Search results for: online teaching and learning

Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The educational system faces a significant concern with regards to Dyslexia and Dysgraphia, which are learning disabilities impacting reading and writing abilities. This is particularly challenging for children who speak the Sinhala language due to its complexity and uniqueness. Commonly used methods to detect the risk of Dyslexia and Dysgraphia rely on subjective assessments, leading to limited coverage and time-consuming processes. Consequently, delays in diagnoses and missed opportunities for early intervention can occur. To address this issue, the project developed a hybrid model that incorporates various deep learning techniques to detect the risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16, and YOLOv8 models were integrated to identify handwriting issues. The outputs of these models were then combined with other input data and fed into an MLP model. Hyperparameters of the MLP model were fine-tuned using Grid Search CV, enabling the identification of optimal values for the model. This approach proved to be highly effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention. The Resnet50 model exhibited a training accuracy of 0.9804 and a validation accuracy of 0.9653. The VGG16 model achieved a training accuracy of 0.9991 and a validation accuracy of 0.9891. The MLP model demonstrated impressive results with a training accuracy of 0.99918, a testing accuracy of 0.99223, and a loss of 0.01371. These outcomes showcase the high accuracy achieved by the proposed hybrid model in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, dyslexia, dysgraphia, deep learning, learning disabilities, data science

Procedia PDF Downloads 58

Authors: Dalila Lino

Abstract:

Practicum is a central dimension of teacher education programs. Learning how to teach is, in effect, a complex process that integrates periods of observation, experimentation, reflection, planning, and evaluation in a real context of practices, providing opportunities for prospective teachers to understand the various dimensions of education and to implement the knowledge built over the theoretical courses they have taken. At the pre-service training of early childhood teachers, specialized guidance and in particular pedagogical supervision assumes a key role in the professional development of students in training. The main goal of this study is to describe and analyze the supervision process that occurs during the practicum of preschool education master programs in Portugal. The objectives of the study are: (i) to describe the cooperative process of professional development experienced by student teachers during the practicum; (ii) to identify the strengths and weaknesses of supervision process; (iii) to identify the supervision styles used by university supervisors and cooperating teachers. The methodology used is the mix-method research and data was collected through semi-structured interviews and online questionnaires. The participants are newly graduated Portuguese early childhood teachers, university supervisors and cooperating teachers. The results reveal gaps in the specialized training of cooperating teachers and university supervisors, a large number of trainees per supervisor, which makes it difficult to support students, and those interpersonal relationships between university supervisors and students and/or cooperating teachers and students interfere in the development of the supervisory processes. The study highlights the need to invest in the specialized training of university supervisors and cooperating teachers to create better opportunities to support the professional development of prospective teachers.

Keywords: mentoring, pedagogical supervision, practicum, preschool teacher education

Procedia PDF Downloads 142

Authors: Regina Rahimi, Delores Liston

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The advent of COVID-19 has heightened awareness of the need for social emotional learning (SEL) throughout all educational contexts. Given this, schools (most often p12 settings) have begun to embrace practices for addressing social-emotional learning. While there is a growing body of research and literature on common practices of SEL, there is no ‘standard’ for its implementation. Our work proposed here recognizes there is no universal approach for addressing SEL and rather, seeks to explore how SEL can be approached in and through diverse contexts. We assert that left unrecognized and unaddressed by teachers, issues with social and emotional well-being profoundly negatively affect students’ academic performance and exacerbate teacher stress. They contribute to negative student-teacher relationships, poor classroom management outcomes, and compromised academic outcomes. Therefore, teachers and administrators have increasingly turned to developing pedagogical and classroom practices that support the social and emotional dimensions of students. Substantive quantitative evidence indicates professional development training to improve awareness and foster positive teacher-student relationships can provide a protective function for psycho-social outcomes and a promotive factor for improved learning outcomes for students. Our work aims to add to the growing body of literature on improving student well-being by providing a unique examination of SEL through a lens of diverse contexts. Methodology: This presentation hopes to present findings from an edited volume that will seek to highlight works that examine SEL practices in a variety of academic settings. The studies contained within the work represent varied forms of qualitative research. Conclusion: This work provides examples of SEL in higher education/postsecondary settings, a variety of P12 academic settings (public; private; rural, urban; charter, etc.), and international contexts. This work demonstrates the variety of ways educational institutions and educators have used SEL to address the needs of students, providing examples for others to adapt to their own diverse contexts. This presentation will bring together exemplar models of SEL in diverse practice settings.

Keywords: social emotional learning, teachers, classrooms, diversity

Procedia PDF Downloads 55

Authors: Cao Jiayu, Lan Ximing, Huang Jingjia, Burra Venkata Durga Kumar

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The first virus to attack personal computers was born in early 1986, called C-Brain, written by a pair of Pakistani brothers. In those days, people still used dos systems, manipulating computers with the most basic command lines. In the 21st century today, computer performance has grown geometrically. But computer viruses are also evolving and escalating. We never stop fighting against security problems. Stack overflow is one of the most common security vulnerabilities in operating systems. It may result in serious security issues for an operating system if a program in it has a vulnerability with administrator privileges. Certain viruses change the value of specific memory through a stack overflow, allowing computers to run harmful programs. This study developed a mechanism to detect and respond to time whenever a stack overflow occurs. We demonstrate the effectiveness of standard machine learning algorithms and control flow enforcement techniques in predicting computer OS security using generating suspicious vulnerability functions (SVFS) and associated suspect areas (SAS). The method can minimize the possibility of stack overflow attacks occurring.

Keywords: operating system, security, stack overflow, buffer overflow, machine learning, control-flow enforcement technology

Procedia PDF Downloads 111

Authors: Osman Okumuş

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The pandemic has significantly affected every aspect of life. Especially in recenttimes, as a result of this effect, we have come closer to technology. Distance education has taken the place of formal education rather than supporting formal education. Thiscreatednewexperiencesforbothteachersandstudents. This research focused on revealing the experiences of the same students in distance and formal education, especially in history lessons. In the study, which was designed as a case study, 20 students were interviewed through a semi-structured interview form prepared by the researcher. The results show that both learning environments provide students with important experiences. However, despite the fact that the students developed their digital competencies and experienced different learning environments, they focused on formal education in the name of socialization.

Keywords: history lessons, distance education, pandemic., formal education

Procedia PDF Downloads 96

Authors: Rahul Paul, Kedar Nath Das

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The paradigm of Reinforcement Learning (RL) has become prominent in training intelligent agents to make decisions in environments that are both dynamic and uncertain. The primary objective of RL is to optimize the policy of an agent in order to maximize the cumulative reward it receives throughout a given period. Nevertheless, the process of optimization presents notable difficulties as a result of the inherent trade-off between exploration and exploitation, the presence of extensive state-action spaces, and the intricate nature of the dynamics involved. Evolutionary Optimization Methods (EOMs) have garnered considerable attention as a supplementary approach to tackle these challenges, providing distinct capabilities for optimizing RL policies and value functions. The ongoing advancement of research in both RL and EOMs presents an opportunity for significant advancements in autonomous decision-making systems. The convergence of these two fields has the potential to have a transformative impact on various domains of artificial intelligence (AI) applications. This article highlights the considerable influence of EOMs in enhancing the capabilities of RL. Taking advantage of evolutionary principles enables RL algorithms to effectively traverse extensive action spaces and discover optimal solutions within intricate environments. Moreover, this paper emphasizes the practical implementations of EOMs in the field of RL, specifically in areas such as robotic control, autonomous systems, inventory problems, and multi-agent scenarios. The article highlights the utilization of EOMs in facilitating RL agents to effectively adapt, evolve, and uncover proficient strategies for complex tasks that may pose challenges for conventional RL approaches.

Keywords: machine learning, reinforcement learning, loss function, optimization techniques, evolutionary optimization methods

Procedia PDF Downloads 76

Authors: Bellali Brahim, Bellali Fatima

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The integration of information technology systems (ITS) into a company's ‘human resources processes seems to be the appropriate solution to the problem of evolving and adapting its human resources management practices in order to be both more strategic and more efficient in terms of costs and service quality. In this context, the aim of this work is to study the impact of nformation technology systems (ITS) on the recruitment process. In this study, we targeted candidates who had recruited using IT tools. The target population consists of 34 candidates based in Casablanca, Morocco. In order to collect the data, a questionnaire had to be drawn up. The survey is based on a data sheet and a questionnaire that is divided into several sections to make it more structured and comprehensible. The results show that the majority of respondents say that companies are making greater use of online CV libraries and social networks as digital solutions during the recruitment process. The results also show that 50% of candidates say that the use of digital tools by companies would not slow them down when applying for a job and that these IT tools improve manual recruitment processes, while 44.1% think that they facilitate recruitment without any human intervention. The majority of respondents (52.9%) think that social networks are the digital solutions most often used by recruiters in the sourcing phase. The constraints of digital recruitment encountered are the dehumanization of human resources (44.1%) and the limited interaction during remote interviews (44.1%), which leaves no room for informal exchanges. Digital recruitment can be a highly effective strategy for finding qualified candidates in a variety of fields. Here are a few recommendations for optimizing your digital recruitment process: (1) Use online recruitment platforms: LinkedIn, Twitter, and Facebook ; (2) Use applicant tracking systems (ATS) ; (3) Develop a content marketing strategy.

Keywords: IT systems, recruitment, challenges, constraints

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Authors: R. Ajith, Kamal Bijlani

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Over the past few years, tremendous evolutions have happened in the educational discipline. Serious game, which is regarded as one of the most important inventions is being widely for learning purposes. Serious games can be used to negate the various drawbacks that the current evaluation and assessment methods have, like examination anxiety and the lack of proper feedback given to the learners. This paper proposes serious game as a tool for conducting evaluations and assessments. The examination anxiety faced by learners can be reduced, as they are provided with a game as an examination. The serious game also tracks learner’s actions, records them and provide feedback based on the predefined set of actions according to the course objectives. The appropriate feedback given to the learner will help in developmental activities in the learning process.

Keywords: serious games, evaluation, performance assessment, examination anxiety, performance feedback

Procedia PDF Downloads 593

Authors: M. Talebzadegan, S. Bina , I. Riazi

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The purpose of this paper is to present an active solar energy system to supply heating demands of the teaching staff dormitory of Islamic Azad University of Ramhormoz. The design takes into account the solar radiations and climate data of Ramhormoz town and is based on the daily warm water consumption for health demands of 450 residents of the dormitory, which is equal to 27000 lit of 50 C° water, and building heating requirements with an area of 3500 m² well-protected by heatproof materials. First, heating demands of the building were calculated, then a hybrid system made up of solar and fossil energies was developed and finally, the design was economically evaluated. Since there is only roof space for using 110 flat solar water heaters, the calculations were made to hybridize solar water heating system with heat pumping system in which solar energy contributes 67% of the heat generated. According to calculations, the Net Present Value “N.P.V.” of revenue stream exceeds “N.P.V.” of cash paid off in this project over three years, which makes economically quite promising. The return of investment and payback period of the project is 4 years. Also, the Internal Rate of Return (IRR) of the project was 25%, which exceeds bank rate of interest in Iran and emphasizes the desirability of the project.

Keywords: solar energy, heat demand, renewable, pollution

Procedia PDF Downloads 413

Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

Procedia PDF Downloads 91

Authors: Tahar Alimi, Rahma Boujebane, Wiem Derouich, Lamia Hadrich Belguith

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Machine translation task of low-resourced languages such as Arabic is a challenging task. Despite the appearance of sophisticated models based on the latest deep learning techniques, namely the transfer learning and transformers, all models prove incapable of carrying out an acceptable translation, which includes Arabic Dialects (AD), because they do not have official status. In this paper, we present a machine translation model designed to translate Arabic multidialectal content into Modern Standard Arabic (MSA), leveraging both new and existing parallel resources. The latter achieved the best results for both Levantine and Maghrebi dialects with a BLEU score of 64.99.

Keywords: Arabic translation, dialect translation, fine-tune, MSA translation, transformer, translation

Procedia PDF Downloads 47

Authors: Evrim Buyukaslan, Simona Jevsnik, Fatma Kalaoglu

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Simulation of fabrics is more difficult than any other simulation due to complex mechanics of fabrics. Most of the virtual garment simulation software use mass-spring model and incorporate fabric mechanics into simulation models. The accuracy and fidelity of these virtual garment simulation software is a question mark. Drape is a subjective phenomenon and evaluation of drape has been studied since 1950’s. On the other hand, fabric and garment simulation is relatively new. Understanding drape perception of subjects when looking at fabric simulations is critical as virtual try-on becomes more of an issue by enhanced online apparel sales. Projected future of online apparel retailing is that users may view their avatars and try-on the garment on their avatars in the virtual environment. It is a well-known fact that users will not be eager to accept this innovative technology unless it is realistic enough. Therefore, it is essential to understand what users see when they are displaying fabrics in a virtual environment. Are they able to distinguish the differences between various fabrics in virtual environment? The purpose of this study is to investigate human perception when looking at a virtual fabric and determine the most visually noticeable drape parameter. To this end, five different fabrics are mechanically tested, and their drape simulations are generated by commercial garment simulation software (Optitex®). The simulation images are processed by an image analysis software to calculate drape parameters namely; drape coefficient, node severity, and peak angles. A questionnaire is developed to evaluate drape properties subjectively in a virtual environment. Drape simulation images are shown to 27 subjects and asked to rank the samples according to their questioned drape property. The answers are compared to the calculated drape parameters. The results show that subjects are quite sensitive to drape coefficient changes while they are not very sensitive to changes in node dimensions and node distributions.

Keywords: drape simulation, drape evaluation, fabric mechanics, virtual fabric

Procedia PDF Downloads 334

Authors: Grienggrai Rajchakit

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As we all know that coronavirus is announced as a pandemic in the world by WHO. It is speeded all over the world with few days of time. To control this spreading, every citizen maintains social distance and self-preventive measures are the best strategies. As of now, many researchers and scientists are continuing their research in finding out the exact vaccine. The machine learning model finds that the coronavirus disease behaves in an exponential manner. To abolish the consequence of this pandemic, an efficient step should be taken to analyze this disease. In this paper, a recurrent neural network model is chosen to predict the number of active cases in a particular state. To make this prediction of active cases, we need a database. The database of COVID-19 is downloaded from the KAGGLE website and is analyzed by applying a recurrent LSTM neural network with univariant features to predict the number of active cases of patients suffering from the corona virus. The downloaded database is divided into training and testing the chosen neural network model. The model is trained with the training data set and tested with a testing dataset to predict the number of active cases in a particular state; here, we have concentrated on Andhra Pradesh state.

Keywords: COVID-19, coronavirus, KAGGLE, LSTM neural network, machine learning

Procedia PDF Downloads 153

Authors: Sami M. Alshareef

Abstract:

The rapid growth of smart grid technology has brought significant advancements to the power industry. However, with the increasing interconnectivity and reliance on information and communication technologies, smart grids have become vulnerable to cyber-attacks, posing significant threats to the reliable operation of power systems. Among the critical components of smart grids, the Automatic Generation Control (AGC) system plays a vital role in maintaining the balance between generation and load demand. Therefore, protecting the AGC system from cyber threats is of paramount importance to maintain grid stability and prevent disruptions. Traditional security measures often fall short in addressing sophisticated and evolving cyber threats, necessitating the exploration of innovative approaches. Machine learning, with its ability to analyze vast amounts of data and learn patterns, has emerged as a promising solution to enhance AGC system security. Therefore, this research proposal aims to address the challenges associated with detecting and mitigating cyber-attacks on AGC in smart grids by leveraging machine learning techniques on automatic generation control of two-area power systems. By utilizing historical data, the proposed system will learn the normal behavior patterns of AGC and identify deviations caused by cyber-attacks. Once an attack is detected, appropriate mitigation strategies will be employed to safeguard the AGC system. The outcomes of this research will provide power system operators and administrators with valuable insights into the vulnerabilities of AGC systems in smart grids and offer practical solutions to enhance their cyber resilience.

Keywords: machine learning, cyber-attacks, automatic generation control, smart grid

Procedia PDF Downloads 81

Authors: David Cobham

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In higher education, a great degree of importance is placed on the internationalization of the student experience. This is seen as a valuable contributor to elements such as building confidence, broadening knowledge, creating networks and connections, and enhancing employability for current students who will become the next generation of managers in technology and business. The COVID-19 pandemic has affected all areas of people’s lives. The limitations of travel coupled with the fears and concerns generated by the health risks have dramatically reduced the opportunity for students to engage with this agenda. Institutions of higher education have been required to rethink fundamental aspects of their business model from recruitment and enrolment through learning approaches, assessment methods, and the pathway to employment. This paper presents a case study which focuses on student mobility and how the physical experience of being in another country, either to study, to work, to volunteer or to gain cultural and social enhancement, has of necessity been replaced by alternative approaches. It considers trans-national education as an alternative to physical study overseas, virtual mobility and internships as an alternative to international work experience, and adopting collaborative online projects as an alternative to in-person encounters. The paper concludes that although these elements have been adopted to address the current situation, the lessons learned and the feedback gained suggests that they have contributed successfully in new and sometimes unexpected ways and that they will persist beyond the present to become part of the 'new normal' for the future. That being the case, senior leaders of institutions of higher education will be required to revisit their international plans and to rewrite their international strategies to take account of and build upon these changes.

Keywords: higher education management, internationalization, transnational education, virtual mobility

Procedia PDF Downloads 101

Authors: Diana Ruth Caga-Anan

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Classroom management is a critical skill but the styles are constantly evolving. It is constantly under pressure particularly in the college education level due to diversity in student profiles, modes of delivery, and marketization of higher education. This study sought to analyze the extent of implementation of classroom management practices (CMPs) of the college instructors of the Hotel, Restaurant, and Institution Management of a premier university in the Philippines. It was also determined if their length of teaching affects their classroom management style. A questionnaire with sixteen 'evidenced-based' CMPs grouped into five critical features of classroom management, adopted from the literature search of Simonsen et al. (2008), was administered to 4 instructor-respondents and to their 88 students. Weighted mean scores of each of the CMPs revealed that there were differences between the instructors’ self-scores and their students’ ratings on their implementation of CMPs. The critical feature of classroom management 'actively engage students in observable ways' got the highest mean score, corresponding to 'always' from the instructors’ self-rating and 'frequently' from their students’ ratings. However, 'use a continuum of strategies to respond to inappropriate behaviors' got the lowest scores from both the instructors and their students corresponding only to 'occasionally'. Analysis of variance showed that the only CMP affected by the length of teaching is the practice of 'prompting students to respond'. Based on the findings, some recommendations for the instructors to improve on the critical feature where they scored low are discussed and suggestions are included for future research.

Keywords: classroom management, CMPs, critical features, evidence-based classroom management practices

Procedia PDF Downloads 165

Authors: Jeffrey M. Byford

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The conceptualized theory of decision making on historical events often does not conform to uniform beliefs among students. When presented the opportunity, many students have differing opinions and rationales associated with historical events and outcomes. The intent of this paper was to provide students with the economic, social and political dilemmas associated with the autonomy of East Berlin. Students ranked seven possible actions from the most to least acceptable. In addition, students were required to provide both positive and negative factors for each decision and relative ranking. Results from this activity suggested that while most students chose a financial action towards West Berlin, some students had trouble justifying their actions.

Keywords: content-centered learning, cold war, Berlin, decision-making

Procedia PDF Downloads 450

Authors: Boumediene Hamzi, Turky N. AlOtaiby, Saleh AlShebeili, Arwa AlAnqary

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We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.

Keywords: kernel methods, maximum mean discrepancy, seizure detection, machine learning

Procedia PDF Downloads 231

Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

Procedia PDF Downloads 23

Authors: Budhvin T. Withana, Sulochana Rupasinghe

Abstract:

The problem of Dyslexia and Dysgraphia, two learning disabilities that affect reading and writing abilities, respectively, is a major concern for the educational system. Due to the complexity and uniqueness of the Sinhala language, these conditions are especially difficult for children who speak it. The traditional risk detection methods for Dyslexia and Dysgraphia frequently rely on subjective assessments, making it difficult to cover a wide range of risk detection and time-consuming. As a result, diagnoses may be delayed and opportunities for early intervention may be lost. The project was approached by developing a hybrid model that utilized various deep learning techniques for detecting risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16 and YOLOv8 were integrated to detect the handwriting issues, and their outputs were fed into an MLP model along with several other input data. The hyperparameters of the MLP model were fine-tuned using Grid Search CV, which allowed for the optimal values to be identified for the model. This approach proved to be effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention of these conditions. The Resnet50 model achieved an accuracy of 0.9804 on the training data and 0.9653 on the validation data. The VGG16 model achieved an accuracy of 0.9991 on the training data and 0.9891 on the validation data. The MLP model achieved an impressive training accuracy of 0.99918 and a testing accuracy of 0.99223, with a loss of 0.01371. These results demonstrate that the proposed hybrid model achieved a high level of accuracy in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, Dyslexia, Dysgraphia, deep learning, learning disabilities, data science

Procedia PDF Downloads 100

Authors: Yarhands Dissou Arthur, Samuel Asiedu Addo, Jonathan Annan

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Interest in subject specific is very essential in the quest to ensure effective teaching and learning. In building interest in subject specific areas requires certain factors and strategies well-spelled out.The factors such as the gender of the student, the type of basic school attended, the parent interest as well as the scare of the basic school mathematics teacher is very important to consider. The relational effect and the contribution these above mentioned variables on student have not been fully investigated and this paper address the effect of these factors on the student interest. In the attainment of this goal, the current paper addresses the effect of parent interest, the type of basic school attended, the scare by basic school mathematics teacher and its effect on student’s interest in mathematics. A cross sectional data collected from two hundred and sixty post-secondary school student were analyzed using descriptive and inferential statistical methods by aid of SPSS version 16. The study found that parent interest and value for mathematics significantly influenced students interest and joy in solving mathematical problems. Moreover, we also observed that the fear imposed by basic school mathematics teachers was found to significantly influence students’ interest. The study further found that the type of basic school attended and gender are factors that do not influence students’ interest in mathematics. In addition to concluding that a student’s interest is influenced by both parent interest and the fear of basic school mathematics teacher, the study also showed that the type of basic school attended and gender does not affect the students’ interest in mathematics.

Keywords: gender, mathematics interest, teacher interest, teacher interest, student interest

Procedia PDF Downloads 361

Authors: Seedwell Sithole

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One of the persistent problems in accounting education is how to effectively support students’ learning. A promising technique to this issue is to investigate the extent that learning is determined by the design of instructional material. This study examines the academic performance of students using three instructional designs in financial accounting. Student’s performance scores and reported mental effort ratings were used to determine the instructional effectiveness. The findings of this study show that accounting students prefer graph and text designs that are integrated. The results suggest that spatially separated graph and text presentations in accounting should be reorganized to align with the requirements of human cognitive architecture.

Keywords: accounting, cognitive load, education, instructional preferences, students

Procedia PDF Downloads 140

Authors: Kamolrat Turner, Chularat Howharn

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Critical thinking and creativity are desirable competency for contemporary nurses although there are growing concerns supporting a disturbing paucity in its achievement. Nursing colleges in Thailand have developed teaching strategies and curricula that nurture critical thinking and creativity dispositions according academic levels. Objectives: This descriptive study identified critical thinking and creativity dispositions of Thai nursing students according academic levels. Methods: A cross-sectional questionnaire survey was conducted among 515 nursing students for four academic levels. All are studying at Boromarajonani College of Nursing Chon Buri, Thailand. Descriptive and univariate general linear model analysis were applied. Results: The scores on critical thinking disposition gradually increased as academic level is rising from the junior year throughout the senior year, but its scores are neutral. Scores on creativity skill is neutral and constant thorough the four academic years. The fourth grade students had slightly higher scores on creativity when compared to others. A significant relationship between critical thinking and creativity was also found. Conclusions: The scores on critical thinking disposition gradually improved which greatly increased in the senior year. However, creativity has neutrally progressed. The findings suggest the importance of targeting the development of curriculum and teaching strategies for all grades of nursing students to increase their critical thinking and creativity skills.

Keywords: critical thinking, creativity, undergraduate nursing students, competency

Procedia PDF Downloads 425

Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

Procedia PDF Downloads 125

Authors: F. Slaven, M. Uys, Y. Erasmus

Abstract:

The South African National Mental Health Education Programme (SANMHEP) was a National Department of Health (NDoH) initiative to strengthen mental health services in South Africa in collaboration with the Foundation for Professional Development (FPD), SANOFI and the various provincial departments of health. The programme was implemented against the backdrop of a number of challenges in the management of mental health in the country related to staff shortages and infrastructure, the intersection of mental health with the growing burden of non-communicable diseases and various forms of violence, and challenges around substance abuse and its relationship with mental health. The Mental Health Care Act (No. 17 of 2002) prescribes that mental health should be integrated into general health services including primary, secondary and tertiary levels to improve access to services and reduce stigma associated with mental illness. In order for the provisions of the Act to become a reality, and for the journey of mental health patients through the system to improve, sufficient and skilled health care providers are critical. SANMHEP specifically targeted Medical Doctors and Professional Nurses working within the facilities that are listed to conduct 72-hour assessments, as well as District Hospitals. The aim of the programme was to improve the clinical diagnosis and management of mental disorders/conditions and the understanding of and compliance with the Mental Health Care Act and related Regulations and Guidelines in the care, treatment and rehabilitation of mental health care users. The course used a blended-learning approach and trained 1 120 health care providers through 36 workshops between February and November 2019. Of those trained, 689 (61.52%) were Professional Nurses, 337 (30.09%) were Medical Doctors, and 91 (8.13%) indicated their occupation as ‘other’ (of these more than half were psychologists). The pre- and post-evaluation of the face-to-face training sessions indicated a marked improvement in knowledge and confidence level scores (both clinical and legislative) in the care, treatment and rehabilitation of mental health care users by participants in all the training sessions. There was a marked improvement in the knowledge and confidence of participants in performing certain mental health activities (on average the ratings increased by 2.72; or 27%) and in managing certain mental health conditions (on average the ratings increased by 2.55; or 25%). The course also required that participants obtain 70% or higher in their formal assessments as part of the online component. The 337 participants who completed and passed the course scored 90% on average. This illustrates that when participants attempted and completed the course, they did very well. To further assess the effect of the course on the knowledge and behaviour of the trained mental health care practitioners a mixed-method outcome evaluation is currently underway consisting of a survey with participants three months after completion, follow-up interviews with participants, and key informant interviews with department of health officials and course facilitators. This will enable a more detailed assessment of the impact of the training on participants' perceived ability to manage and treat mental health patients.

Keywords: mental health, public health facilities, South Africa, training

Procedia PDF Downloads 115

Authors: Frazer G. Thompson

Abstract:

Israel is a nation of cultural and historical diversity, yet the success factors for a modern Bedouin-Arab high-tech entrepreneur seem to be different from those of other Jewish-Israeli citizens. The purpose of this descriptive narrative case study is to explore how an Arab-Israeli all Negev-Bedouin technology company has succeeded in the Israeli high-tech industry by utilizing technology and engineering career opportunities available to Bedouin youth for ‘Sadel Tech,’ at Be’er-Sheva, the Negev, Israel. Methods: The strategy of inquiry seeks to explore real-life contextual understandings, multi-level perspectives, and the cultural influences of personal, community, educational, and entrepreneurial factors. The research methodology includes in-depth one-on-one interviews, focus group sessions, and overt observation to explore the meaning and understanding of the constructs toward determining the effect all or a few of the elements may have on the overall success factors of the company. Results: Study results indicate that the state-run educational system in Israel fails to adequately integrate important aspects of Bedouin culture into the learning environment. However, Bedouin entrepreneurs are finding ways to compensate for these inadequacies by utilizing non-traditional methods of teaching, learning, and doing business. Government incentives for Bedouin start-ups are also recognized as contributors. Employees of Sadel live and work in the Negev, the Gaza Strip, and the West Bank, further informing the study that the traditions of tribal etiquette continue to contribute to modern Bedouin-Arab business culture. Conclusion: Bedouin's business success in Israel is a multi-dimensional concept. While cultural acumen plays a prominent and unique role for both Arab-Israelis and Jewish-Israelis in economic and entrepreneurial pursuits, the marginalization of the Bedouin continues to contribute to the lack of educational and professional opportunities for Bedouin in Israel. Although recognized as important at the government level, programs necessary to implement the infrastructure required to support Bedouin entrepreneurship in Israel remain infantile. The Israeli Government is providing opportunities through grants and other incentives for Bedouin entrepreneurial start-ups, indicating that Israel has recognized the impact of this growing demographic. However, although many Bedouin graduates from University each year with advanced degrees, opportunities for Bedouin within the Israeli high-tech sector remain scarce.

Keywords: Bedouin education, Bedouin entrepreneur, economic anthropology, ethnic business opportunities, Israeli tech, Silicon Wadi

Procedia PDF Downloads 117

Authors: Niina Meriläinen

Abstract:

Digital platforms are arenas for dialogue, various kinds of political debates, information and news gathering, policymaking, and social change processes. Human rights serve as examples of social and political issues that are universally noted as principles and yet often violated on digital platforms as well as in the analog world. Digital platforms in this study are different Internet sites, blogs, discussion platforms, social media apps, and gaming. Various actors, from human rights activists and non-governmental organizations to individual people, governments, and corporations, use digital platforms along with analog arenas to discuss and defend human rights, while violators can find new victims and continue violating rights on the same platforms. Digital platforms create opportunities for various women and minorities to empower themselves and others and to be active in various arenas of society and policymaking. At the same time, digital platforms pose threats to human rights globally, especially to women, girls, and minorities. The results of this meta-study of n=120 academic case studies indicate that more research is needed to determine the framework of human rights and human rights on digital platforms. A broad discussion must be had on what human rights require in the digital realm and how ICTs may enhance or threaten our ability to respect, protect, and fulfill a wide variety of human rights while various digital platforms pose multiple threats to human rights. This relates to the willingness of political decision-makers to act upon various crimes committed on and with online platforms. More research is needed to determine the framework of digital human rights and human rights on digital platforms in relation to political communication and decision-making. It is important to develop a framework in which these are defined. It must be discussed who participates in this process: those whose rights are violated, companies that profit by selling our personal data, activists, governments, and some unknown actors. In the end, the question comes back to who has the power to define what we talk about, when, and where. This use of power plays a big role. Digital platforms illustrate the darker side of technological progress, which, on the one hand, has given various people the possibility to engage in society, empower themselves, and take ownership of their rights globally. At the same time, the platforms enable others to use the same platforms to find victims, abuse them, and exploit them. Bullying, harassment, and violence are rampant on various digital platforms, where minorities and people with limited support are victims. There is indeed a need for a discussion of normative values in the era of fake news, the power of influencers, Trumpism, and institutionalized disregard for human rights, gender equality, and the elimination of gender-based violence online. Attention and obligations must be placed on politicians and internet architecture, such as corporations, and their roles in human rights and their violations online.

Keywords: human rights, digital platforms, violations, internet, social media

Procedia PDF Downloads 52

Authors: Eylem Tas

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This empirical study aims to explore the development of students' entrepreneurial skills through problem-based learning within the context of university-industry collaborations (UICs) in curriculum co-design and co-delivery (CDD). The research question guiding this study is: "How do problem-based learning and university-industry collaborations influence the development of students' entrepreneurial skills in the context of curriculum co-design and co-delivery?” To address this question, the study was conducted in a big city in Germany and involved interviews with stakeholders from various industries, including the private sector, government agencies (govt), and non-governmental organizations (NGOs). These stakeholders had established collaborative partnerships with the targeted university for projects encompassing entrepreneurial development aspects in CDD. The study sought to gain insights into the intricacies and subtleties of UIC dynamics and their impact on fostering entrepreneurial skills. Qualitative content analysis, based on Mayring's guidelines, was employed to analyze the interview transcriptions. Through an iterative process of manual coding, 442 codes were generated, resulting in two main sections: "the role of problem-based learning and UIC in fostering entrepreneurship" and "challenges and requirements of problem-based learning within UIC for systematical entrepreneurship development.” The chosen experimental approach of semi-structured interviews was justified by its capacity to provide in-depth perspectives and rich data from stakeholders with firsthand experience in UICs in CDD. By enlisting participants with diverse backgrounds, industries, and company sizes, the study ensured a comprehensive and heterogeneous sample, enhancing the credibility of the findings. The first section of the analysis delved into problem-based learning and entrepreneurial self-confidence to gain a deeper understanding of UIC dynamics from an industry standpoint. It explored factors influencing problem-based learning, alignment of students' learning styles and preferences with the experiential learning approach, specific activities and strategies, and the role of mentorship from industry professionals in fostering entrepreneurial self-confidence. The second section focused on various interactions within UICs, including communication, knowledge exchange, and collaboration. It identified key elements, patterns, and dynamics of interaction, highlighting challenges and limitations. Additionally, the section emphasized success stories and notable outcomes related to UICs' positive impact on students' entrepreneurial journeys. Overall, this research contributes valuable insights into the dynamics of UICs and their role in fostering students' entrepreneurial skills. UICs face challenges in communication and establishing a common language. Transparency, adaptability, and regular communication are vital for successful collaboration. Realistic expectation management and clearly defined frameworks are crucial. Responsible data handling requires data assurance and confidentiality agreements, emphasizing the importance of trust-based relationships when dealing with data sharing and handling issues. The identified key factors and challenges provide a foundation for universities and industrial partners to develop more effective UIC strategies for enhancing students' entrepreneurial capabilities and preparing them for success in today's digital age labor market. The study underscores the significance of collaborative learning and transparent communication in UICs for entrepreneurial development in CDD.

Keywords: collaborative learning, curriculum co-design and co-delivery, entrepreneurial skills, problem-based learning, university-industry collaborations

Procedia PDF Downloads 56

Authors: Saima Ali, Liaqat Ali, Nasir Orakzai

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Background: Hydronephrosis is the most common abnormal finding in the urinary tract on prenatal screening with Ultrasonography. The prenatal hydronephrosis is a diagnostic dilemma in differentiating between obstructive variant versus physiologic hydronephrosis. The assessment and prompt diagnosis of prenatal hydronephrosis is important because of the fact that untreated obstructive hydronephrosis usually leads to recurrent UTI, Urosepsis, deterioration of renal functions, non-functioning kidneys, and even end-stage renal disease. Objectives: To determine the etiology and outcome of postnatal treatment of children with prenatal hydronephrosis in two teaching hospitals of Khyber Pakhtunkhwa (KPK) Methods: It is a multicentric descriptive study that was conducted in department of Paediatrics in Kuwait teaching hospital Peshawar and Department of Urology in Institute of Kidney Diseases Hayatabad Medical Complex Peshawar from January 2008 till December 2010. Total numbers of 64 neonates were included in the study with the mean follow-up of 14.5 months. All the diagnostic data in prenatal, postnatal data, and operative and non-operative data were collected on structured Proforma and was analyzed on SPSS version 17. Results: Out of 64 patients, 39 (60.9 %) were male while 25 were female. 52 patients had unilateral while 12 patients had bilateral hydronephrosis. Based upon prenatal USG in term of AP diameter, 37 (57 %) patients had mild hydronephrosis (5-10 mm AP diameter), 14 patients had moderate hydronephrosis (10-15 mm AP diameter) while 13 patients had gross hydronephrosis (More than 15mm). Regarding etiology, 44(76 %) patients were labeled as physiologic hydronephrosis, 11 patients (9.3%) with PUJ obstruction, 5 patients with Vesicoureteric reflux (VUR) and 4 patients with posterior urethral valves. Surgery was performed in total of 15 (23.4%) patients that included open Pyeloplasty in 11 patients, Vesicostomy followed by posterior valve fulguration in 4 patients. All the patients of VUR treated medically. The severity in the grade of prenatal hydronephrosis is significantly associated with the need for definitive urological surgery p < 0.005. Ancillary procedures like percutaneous nephrostomy (PCN) were inserted 7 patients. Conclusions: Prenatal hydronephrosis is a common ailment associated with significant morbidity. Physiological Hydronephrosis and VUR can be successfully treated with medical treatment. However obstructive PUJ obstructions and posterior urethral valves require surgical correction with a good success rate.

Keywords: prenatal hydronephrosis, Pelviureteric Junction (PUJ) Obstruction, vesicoureteric reflux, posterior urethral valve, renography

Procedia PDF Downloads 243

Authors: Peter Akayuure, Michael Johnson Nabie

Abstract:

Similar to many colonized nations in the world, one indelible mark left by colonial masters after Ghana’s independence in 1957 has been the fact that many contexts used to teach statistics and probability concepts are often alien and do not resonate with the social domain of our indigenous Ghanaian child. This has seriously limited the understanding, discoveries, and applications of mathematics for national developments. With the recent curriculum demands of making the Ghanaian child mathematically literate, this qualitative study involved video recordings and mathematical analysis of play sessions of an indigenous girl game called Ampe with the aim to demystify the concepts in probability theorem, which is applied in mathematics related fields of study. The mathematical analysis shows that the game of Ampe, which is widely played by school girls in Ghana, is suitable for learning concepts of the probability theorems. It was also revealed that as a girl game, the use of Ampe provides good lessons to educators, textbook writers, and teachers to rethink about the selection of mathematics tasks and learning contexts that are sensitive to gender. As we undertake to transform teacher education and student learning, the use of indigenous games should be critically revisited.

Keywords: Ampe, mathematical analysis, probability theorem, Ghanaian girl game

Procedia PDF Downloads 365