Search results for: emergency remote learning
5035 Bridging the Data Gap for Sexism Detection in Twitter: A Semi-Supervised Approach
Authors: Adeep Hande, Shubham Agarwal
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This paper presents a study on identifying sexism in online texts using various state-of-the-art deep learning models based on BERT. We experimented with different feature sets and model architectures and evaluated their performance using precision, recall, F1 score, and accuracy metrics. We also explored the use of pseudolabeling technique to improve model performance. Our experiments show that the best-performing models were based on BERT, and their multilingual model achieved an F1 score of 0.83. Furthermore, the use of pseudolabeling significantly improved the performance of the BERT-based models, with the best results achieved using the pseudolabeling technique. Our findings suggest that BERT-based models with pseudolabeling hold great promise for identifying sexism in online texts with high accuracy.Keywords: large language models, semi-supervised learning, sexism detection, data sparsity
Procedia PDF Downloads 635034 Structural Analysis of Hydro-Turbine Spiral Casing and Stay Ring Using Ansys
Authors: Surjit Angra, Pooja Rani, Vinod Kumar
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In hydro power plant spiral casing and Stay ring is meant to guide the water flow to guide vane and runner. Spiral casing and Stay ring is subjected to static i.e. pressure load as well as fluctuating load acting on the structure due to water hammer effect in water conductor system. Finite element method has been used to calculate stresses on spiral casing and stay ring. These calculations were done for the maximum possible loading under operating condition "LC1 Quick Shut Down”. The design load is reached for the spiral casing and stay ring during the emergency closure of the guide apparatus "LC1 Quick Shut Down”. During this operation the forces from the head cover to the stay ring also reach their maximum.Keywords: hydro-turbine, spiral casing, stay ring, structural analysis
Procedia PDF Downloads 5065033 Philippine National Police Strategies in the Implementation of 'Peace and Order Agenda for Transformation and Upholding of the Rule-Of-Law' Plan 2030
Authors: Ruby A. L. Espineli
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The study assessed the Philippine National Police strategies in the implementation of ‘Peace and Order Agenda for Transformation and Upholding of the Rule-of-Law’ P.A.T.R.O.L Plan 2030. Its operational roadmap presents four perspectives which include resource management, learning and growth, process excellence; and community. Focused group discussion, observation, and distribution of survey questionnaire to selected PNP officers and community members were done to identify and describe the implementation, problems encountered and measures to address the problems of the PNP P.A.T.R.O.L Plan 2030. In resource management, PNP allocates most sufficient funds in providing service firearms, patrol vehicle, and internet connections. In terms of learning and growth, the attitude of PNP officers is relatively higher than their knowledge and skills. Moreover, in terms of process excellence, the PNP use several crime preventions and crime solution strategies to deliver an immediate response to calls of the community. As regards, community perspective, PNP takes effort in establishing partnership with community. It is also interesting to note that PNP officers and community were both undecided on the existence of problems encountered in the implementation of P.A.T.R.O.L Plan 2030. But, they had proactive behavior as they agreed on all the specified measures to address the problems encountered in implementation of PNP P.A.T.R.O.L. Plan 2030. A strategic framework, based on the findings was formulated in this study that could improve and entrench the harmonious working relationship between the PNP and stakeholders in the enhancement of the implementation of PNP P.A.T.R.O.L. Plan 2030.Keywords: community perspectives, learning and growth, process excellence, resource management
Procedia PDF Downloads 2235032 Assessing the Geothermal Parameters by Integrating Geophysical and Geospatial Techniques at Siwa Oasis, Western Desert, Egypt
Authors: Eman Ghoneim, Amr S. Fahil
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Many regions in Egypt are facing a reduction in crop productivity due to environmental degradation. One factor of crop deterioration includes the unsustainable drainage of surface water, leading to salinized soil conditions. Egypt has exerted time and effort to identify solutions to mitigate the surface water drawdown problem and its resulting effects by exploring renewable and sustainable sources of energy. Siwa Oasis represents one of the most favorable regions in Egypt for geothermal exploitation since it hosts an evident cluster of superficial thermal springs. Some of these hot springs are characterized by high surface temperatures and bottom hole temperatures (BHT) ranging between 20°C to 40 °C and 21 °C to 121.7°C, respectively. The depth to the Precambrian basement rock is commonly greater than 440 m, ranging from 440 m to 4724.4 m. It is this feature that makes the locality of Siwa Oasis sufficient for industrial processes and geothermal power production. In this study, BHT data from 27 deep oil wells were processed by applying the widely used Horner and Gulf of Mexico correction methods to obtain formation temperatures. BHT, commonly used in geothermal studies, remains the most abundant and readily available data source for subsurface temperature information. Outcomes of the present work indicated a geothermal gradient ranging from 18 to 42 °C/km, a heat flow ranging from 24.7 to 111.3 m.W.k⁻¹, and a thermal conductivity of 1.3–2.65 W.m⁻¹.k⁻¹. Remote sensing thermal infrared, topographic, geologic, and geothermal data were utilized to provide geothermal potential maps for the Siwa Oasis. Important physiographic variables (including surface elevation, lineament density, drainage density), geological and geophysical parameters (including land surface temperature, depth to basement, bottom hole temperature, magnetic, geothermal gradient, heat flow, thermal conductivity, and main rock units) were incorporated into GIS to produce a geothermal potential map (GTP) for the Siwa Oasis region. The model revealed that both the northeastern and southeastern sections of the study region are of high geothermal potential. The present work showed that combining bottom-hole temperature measurements and remote sensing data with the selected geospatial methodologies is a useful tool for geothermal prospecting in geologically and tectonically comparable settings in Egypt and East Africa. This work has implications for identifying sustainable resources needed to support food production and renewable energy resources.Keywords: BHT, geothermal potential map, geothermal gradient, heat flow, thermal conductivity, satellite imagery, GIS
Procedia PDF Downloads 1035031 Screen Casting Instead of Illegible Scribbles: Making a Mini Movie for Feedback on Students’ Scholarly Papers
Authors: Kerri Alderson
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There is pervasive awareness by post secondary faculty that written feedback on course assignments is inconsistently reviewed by students. In order to support student success and growth, a novel method of providing feedback was sought, and screen casting - short, narrated “movies” of audio visual instructor feedback on students’ scholarly papers - was provided as an alternative to traditional means. An overview of the teaching and learning experience as well as the user-friendly software utilized will be presented. This study covers an overview of this more direct, student-centered medium for providing feedback using technology familiar to post secondary students. Reminiscent of direct personal contact, the personalized video feedback is positively evaluated by students as a formative medium for student growth in scholarly writing.Keywords: education, pedagogy, screen casting, student feedback, teaching and learning
Procedia PDF Downloads 1135030 Challenges of Teaching and Learning English Speech Sounds in Five Selected Secondary Schools in Bauchi, Bauchi State, Nigeria
Authors: Mairo Musa Galadima, Phoebe Mshelia
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In Nigeria, the national policy of education stipulates that the kindergarten-primary schools and the legislature are to use the three popular Nigerian Languages namely: Hausa, Igbo, and Yoruba. However, the English language seems to be preferred and this calls for this paper. Attempts were made to draw out the challenges faced by learners in understanding English speech sounds and using them to communicate effectively in English; using 5 (five) selected secondary school in Bauchi. It was discovered that challenges abound in the wrong use of stress and intonation, transfer of phonetic features from their first language. Others are inadequately qualified teachers and relevant materials including textbooks. It is recommended that teachers of English should lay more emphasis on the teaching of supra-segmental features and should be encouraged to go for further studies, seminars and refresher courses.Keywords: stress and intonation, phonetic and challenges, teaching and learning English, secondary schools
Procedia PDF Downloads 3465029 Career Guidance System Using Machine Learning
Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan
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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills
Procedia PDF Downloads 735028 Photogrammetry and Topographic Information for Urban Growth and Change in Amman
Authors: Mahmoud M. S. Albattah
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Urbanization results in the expansion of administrative boundaries, mainly at the periphery, ultimately leading to changes in landcover. Agricultural land, naturally vegetated land, and other land types are converted into residential areas with a high density of constructs, such as transportation systems and housing. In urban regions of rapid growth and change, urban planners need regular information on up to date ground change. Amman (the capital of Jordan) is growing at unprecedented rates, creating extensive urban landscapes. Planners interact with these changes without having a global view of their impact. The use of aerial photographs and satellite images data combined with topographic information and field survey could provide effective information to develop urban change and growth inventory which could be explored towards producing a very important signature for the built-up area changes.Keywords: highway design, satellite technologies, remote sensing, GIS, image segmentation, classification
Procedia PDF Downloads 4335027 Motivations for Using Social Networking Sites by College Students for Educational Purposes
Authors: Kholoud H. Al-Zedjali, Abir S. Al-Harrasi, Ali H. Al-Badi
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Recently there has been a dramatic proliferation in the number of social networking sites (SNSs) users; however, little is published about what motivates college students to use SNSs in education. The main goal of this research is to explore the college students’ motives for using SNSs in education. A conceptual framework has therefore been developed to identify the main factors that influence/motivate students to use social networking sites for learning purposes. To achieve the research objectives a quantitative method was used to collect data. A questionnaire has been distributed amongst college students. The results reveal that social influence, perceived enjoyment, institute regulation, perceived usefulness, ranking up-lift, attractiveness, communication tools, free of charge, sharing material and course nature all play an important role in the motivation of college students to use SNSs for learning purposes.Keywords: Social Networking Sites (SNSs), education, college students, motivations
Procedia PDF Downloads 2525026 Using Deep Learning for the Detection of Faulty RJ45 Connectors on a Radio Base Station
Authors: Djamel Fawzi Hadj Sadok, Marrone Silvério Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner
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A radio base station (RBS), part of the radio access network, is a particular type of equipment that supports the connection between a wide range of cellular user devices and an operator network access infrastructure. Nowadays, most of the RBS maintenance is carried out manually, resulting in a time consuming and costly task. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. This paper proposes and compares two deep learning solutions to identify attached RJ45 connectors on network ports. We named connector detection, the solution based on object detection, and connector classification, the one based on object classification. With the connector detection, we get an accuracy of 0:934, mean average precision 0:903. Connector classification, get a maximum accuracy of 0:981 and an AUC of 0:989. Although connector detection was outperformed in this study, this should not be viewed as an overall result as connector detection is more flexible for scenarios where there is no precise information about the environment and the possible devices. At the same time, the connector classification requires that information to be well-defined.Keywords: radio base station, maintenance, classification, detection, deep learning, automation
Procedia PDF Downloads 1935025 A Computerized Tool for Predicting Future Reading Abilities in Pre-Readers Children
Authors: Stephanie Ducrot, Marie Vernet, Eve Meiss, Yves Chaix
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Learning to read is a key topic of debate today, both in terms of its implications on school failure and illiteracy and regarding what are the best teaching methods to develop. It is estimated today that four to six percent of school-age children suffer from specific developmental disorders that impair learning. The findings from people with dyslexia and typically developing readers suggest that the problems children experience in learning to read are related to the preliteracy skills that they bring with them from kindergarten. Most tools available to professionals are designed for the evaluation of child language problems. In comparison, there are very few tools for assessing the relations between visual skills and the process of learning to read. Recent literature reports that visual-motor skills and visual-spatial attention in preschoolers are important predictors of reading development — the main goal of this study aimed at improving screening for future reading difficulties in preschool children. We used a prospective, longitudinal approach where oculomotor processes (assessed with the DiagLECT test) were measured in pre-readers, and the impact of these skills on future reading development was explored. The dialect test specifically measures the online time taken to name numbers arranged irregularly in horizontal rows (horizontal time, HT), and the time taken to name numbers arranged in vertical columns (vertical time, VT). A total of 131 preschoolers took part in this study. At Time 0 (kindergarten), the mean VT, HT, errors were recorded. One year later, at Time 1, the reading level of the same children was evaluated. Firstly, this study allowed us to provide normative data for a standardized evaluation of the oculomotor skills in 5- and 6-year-old children. The data also revealed that 25% of our sample of preschoolers showed oculomotor impairments (without any clinical complaints). Finally, the results of this study assessed the validity of the DiagLECT test for predicting reading outcomes; the better a child's oculomotor skills are, the better his/her reading abilities will be.Keywords: vision, attention, oculomotor processes, reading, preschoolers
Procedia PDF Downloads 1375024 Use of Artificial Intelligence in Teaching Practices: A Meta-Analysis
Authors: Azmat Farooq Ahmad Khurram, Sadaf Aslam
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This meta-analysis systematically examines the use of artificial intelligence (AI) in instructional methods across diverse educational settings through a thorough analysis of empirical research encompassing various disciplines, educational levels, and regions. This study aims to assess the effects of AI integration on teaching methodologies, classroom dynamics, teachers' roles, and student engagement. Various research methods were used to gather data, including literature reviews, surveys, interviews, and focus group discussions. Findings indicate paradigm shifts in teaching and education, identify emerging trends, practices, and the application of artificial intelligence in learning, and provide educators, policymakers, and stakeholders with guidelines and recommendations for effectively integrating AI in educational contexts. The study concludes by suggesting future research directions and practical considerations for maximizing AI's positive influence on pedagogical practices.Keywords: artificial intelligence, teaching practices, meta-analysis, teaching-learning
Procedia PDF Downloads 505023 Career Guidance System Using Machine Learning
Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan
Abstract:
Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills
Procedia PDF Downloads 615022 Small-Group Case-Based Teaching: Effects on Student Achievement, Critical Thinking, and Attitude toward Chemistry
Authors: Reynante E. Autida, Maria Ana T. Quimbo
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The chemistry education curriculum provides an excellent avenue where students learn the principles and concepts in chemistry and at the same time, as a central science, better understand related fields. However, the teaching approach used by teachers affects student learning. Cased-based teaching (CBT) is one of the various forms of inductive method. The teacher starts with specifics then proceeds to the general principles. The students’ role in inductive learning shifts from being passive in the traditional approach to being active in learning. In this paper, the effects of Small-Group Case-Based Teaching (SGCBT) on college chemistry students’ achievement, critical thinking, and attitude toward chemistry including the relationships between each of these variables were determined. A quasi-experimental counterbalanced design with pre-post control group was used to determine the effects of SGCBT on Engineering students of four intact classes (two treatment groups and two control groups) in one of the State Universities in Mindanao. The independent variables are the type of teaching approach (SGCBT versus pure lecture-discussion teaching or PLDT) while the dependent variables are chemistry achievement (exam scores) and scores in critical thinking and chemistry attitude. Both Analysis of Covariance (ANCOVA) and t-tests (within and between groups and gain scores) were used to compare the effects of SGCBT versus PLDT on students’ chemistry achievement, critical thinking, and attitude toward chemistry, while Pearson product-moment correlation coefficients were calculated to determine the relationships between each of the variables. Results show that the use of SGCBT fosters positive attitude toward chemistry and provides some indications as well on improved chemistry achievement of students compared with PLDT. Meanwhile, the effects of PLDT and SGCBT on critical thinking are comparable. Furthermore, correlational analysis and focus group interviews indicate that the use of SGCBT not only supports development of positive attitude towards chemistry but also improves chemistry achievement of students. Implications are provided in view of the recent findings on SGCBT and topics for further research are presented as well.Keywords: case-based teaching, small-group learning, chemistry cases, chemistry achievement, critical thinking, chemistry attitude
Procedia PDF Downloads 2865021 Micro-Rest: Extremely Short Breaks in Post-Learning Interference Support Memory Retention over the Long Term
Authors: R. Marhenke, M. Martini
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The distraction of attentional resources after learning hinders long-term memory consolidation compared to several minutes of post-encoding inactivity in form of wakeful resting. We tested whether an 8-minute period of wakeful resting, compared to performing an adapted version of the d2 test of attention after learning, supports memory retention. Participants encoded and immediately recalled a word list followed by either an 8 minute period of wakeful resting (eyes closed, relaxed) or by performing an adapted version of the d2 test of attention (scanning and selecting specific characters while ignoring others). At the end of the experimental session (after 12-24 min) and again after 7 days, participants were required to complete a surprise free recall test of both word lists. Our results showed no significant difference in memory retention between the experimental conditions. However, we found that participants who completed the first lines of the d2 test in less than the given time limit of 20 seconds and thus had short unfilled intervals before switching to the next test line, remembered more words over the 12-24 minute and over the 7 days retention interval than participants who did not complete the first lines. This interaction occurred only for the first test lines, with the highest temporal proximity to the encoding task and not for later test lines. Differences in retention scores between groups (completed first line vs. did not complete) seem to be widely independent of the general performance in the d2 test. Implications and limitations of these exploratory findings are discussed.Keywords: long-term memory, retroactive interference, attention, forgetting
Procedia PDF Downloads 1215020 The Impact of Hosting an On-Site Vocal Concert in Preschool on Music Inspiration and Learning Among Preschoolers
Authors: Meiying Liao, Poya Huang
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The aesthetic domain is one of the six major domains in the Taiwanese preschool curriculum, encompassing visual arts, music, and dramatic play. Its primary objective is to cultivate children’s abilities in exploration and awareness, expression and creation, and response and appreciation. The purpose of this study was to explore the effects of hosting a vocal music concert on aesthetic inspiration and learning among preschoolers in a preschool setting. The primary research method employed was a case study focusing on a private preschool in Northern Taiwan that organized a school-wide event featuring two vocalists. The concert repertoires included children’s songs, folk songs, and arias performed in Mandarin, Hakka, English, German, and Italian. In addition to professional performances, preschool teachers actively participated by presenting a children’s song. A total of 5 classes, comprising approximately 150 preschoolers, along with 16 teachers and staff, participated in the event. Data collection methods included observation, interviews, and documents. Results indicated that both teachers and children thoroughly enjoyed the concert, with high levels of acceptance when the program was appropriately designed and hosted. Teachers reported that post-concert discussions with children revealed the latter’s ability to recall people, events, and elements observed during the performance, expressing their impressions of the most memorable segments. The concert effectively achieved the goals of the aesthetic domain, particularly in fostering response and appreciation. It also inspired preschoolers’ interest in music. Many teachers noted an increased desire for performance among preschoolers after exposure to the concert, with children imitating the performers and their expressions. Remarkably, one class extended this experience by incorporating it into the curriculum, autonomously organizing a high-quality concert in the music learning center. Parents also reported that preschoolers enthusiastically shared their concert experiences at home. In conclusion, despite being a single event, the positive responses from preschoolers towards the music performance suggest a meaningful impact. These experiences extended into the curriculum, as firsthand exposure to performances allowed teachers to deepen related topics, fostering a habit of autonomous learning in the designated learning centers.Keywords: concert, early childhood music education, aesthetic education, music develpment
Procedia PDF Downloads 445019 The Design and Development of Online Infertility Prevention Education in the Frame of Mayer's Multimedia Learning Theory
Authors: B. Baran, S. N. Kaptanoglu, M. Ocal, Y. Kagnici, E. Esen, E. Siyez, D. M. Siyez
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Infertility is the fact that couples cannot have children despite 1 year of unprotected sexual life. Infertility can be considered as an important problem affecting not only sexual life but also social and psychological conditions of couples. Learning about information about preventable factors related to infertility during university years plays an important role in preventing a possible infertility case in older ages. The possibility to facilitate access to information with the internet has provided the opportunity to reach a broad audience in the diverse learning environments and educational environment. Moreover, the internet has become a basic resource for the 21st-century learners. Providing information about infertility over the internet will enable more people to reach in a short time. When studies conducted abroad about infertility are examined, interactive websites and online education programs come to the fore. In Turkey, while there is no comprehensive online education program for university students, it seems that existing studies are aimed to make more advertisements for doctors or hospitals. In this study, it was aimed to design and develop online infertility prevention education for university students. Mayer’s Multimedia Learning Theory made up the framework for the online learning environment in this study. The results of the needs analysis collected from the university students in Turkey who were selected with sampling to represent the audience for online learning contributed to the design phase. In this study, an infertility prevention online education environment designed as a 4-week education was developed by explaining the theoretical basis and needs analysis results. As a result; in the development of the online environment, different kind of visual aids that will increase teaching were used in the environment of online education according to Mayer’s principles of extraneous processing (coherence, signaling, spatial contiguity, temporal contiguity, redundancy, expectation principles), essential processing (segmenting, pre-training, modality principles) and generative processing (multimedia, personalization, voice principles). For example, the important points in reproductive systems’ expression were emphasized by visuals in order to draw learners’ attention, and the presentation of the information was also supported by the human voice. In addition, because of the limited knowledge of university students in the subject, the issue of female reproductive and male reproductive systems was taught before preventable factors related to infertility. Furthermore, 3D video and augmented reality application were developed in order to embody female and male reproductive systems. In conclusion, this study aims to develop an interactive Online Infertility Prevention Education in which university students can easily access reliable information and evaluate their own level of knowledge about the subject. It is believed that the study will also guide the researchers who want to develop online education in this area as it contains design-stage decisions of interactive online infertility prevention education for university students.Keywords: infertility, multimedia learning theory, online education, reproductive health
Procedia PDF Downloads 1585018 Implementation of a Program of Orientation for Travel Nursing Staff Based on Nurse-Identified Learning Needs
Authors: Olga C. Rodrigue
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Long-term care and skilled nursing facilities experience ebbs and flows of nursing staffing, a problem compounded by the perception of the facilities as undesirable workplaces and competition for staff from other healthcare entities. Travel nurses are contracted to fill staffing needs due to increased admissions, increased and unexpected attrition of nurses, or facility expansion of services. Prior to beginning the contracted assignment, the travel nurse must meet industry, company, and regulatory requirements (The Joint Commission and CMS) for skills and knowledge. Travel nurses, however, inconsistently receive the pre-assignment orientation needed to work at the contracted facility, if any information is given at all. When performance expectations are not met, travel nurses may subsequently choose to leave the position without completing the terms of the contract, and some facilities may choose to terminate the contract prior to the expected end date. The overarching goal of the Doctor of Nursing Practice evidence-based practice improvement project is to provide travel nurses with the basic and necessary information to prepare them to begin a long-term and skilled nursing assignment. The project involves the identification of travel nurse learning needs through a survey and the development and provision of web-based learning modules to address those needs prior to arrival for a long-term and skilled nursing assignment.Keywords: nurse staffing, travel nurse, travel staff, contract staff, contracted assignment, long-term care, skilled nursing, onboarding, orientation, staff development, supplemental staff
Procedia PDF Downloads 1565017 Integration, a Tool to Develop Critical Thinking Skills of Undergraduate Veterinary Students
Authors: M. L. W. P. De Silva, R. A. C. Rabel, N. Smith, L. McIntyre, T. J Parkinson, K. A. N. Wijayawardhane
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Curricular integration is an important concept in medical education for developing students’ ability to create connections between different medical disciplines. Problem-Based Learning (PBL) is one of the vehicles through which such integration can be achieved. During the recent review of the veterinary curriculum at the University of Peradeniya, a series of courses in Integrative Veterinary Science (IVS) were introduced, in which PBL was the primary teaching methodology. The objectives of this study were to evaluate students’ opinions on PBL as a teaching method: it should be noted that, within the context of secondary and tertiary education in Sri Lanka, this would be an entirely novel learning experience for the students. Opinions were sought at the conclusion of IVS sessions where students of semesters 2, 4, 6, and 7 (of an 8-semester program) were exposed to a two, 2-hour PBL-based case scenario. The PBL-based case scenario in semesters 2, 4, and 7 were delivered using material previously developed by an experienced PBL practitioner, whilst material for semester 6 was prepared de novo by a less experienced practitioner. Each student (semesters 2: n=38, 4: n=37, 6: n=55, and 7: n=40) completed a questionnaire which asked whether: (i) the course had improved their critical thinking skills; (ii) the learning environment was sufficiently comfortable to express/share student’s opinion; (iii) there was sufficient facilitator guidance; (iv) the online study environment enhanced learning; and (v) the students were overall satisfied with the PBL approach and IVS concept. Responses were given on a 5-point Likert-scale (strongly agree (SA), agree (A), neutral (N), disagree (D), and strongly disagree (SD)). SA and A responses were summed to provide an overall ‘satisfactory’ response. Results were subjected to frequency-distribution statistical analysis. A total of 88.5% of students gave SA+A scores to their overall satisfaction. The proportion of SA+A scores differed between different semesters, such that 95% of semester 2, 4, and 7 students gave SA+A scores, whereas only 69% of semester 6 students did so for their respective sessions. Overall, 96% of the students gave SA+A scores to the question relating to the improvement of critical thinking skills: semester 6 students’ scores were marginally, but not significantly, lower (91% SA+A) than those in other semesters. The difference of scores between semester 6 and the other semesters may be attributed to the different PBL-material used and/or the different experience levels of the practitioners that developed the study material. The use of PBL as a means of teaching IVS curriculum-integration courses was well-received by the students in terms of their overall satisfaction and their perceptions of improved critical thinking skills. Importantly, this was achieved in the face of a methodology that was entirely novel to the students. Finally, the delivery of the PBL medium was readily mastered by the practitioner to whom it was also a novel methodology.Keywords: critical thinking skills, integration, problem based learning, veterinary education
Procedia PDF Downloads 1285016 The Good Form of a Sustainable Creative Learning City Based on “The Theory of a Good City Form“ by Kevin Lynch
Authors: Fatemeh Moosavi, Tumelo Franck Nkoshwane
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Peter Drucker the renowned management guru once said, “The best way to predict the future is to create it.” Mr. Drucker is also the man who placed human capital as the most vital resource of any institution. As such any institution bent on creating a better future, requires a competent human capital, one that is able to execute with efficiency and effectiveness the objective a society aspires to. Technology today is accelerating the rate at which many societies transition to knowledge based societies. In this accelerated paradigm, it is imperative that those in leadership establish a platform capable of sustaining the planned future; intellectual capital. The capitalist economy going into the future will not just be sustained by dollars and cents, but by individuals who possess the creativity to enterprise, innovate and create wealth from ideas. This calls for cities of the future, to have this premise at the heart of their future plan, if the objective of designing sustainable and liveable future cities will be realised. The knowledge economy, now transitioning to the creative economy, requires cities of the future to be ‘gardens’ of inspiration, to be places where knowledge, creativity, and innovation can thrive as these instruments are becoming critical assets for creating wealth in the new economic system. Developing nations must accept that learning is a lifelong process that requires keeping abreast with change and should invest in teaching people how to keep learning. The need to continuously update one’s knowledge, turn these cities into vibrant societies, where new ideas create knowledge and in turn enriches the quality of life of the residents. Cities of the future must have as one of their objectives, the ability to motivate their citizens to learn, share knowledge, evaluate the knowledge and use it to create wealth for a just society. The five functional factors suggested by Kevin Lynch;-vitality, meaning/sense, adaptability, access, control, and monitoring should form the basis on which policy makers and urban designers base their plans for future cities. The authors of this paper believe that developing nations “creative economy clusters”, cities where creative industries drive the need for constant new knowledge creating sustainable learning creative cities. Obviously the form, shape and size of these districts should be cognisant of the environmental, cultural and economic characteristics of each locale. Gaborone city in the republic of Botswana is presented as the case study for this paper.Keywords: learning city, sustainable creative city, creative industry, good city form
Procedia PDF Downloads 2995015 A Survey of Response Generation of Dialogue Systems
Authors: Yifan Fan, Xudong Luo, Pingping Lin
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An essential task in the field of artificial intelligence is to allow computers to interact with people through natural language. Therefore, researches such as virtual assistants and dialogue systems have received widespread attention from industry and academia. The response generation plays a crucial role in dialogue systems, so to push forward the research on this topic, this paper surveys various methods for response generation. We sort out these methods into three categories. First one includes finite state machine methods, framework methods, and instance methods. The second contains full-text indexing methods, ontology methods, vast knowledge base method, and some other methods. The third covers retrieval methods and generative methods. We also discuss some hybrid methods based knowledge and deep learning. We compare their disadvantages and advantages and point out in which ways these studies can be improved further. Our discussion covers some studies published in leading conferences such as IJCAI and AAAI in recent years.Keywords: deep learning, generative, knowledge, response generation, retrieval
Procedia PDF Downloads 1265014 The Impact of Using Technology Tools on Preparing English Language Learners for the 21st Century
Authors: Ozlem Kaya
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21st-century learners are energetic and tech-savvy, and the skills and the knowledge required in this century are complex and challenging. Therefore, teachers need to find new ways to appeal to the needs and interests of their students and meet the demands of the 21st century at the same time. One way to do so in English language learning has been to incorporate various technology tools into classroom practices. Although teachers think these practices are effective and their students enjoy them, students may have different perceptions. To find out what students think about the use of technology tools in terms of developing 21st-century skills and knowledge, this study was conducted at Anadolu University School of Foreign Languages. A questionnaire was administered to 40 students at elementary level. Afterward, semi-structured interviews were held with 8 students to provide deeper insight into their perceptions. The details of the findings of the study will be presented and discussed during the presentation.Keywords: 21st century skills, technology tools, perception, English Language Learning
Procedia PDF Downloads 2885013 Willingness and Attitude towards Organ Donation of Nurses in Taiwan
Authors: ShuYing Chung, Minchuan Huang, Iping Chen
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Taking the medical staff in an emergency ward of a medical center in Central Taiwan as the research object, the questionnaire data were collected by anonymous and voluntary reporting methods with structured questionnaire to explore the actual situation, willingness and attitude of organ donation. Only 80 valid questionnaires were collected. Among the 8 questions, the average correct rate was 5.9 + 1.2, and the correct rate was 73.13%. The willingness of organ donation that 7.5% of the people are not willing; 92.5% of the people are willing, of which 62.5% have considered but have not yet decided; 21.3% are willing but have not signed the consent of organ donation; They have signed the consent of organ donation 8.7%. The average total score (standard deviation) of attitude towards organ donation was 36.2. There is no significant difference between the demographic variables and the awareness and willingness of organ donation, but there is a significant correlation between the marital status and the attitude of organ donation.Keywords: clinical psychology, organ donation, doctors affecting psychological disorders, commitment
Procedia PDF Downloads 1265012 Supporting International Student’s Acculturation Through Chatbot Technology: A Proposed Study
Authors: Sylvie Studente
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Despite the increase in international students migrating to the UK, the transition from home environment to a host institution abroad can be overwhelming for many students due to acculturative stressors. These stressors are reported to peak within the first six months of transitioning into study abroad which has determinantal impacts for Higher Education Institutions. These impacts include; increased drop-out rates and overall decreases in academic performance. Research suggests that belongingness can negate acculturative stressors through providing opportunities for students to form necessary social connections. In response to this universities have focussed on utilising technology to create learning communities with the most commonly deployed being social media, blogs, and discussion forums. Despite these attempts, the application of technology in supporting international students is still ambiguous. With the reported growing popularity of mobile devices among students and accelerations in learning technology owing to the COVID-19 pandemic, the potential is recognised to address this challenge via the use of chatbot technology. Whilst traditionally, chatbots were deployed as conversational agents in business domains, they have since been applied to the field of education. Within this emerging area of research, a gap exists in addressing the educational value of chatbots over and above the traditional service orientation categorisation. The proposed study seeks to extend upon current understandings by investigating the challenges faced by international students in studying abroad and exploring the potential of chatbots as a solution to assist students’ acculturation. There has been growing interest in the application of chatbot technology to education accelerated by the shift to online learning during the COVID-19 pandemic. Although interest in educational chatbots has surged, there is a lack of consistency in the research area in terms of guidance on the design to support international students in HE. This gap is widened when considering the additional challenge of supporting multicultural international students with diverse. Diversification in education is rising due to increases in migration trends for international study. As global opportunities for education increase, so does the need for multiculturally inclusive learning support.Keywords: chatbots, education, international students, acculturation
Procedia PDF Downloads 245011 The Impact of Professional Development in the Area of Technology Enhanced Learning on Higher Education Teaching Practices Across Atlantic Technological University - Research Methodology and Preliminary Findings
Authors: Annette Cosgrove, Carina Ginty, Tony Hall, Cornelia Connolly
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The objectives of this research study is to examine the impact of professional development in Technology Enhanced Learning (TEL) and the digitization of learning in teaching communities across multiple higher education sites in the ATU (Atlantic Technological University *) ( 2020-2025), including the proposal of an evidence-based digital teaching model for use in a future pandemic. The research strategy undertaken for this study is a multi-site study using mixed methods. Qualitative & quantitative methods are being used in the study to collect data. A pilot study was carried out initially, feedback was collected and the research instrument was edited to reflect this feedback before being administered. The purpose of the staff questionnaire is to evaluate the impact of professional development in the area of TEL, and to capture the practitioner's views on the perceived impact on their teaching practice in the higher education sector across ATU (West of Ireland – 5 Higher education locations ). The phenomenon being explored is ‘ the impact of professional development in the area of technology-enhanced learning and on teaching practice in a higher education institution. The research methodology chosen for this study is an Action based Research Study. The researcher has chosen this approach as it is a prime strategy for developing educational theory and enhancing educational practice. This study includes quantitative and qualitative methods to elicit data that will quantify the impact that continuous professional development in the area of digital teaching practice and technologies has on the practitioner’s teaching practice in higher education. The research instruments/data collection tools for this study include a lecturer survey with a targeted TEL Practice group ( Pre and post covid experience) and semi-structured interviews with lecturers. This research is currently being conducted across the ATU multi-site campus and targeting Higher education lecturers that have completed formal CPD in the area of digital teaching. ATU, a West of Ireland university, is the focus of the study. The research questionnaire has been deployed, with 75 respondents to date across the ATU - the primary questionnaire and semi-formal interviews are ongoing currently – the purpose being to evaluate the impact of formal professional development in the area of TEL and its perceived impact on the practitioners teaching practice in the area of digital teaching and learning. This paper will present initial findings, reflections and data from this ongoing research study.Keywords: TEL, technology, digital, education
Procedia PDF Downloads 695010 Alcohol Detection with Engine Locking System Using Arduino and ESP8266
Authors: Sukhpreet Singh, Kishan Bhojrath, Vijay, Avinash Kumar, Mandlesh Mishra
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The project uses an Arduino and ESP8266 to construct an alcohol detection system with an engine locking mechanism, offering a distinct way to fight drunk driving. An alcohol sensor module is used by the system to determine the amount of alcohol present in the ambient air. When the system detects alcohol levels beyond a certain threshold that is deemed hazardous for driving, it activates a relay module that is linked to the engine of the car, so rendering it inoperable. By preventing people from operating a vehicle while intoxicated, this preventive measure seeks to improve road safety. Adding an ESP8266 module also allows for remote monitoring and notifications, giving users access to real-time status updates on their system. By using an integrated strategy, the initiative provides a workable and efficient way to lessen the dangers related to driving while intoxicated.Keywords: MQ3 sensor, ESP 8266, arduino, IoT
Procedia PDF Downloads 555009 Machine Learning Approach for Automating Electronic Component Error Classification and Detection
Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski
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The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.Keywords: augmented reality, machine learning, object recognition, virtual laboratories
Procedia PDF Downloads 1245008 Deep Neural Network Approach for Navigation of Autonomous Vehicles
Authors: Mayank Raj, V. G. Narendra
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Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric.Keywords: autonomous vehicles, deep learning, computer vision, artificial intelligence
Procedia PDF Downloads 1495007 Prediction-Based Midterm Operation Planning for Energy Management of Exhibition Hall
Authors: Doseong Eom, Jeongmin Kim, Kwang Ryel Ryu
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Large exhibition halls require a lot of energy to maintain comfortable atmosphere for the visitors viewing inside. One way of reducing the energy cost is to have thermal energy storage systems installed so that the thermal energy can be stored in the middle of night when the energy price is low and then used later when the price is high. To minimize the overall energy cost, however, we should be able to decide how much energy to save during which time period exactly. If we can foresee future energy load and the corresponding cost, we will be able to make such decisions reasonably. In this paper, we use machine learning technique to obtain models for predicting weather conditions and the number of visitors on hourly basis for the next day. Based on the energy load thus predicted, we build a cost-optimal daily operation plan for the thermal energy storage systems and cooling and heating facilities through simulation-based optimization.Keywords: building energy management, machine learning, operation planning, simulation-based optimization
Procedia PDF Downloads 3155006 A Supervised Goal Directed Algorithm in Economical Choice Behaviour: An Actor-Critic Approach
Authors: Keyvanl Yahya
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This paper aims to find a algorithmic structure that affords to predict and explain economic choice behaviour particularly under uncertainty (random policies) by manipulating the prevalent Actor-Critic learning method that complies with the requirements we have been entrusted ever since the field of neuroeconomics dawned on us. Whilst skimming some basics of neuroeconomics that might be relevant to our discussion, we will try to outline some of the important works which have so far been done to simulate choice making processes. Concerning neurological findings that suggest the existence of two specific functions that are executed through Basal Ganglia all the way down to sub-cortical areas, namely 'rewards' and 'beliefs', we will offer a modified version of actor/critic algorithm to shed a light on the relation between these functions and most importantly resolve what is referred to as a challenge for actor-critic algorithms, that is lack of inheritance or hierarchy which avoids the system being evolved in continuous time tasks whence the convergence might not emerge.Keywords: neuroeconomics, choice behaviour, decision making, reinforcement learning, actor-critic algorithm
Procedia PDF Downloads 391