). The ResNet-50 showed high performance in the classification of foliar N levels in common beans, with AC values of 85.6%. The F1 for classes T1, T2, T3, and T4 was 76, 72, 74, and 77%, respectively. This study revealed that the use of RGB images combined with deep learning can be a promising alternative to slow laboratory analyses, capable of optimizing the estimation of foliar N. This can allow rapid intervention by the producer to achieve higher productivity and less fertilizer waste. Future approaches are encouraged to develop mobile devices capable of handling images using deep learning for the classification of the nutritional status of plants in situ.

Keywords: convolutional neural network, residual network 50, nutritional status, artificial intelligence

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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

Procedia PDF Downloads 158

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

Procedia PDF Downloads 117

Authors: Shaik Asha Begum, S. Joshna Rani, Shaik Abdul Rahaman

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INTRODUCTION: Breast cancer is a disease that creates in breast cells. "KAP" study estimates the Knowledge, Attitude, and Practices of a local area. More than 1.5 million ladies (25% of all ladies with malignancy) are determined to have bosom disease consistently all through the world. Understanding the degrees of Knowledge, Attitude and Practice will empower a more effective cycle of mindfulness creation as it will permit the program to be custom-made all the more properly to the necessities of the local area. OBJECTIVES: The objective of this study is to assess the knowledge on signs and symptoms, risk factors, provide awareness on the practicing of the early detection techniques of breast cancer and provide knowledge on the overall breast cancer including preventive techniques. METHODOLOGY: This is an expressive cross-sectional investigation. This investigation of KAP was done in the Nirmala Educational Institutions from January to April 2021. A total of 300 participants are included from women students in pharmacy graduates & lecturers, and also from graduates other than the pharmacy. The examiners are taken from the BCAM (Breast Cancer Awareness Measure), tool compartment (Version 2). RESULT: According to the findings of the study, the majority of the participants were not well informed about breast cancer. A lump in the breast was the most commonly mentioned sign of breast cancer, followed by pain in the breast or nipple. The percentage of knowledge related to the breast cancer risk factors was also very less. The correct answers for breast cancer risk factors were radiation exposure (58.20 percent), a positive family history (47.6 percent), obesity (46.9 percent), a lack of physical activity (43.6 percent), and smoking (43.2 percent). Breast cancer screening, on the other hand, was uncommon (only 30 and 11.3 percent practiced clinical breast examination and mammography respectively). CONCLUSION: In this study, the knowledge on the signs and symptoms, risk factors of breast cancer - pharmacy graduates have more knowledge than the non-pharmacy graduates but in the preventive techniques and early detective tools of breast cancer -had poor knowledge in the pharmacy and non-pharmacy graduate. After the awareness program, pharmacy and non-pharmacy graduates got supportive knowledge on the preventive techniques and also practiced the early detective techniques of breast cancer.

Keywords: breast cancer, mammography, KAP study, early detection

Procedia PDF Downloads 138