Search results for: student-centered teaching and learning

Authors: Pei-Gi Shu, Yin-Hua Yeh, Chao-Ting Chen

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Using a dataset consisting of 7,120 firm-year observations from the Taiwan stock market over the 2007-2011 sample period, we find a significantly negative relationship between board external connectedness and firm diversification. We propose a learningeffect hypothesis indicating that an externally connected board member’s experiences in other companies directly affect his recommendations regarding the underlying firm’s diversification. The partial correlation between diversification and the performance of firms with externally connected board members is used as a proxy for the learning effect. The empirical results show that the learning effect is asymmetrically embedded in firm diversification, with negative experiences having a greater effect on firm diversification than positive experiences. Externally connected board members are associated with reduced diversification in one firm after they learn that diversification is detrimental to value in other companies. Moreover, the diversification of a firm due to board external connectedness is moderated by the controlling owner’s interest alignment and entrenchment.

Keywords: board, external, connectedness, diversification

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Authors: Nida B. Syed

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Following second wave of the current pandemic COVID-19, education transmission is occurring via both the modes of education, that is, online as well as offline in the college. The aim of the current study was, therefore, to bring forth the comparative analysis of both the modes of education and their impact on the levels of academic stress and states of the mental wellbeing of the students amidst the current pandemic. Measures of the constructs were obtained by the online Google forms, which consist of the Perceptions of Academic Stress Scale (PASS) by and Warwick-Edinburg Mental Well-being Scale, from a sample of 100 undergraduate students aged 19-25 years studying in different colleges of Bengaluru, India. Modes of education were treated as the predictor variables whilst academic stress, and mental wellbeing constituted the criterion variables. Two-way ANOVA was employed. Results show that the levels of academic stress are found to be a bit higher in students attending online classes as compared to those taking offline classes in college (MD = 1.10, df = 98, t = 0.590, p > 0.05), whereas mental wellbeing is found to be low in students attending offline classes in colleges than those taking online classes (MD = 5.180, df = 98, t =2.340, p > 0.05 level). The combined interactional effect of modes of education and academic stress on the states of the mental wellbeing of the students is found to be low (R2 = 0.053), whilst the combined impact of modes of education and mental wellbeing on the levels of academic stress was found to be quite low (R2 = 0.014). It was concluded that modes of education have an impact on levels of academic stress and states of the mental well-being of the students amidst the current pandemic, but it is low.

Keywords: modes of education, online learning, offline learning, pandemic

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Authors: Kevin Fernagut, Olivier Flauzac, Erick M. G. Robledo, Florent Nolot

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The adoption of modern lightweight virtualization often comes with new threats and network vulnerabilities. This paper seeks to assess this with a different approach studying the behavior of a testbed built with tools such as Kernel-Based Virtual Machine (KVM), Linux Containers (LXC) and Docker, by performing stress tests within a platform where students experiment simultaneously with cyber-attacks, and thus observe the impact on the campus network and also find the best solution for cyber-security learning. Interesting outcomes can be found in the literature comparing these technologies. It is, however, difficult to find results of the effects on the global network where experiments are carried out. Our work shows that other physical hosts and the faculty network were impacted while performing these trials. The problems found are discussed, as well as security solutions and the adoption of new network policies.

Keywords: containerization, containers, cybersecurity, cyberattacks, isolation, performance, virtualization, virtual machines

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Authors: Said Alshukri, Mazhar Hussain Malik

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Green hydrogen is hydrogen produced using renewable energy sources. In the last few years, Oman aimed to reduce its dependency on fossil fuels. Recently, the hydrogen economy has become a global trend, and many countries have started to investigate the feasibility of implementing this sector. Oman created an alliance to establish the policy and rules for this sector. With motivation coming from both global and local interest in green hydrogen, this paper investigates the potential of producing hydrogen from wind and solar energies in three different locations in Oman, namely Duqm, Salalah, and Sohar. By using machine learning-based software “WEKA” and local metrological data, the project was designed to figure out which location has the highest wind and solar energy potential. First, various supervised models were tested to obtain their prediction accuracy, and it was found that the Random Forest (RF) model has the best prediction performance. The RF model was applied to 2021 metrological data for each location, and the results indicated that Duqm has the highest wind and solar energy potential. The system of one wind turbine in Duqm can produce 8335 MWh/year, which could be utilized in the water electrolysis process to produce 88847 kg of hydrogen mass, while a solar system consisting of 2820 solar cells is estimated to produce 1666.223 MWh/ year which is capable of producing 177591 kg of hydrogen mass.

Keywords: green hydrogen, machine learning, wind and solar energies, WEKA, supervised models, random forest

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Authors: C. F. Jones Couto, L. C. Motlhaolwa, K. Williams

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This article explores the re-imagining of physical education teacher education in South African higher education. Utilising student reflections from a physical education practical module, valuable insights into student experiences were obtained about the current physical education pedagogical approaches and potential areas for improvement. The traditional teaching model of physical education is based on the idea of teaching students a variety of sports and physical activities. However, this model has been shown to be ineffective in promoting lifelong physical activity. The modern world demands a more holistic approach to health and wellness. Data was collected using the arts-based collage method in combination with written group reflections from 139 second-year undergraduate physical education students. This study employed thematic analysis methods to gain a comprehensive understanding of the data and extract a broader perspective on the students' experiences. The study aimed to empower student teachers to learn, think, and act creatively within the many educational models that impact their experience, contributing to the ongoing efforts of re-imagining physical education teacher education in South African higher education. This research is significant as the students' valuable insights reflected that they can think and work across disciplines. Sustainable development goals and graduate attributes are important concepts that can contribute to student preparation. Using a multi-model educational approach based on the cultural-historical theory, higher education institutions can help develop graduate attributes that will prepare students for success in the workplace and life.

Keywords: holistic education, graduate attributes, physical education, teacher education, student experiences, sustainable development goals

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Authors: Lucy Tsado

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As cybercrime and cyberattacks continue to increase, the need to respond will follow suit. When cybercrimes occur, the duty to respond sometimes falls on law enforcement. However, criminal justice students are not taught concepts in cybersecurity and digital forensics. There is, therefore, an urgent need for many more institutions to begin teaching cybersecurity and related courses to social science students especially criminal justice students. However, many faculty in universities, colleges, and high schools are not equipped to teach these courses or do not have the knowledge and resources to teach important concepts in cybersecurity or digital forensics to criminal justice students. This research intends to develop curricula and training programs to equip faculty with the skills to meet this need. There is a current call to involve non-technical fields to fill the cybersecurity skills gap, according to experts. There is a general belief among non-technical fields that cybersecurity education is only attainable within computer science and technologically oriented fields. As seen from current calls, this is not entirely the case. Transitioning into the field is possible through curriculum development, training, certifications, internships and apprenticeships, and competitions. There is a need to identify how a cybersecurity eco-system can be created at a university to encourage/start programs that will lead to an interest in cybersecurity education as well as attract potential students. A short-term strategy can address this problem through curricula development, while a long-term strategy will address developing training faculty to teach cybersecurity and digital forensics. Therefore this research project addresses this overall problem in two parts, through curricula development for the criminal justice discipline; and training of faculty in criminal justice to teaching the important concepts of cybersecurity and digital forensics.

Keywords: cybersecurity education, criminal justice, curricula development, nontechnical cybersecurity, cybersecurity, digital forensics

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Authors: K. R. Parvathy, R. Rao Bhavani , M. L. McLain, Kamal Bijlani, R. Jayakrishnan

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Augmented Reality (AR) technology is considered to be an important emerging technology used in education today. One potentially key use of AR in education is to teach socio-scientific issues (SSI), topics that inure students towards social conscience and critical thinking. This work uses multiple markers and virtual buttons that interact with each other, creating a life-like visual spectacle. Learning about issues such as global warming by using AR technology, students will have an increased sense of experiencing immersion, immediacy, and presence, thereby enhancing their learning as well as likely improving their ability to make better informed decisions about considerations of such issues. Another advantage of AR is that it is a low cost technology, making it advantageous for educators to adapt to their classrooms. Also in this work we compare the effectiveness of AR versus ordinary video by polling a group of students to assess the content understandability, effectiveness and interaction of both the delivery methods.

Keywords: augmented reality, global warming, multiple markers, virtual buttons

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Authors: Tukur Husaini Nahuche

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The parent resources play a vital role in the life of the offspring. It help give children basic necessities of life like food, clothing, and housing. In a like manner financial assets allow parents to move into neighborhood with more affluent school systems, to pay school bills, purchase expensive technologies like personal computer, save money for tutoring books, magazines, journals, Newspapers etc. Making of proper provision in the home environment conducive for learning after school hours and creation of other outdoor activities for them are what necessitate in enhancing and accelerating children’s learning opportunities. Indeed, this paper intends to discuss parental investment in education, parent income resources, parental education, occupation, and income as relatively influencing children’s access to quality education. With the hope that families would provide equal opportunities for children irrespective of their sex, intelligence, subject choice,etc.

Keywords: parental investment, children's access, quality education

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Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh

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We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.

Keywords: IDS, Machine learning, WSN, ZigBee technology

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Authors: Bhaveek Maini, Sanjay Dhanka, Surita Maini

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Electroencephalography (EEG) is used to classify several epileptic seizures worldwide. It is a very crucial task for the neurologist to identify the epileptic seizure with manual EEG analysis, as it takes lots of effort and time. Human error is always at high risk in EEG, as acquiring signals needs manual intervention. Disease diagnosis using machine learning (ML) has continuously been explored since its inception. Moreover, where a large number of datasets have to be analyzed, ML is acting as a boon for doctors. In this research paper, authors proposed two different ML models, i.e., logistic regression (LR) and Naïve Bayes (NB), to predict epileptic seizures based on general parameters. These two techniques are applied to the epileptic seizures recognition dataset, available on the UCI ML repository. The algorithms are implemented on an 80:20 train test ratio (80% for training and 20% for testing), and the performance of the model was validated by 10-fold cross-validation. The proposed study has claimed accuracy of 81.87% and 95.49% for LR and NB, respectively.

Keywords: epileptic seizure recognition, logistic regression, Naïve Bayes, machine learning

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Authors: Sevket Ceyhun Cetin, Mehmet Oren

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According to existing educational research studies, students learn better with high teacher quality. Therefore, professional development has become a crucial way of increasing the quality of novices and veteran in-service teachers by providing support regarding content and pedagogy. To answer what makes PD effective, researchers have studied different PD models and revealed some critical elements that need to be considered, such as duration of a PD and the manner of delivery (e.g., lecture vs. engaging). Also, it has been pointed out that if PDs are prepared as one-size-fits-all, they most likely be ineffective in addressing teachers’ needs toward improving instructional quality. Instead, teachers’ voices need to be heard, and the foci of PDs should be determined based on their specific needs. Thus, this study was conducted to identify professional development needs of middle school mathematics teachers based on their self-evaluation of their performances in light of teaching standards. This study also aimed to explore whether the PD needs with respect to years of teaching experience (novice vs. veteran). These teachers had participated in a federally-funded research grant, which aimed to improve the competencies of 6-9 grade-level mathematics teachers in pedagogy and content areas. In the research project, the participants had consistently videoed their lessons throughout a school year and reflected on their performances, using Teacher Advanced Program (TAPTM) rubric, which was based on the best practices of teaching. Particularly, they scored their performances in the following areas and provided evidence as the justifications of their scores: Standards and Objectives, Presenting Instructional Content, Lesson Structure and Pacing, Activities and Materials, Academic Feedback, Grouping Students, and Questioning. The rating scale of the rubric is 1 through 5 (i.e., 1=Unsatisfactory [performance], 3=Proficient, and 5=Exemplary). For each area mentioned above, the numerical scores of 77 written reports (for 77 videoed lessons) of 24 teachers (nnovices=12 and nveteran=12) were averaged. Overall, the average score of each area was below 3 (ranging between 2.43 and 2.86); in other words, teachers judged their performances incompetent across the seven areas. In the second step of the data analysis, the lowest three areas in which novice and veteran teachers performed poorly were selected for further qualitative analysis. According to the preliminary results, the lowest three areas for the novice teachers were: Questioning, Grouping Students, and Academic Feedback. Grouping Students was also one of the lowest areas of the veteran teachers, but the other two areas for this group were: Lesson Structure & Pacing, and Standards & Objectives. Identifying in-service teachers’ needs based on their reflective practitioner experiences provides educators very crucial information that can be used to create more effective PD that improves teacher quality.

Keywords: mathematics teacher, professional development, self-reflection, video data

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Authors: Devendra Kr Tayal, Vrinda Gupta, Aastha Bansal, Khushi Singh, Sristi Sharma, Hunny Gaur

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In today's world, suicide is a serious problem. In order to save lives, early suicide attempt detection and prevention should be addressed. A good number of at-risk people utilize social media platforms to talk about their issues or find knowledge on related chores. Twitter and Reddit are two of the most common platforms that are used for expressing oneself. Extensive research has already been done in this field. Through supervised classification techniques like Nave Bayes, Bernoulli Nave Bayes, and Multiple Layer Perceptron on a Reddit dataset, we demonstrate the early recognition of suicidal ideation. We also performed comparative analysis on these approaches and used accuracy, recall score, F1 score, and precision score for analysis.

Keywords: machine learning, suicide ideation detection, supervised classification, natural language processing

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Authors: Khaled Hamdan, Abid Amorri, Fatima Hamdan

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Involving students in English language learning process and achieving an adequate English language proficiency in the target language can be a great challenge for both teachers and students. This can prove even a far greater challenge to engage students with special needs (Dyslexia) if they have physical impairment and inadequate mastery of basic communicative language competence/proficiency in the target language. From this perspective, technology like robots can probably be used to enhance learning process for the special needs students who have extensive communication needs, who face continuous struggle to interact with their peers and teachers and meet academic requirements. Robots, precisely NAO, can probably provide them with the perfect opportunity to practice social and communication skills, and meet their English academic requirements. This research paper aims to identify to what extent robots can be used to improve students’ social interaction and communication skills and to understand the potential for robotics-based education in motivating and engaging UAEU dyslexic students to meet university requirements. To reach this end, the paper will explore several factors that come into play – Motion Level-involving cognitive activities, Interaction Level-involving language processing, Behavior Level -establishing a close relationship with the robot and Appraisal Level- focusing on dyslexia students’ achievement in the target language.

Keywords: dyslexia, robot technology, motion, interaction, behavior and appraisal levels, social and communication skills

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Authors: Ali Akbar Jabbari

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The present study assessed the acquisition of L3 French direct objects by Persian speakers who had already learned English as their L2. The ultimate goal of this paper is to extend the current knowledge about the CLI phenomenon in the realm of third language acquisition by examining the role of Persian and English as background languages and learners’ English level of proficiency in their performance on French direct object. To fulfill this, the assumptions of three L3 hypotheses, namely L1 Transfer, L2 Status Factor, and Cumulative Enhancement Model, were examined. The research sample was comprised of 40 undergraduate students in the fields of English language and literature and translation studies at Birjand University in Iran. According to the English proficiency level of learners revealed by the Quick Oxford English Placement test, the participants were grouped as upper intermediate and lower intermediate. A grammaticality judgment and a translation test were administered to gather the required data on learners' comprehension and production of the desired structure in French. It was demonstrated that the rate of positive transfer from previously learned languages was more potent than the rate of negative transfer. A Comparison of groups' performances revealed a significant difference between upper and lower intermediate groups in positing French direct objects correctly. However, the upper intermediate group did not significantly differ from the lower intermediate group in negative transfer. It can be said that by increasing the L2 proficiency of the learners, they could use their previous linguistic knowledge more efficiently. Although further examinations are needed, the current study contributed to a better characterization of cross-linguistic influence in third language acquisition. The findings help French teachers and learners to positively exploit the prior knowledge of Persian and English and apply it in in the multilingual context of French direct object's teaching and learning process.

Keywords: Cross-Linguistic Influence, Persian, French & English Direct Object, Third Language Acquisition, Language Transfer

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Authors: Akshat Mathur, Sunil Kumar Khatri

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This paper presents brief about how the use of Artificial intelligence in respect to GUI testing can reduce workload by using DL-fueled method. This paper also discusses about how graphical user interface and event driven software testing can derive benefits from the use of AI techniques. The use of AI techniques not only reduces the task and work load but also helps in getting better output than manual testing. Although results are same, but the use of Artifical intelligence techniques for GUI testing has proven to provide ideal results. DL-fueled framework helped us to find imperfections of the entire webpage and provides test failure result in a score format between 0 and 1which signifies that are test meets it quality criteria or not. This paper proposes DL-fueled method which helps us to find the genuine GUI bugs and defects and also helped us to scale the existing labour-intensive and skill-intensive methodologies.

Keywords: graphical user interface, GUI, artificial intelligence, deep learning, ML technology

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Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Ayala Kobo-Greenhut, Amos Notea, Izhar Ben-Shlomo

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In R&D projects, there is no doubt about the need to change a current concept to an alternative one over time (i.e., concept leaping). Concept leaping is required since with most R&D projects uncertainty is present as they take place in dynamic environments. Despite the importance of concept leaping when needed, R&D teams may fail to do so (i.e., frozen concept). This research suggests a possible reason why frozen concept happens in the framework of quality engineering and control engineering. We suggest that frozen concept occurs since concept determines the derived plan and its implementation may be considered as equivalent to a closed-loop process, and is subject to the problem of not recognizing gaps as failures. We suggest that although implementing quality practices into an R&D project’s routine has many advantages, it intensifies the frozen concept problem since working according to quality practices relates to exploitation of learning behavior, while leaping to a new concept relates to exploring learning behavior.

Keywords: closed loop, control engineering, design, leaping, frozen concept, quality engineering, quality practices

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Authors: Lizhu Y. Davis, Gretchen Higginbottom, Vang Vang

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This presentation explores the adoption of library resources to engage students in a Visual Merchandising course during the 2016 spring semester. This study was a cross-disciplinary collaboration between the Fashion Merchandising Program and the Madden Library at California State University, Fresno. The goal of the project was to explore and assess the students’ use of library resources as a part of the Affordable Learning Solutions Initiative, a California State University (CSU) Office of the Chancellor Program that enables faculty to choose and provide high-quality, free or low-cost educational materials for their students. Students were interviewed afterwards and the results were generally favorable and provided insight into how students perceive and use library resources to support their research needs. This study reveals an important step in examining how open educational resources impact student learning.

Keywords: collaboration, library resources, open educational resources, visual merchandising

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

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In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent Transportation Systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning

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Authors: Mukhtar Sarkin-Kebbi

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Insurgency, religious extremism and other related religious crises become hydra-headed in Nigeria, which caused destruction of human lives and properties worth of billions naira. As result, millions people were displaced and million children were out of school most of whom from Muslims community. The wrong teaching and misinterpretation of Islam by some Muslim community fuel the spread of extremist ideology hatred among Muslim sects, non-Muslims and emergency of extremist groups, like Boko Haram. A multi-religious country like Nigeria to realise its development in all human aspects, there must be unity and religious tolerance. Many agreed that changing the ideologies of insurgents and religious extremism will require intellectual role with vigorous campaign. Muslim scholars can play a vital role in promoting social reform and peaceful coexistence. This paper discusses the importance of unity among Muslim community and religious tolerance in light of the Qur’an and the Hadith. The paper also reviews the relationship between Muslims and non Muslims during the life time the Prophet (S.A.W.) in order to serve as exemplary model. Contemporary issues such as religious extremism, sectarians, intolerance and their consequences were examined. To minify religious intolerance and extremism,the paper identifies the roles to be played by Muslim scholars with references from Qur’an and Sunnah. The paper concludes that to realise overall human development and eternal salvation, Muslim should shun away from any religious crises and embrace unity and religious tolerance. Finally the paper recommends among others that only pious and learned scholars should be allowed to preach in any religious gathering, Muslim should exercise patience, tolerance in dealing with Muslims and non Muslims. Muslims should leave by example from the teaching of Qur’an and Sunnah of the Prophet (S.A.W.).

Keywords: Muslim scholars, peace building, religious extremism, religious tolerance

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Authors: Venuste Kayinamura, V. Iyamuremye, A. Ngirabakunzi

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Anti-retroviral therapy (ART) for HIV patient can cause a deficiency in glucose metabolism by promoting insulin resistance, glucose intolerance, and diabetes, diabetes mellitus keep increasing among HIV-infected patients worldwide but there is limited data on levels of blood glucose and its relationship with antiretroviral drugs (ARVs) and HIV-infection worldwide, particularly in Rwanda. A convenient sampling strategy was used in this study and it involved 323 HIV patients (n=323). Patients who are HIV positive under ARVs were involved in this study. The patient’s blood glucose was analyzed using an automated machine or glucometer (COBAS C 311). Data were analyzed using Microsoft Excel and SPSS V. 20.0 and presented in percentages. The highest diabetes mellitus prevalence was 93.33 % in people aged >40 years while the lowest diabetes mellitus prevalence was 6.67% in people aged between 21-and 40 years. The P-value was (0.021). Thus, there is a significant association between age and diabetes occurrence. The highest diabetes mellitus prevalence was 28.2% in patients under ART treatment for more than 10 years, 16.7% were <5years while 20% of patients were on ART treatment between 5-10 years. The P-value here is (0.03), thus the incidence of diabetes is associated with long-term ART use in HIV-infected patients. This study assessed the prevalence of diabetes among HIV-infected patients under ARVs attending the University Teaching Hospital of Butare (CHUB), it shows that the prevalence of diabetes is high in HIV-infected patients under ARTs. This study found no significant relationship between gender and diabetes mellitus growth. Therefore, regular assessment of diabetes mellitus especially among HIV-infected patients under ARVs is highly recommended to control other health issues caused by diabetes mellitus.

Keywords: anti-retroviral, diabetes mellitus, antiretroviral therapy, human immune deficiency virus

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Authors: Ola Hall, Ibrahim Wahab, Thorsteinn Rognvaldsson, Mattias Ohlsson

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The subfield of poverty and welfare estimation that applies machine learning tools and methods on satellite imagery is a nascent but rapidly growing one. This is in part driven by the sustainable development goal, whose overarching principle is that no region is left behind. Among other things, this requires that welfare levels can be accurately and rapidly estimated at different spatial scales and resolutions. Conventional tools of household surveys and interviews do not suffice in this regard. While they are useful for gaining a longitudinal understanding of the welfare levels of populations, they do not offer adequate spatial coverage for the accuracy that is needed, nor are their implementation sufficiently swift to gain an accurate insight into people and places. It is this void that satellite imagery fills. Previously, this was near-impossible to implement due to the sheer volume of data that needed processing. Recent advances in machine learning, especially the deep learning subtype, such as deep neural networks, have made this a rapidly growing area of scholarship. Despite their unprecedented levels of performance, such models lack transparency and explainability and thus have seen limited downstream applications as humans generally are apprehensive of techniques that are not inherently interpretable and trustworthy. While several studies have demonstrated the superhuman performance of AI models, none has directly compared the performance of such models and human readers in the domain of poverty studies. In the present study, we directly compare the performance of human readers and a DL model using different resolutions of satellite imagery to estimate the welfare levels of demographic and health survey clusters in Tanzania, using the wealth quintile ratings from the same survey as the ground truth data. The cluster-level imagery covers all 608 cluster locations, of which 428 were classified as rural. The imagery for the human readers was sourced from the Google Maps Platform at an ultra-high resolution of 0.6m per pixel at zoom level 18, while that of the machine learning model was sourced from the comparatively lower resolution Sentinel-2 10m per pixel data for the same cluster locations. Rank correlation coefficients of between 0.31 and 0.32 achieved by the human readers were much lower when compared to those attained by the machine learning model – 0.69-0.79. This superhuman performance by the model is even more significant given that it was trained on the relatively lower 10-meter resolution satellite data while the human readers estimated welfare levels from the higher 0.6m spatial resolution data from which key markers of poverty and slums – roofing and road quality – are discernible. It is important to note, however, that the human readers did not receive any training before ratings, and had this been done, their performance might have improved. The stellar performance of the model also comes with the inevitable shortfall relating to limited transparency and explainability. The findings have significant implications for attaining the objective of the current frontier of deep learning models in this domain of scholarship – eXplainable Artificial Intelligence through a collaborative rather than a comparative framework.

Keywords: poverty prediction, satellite imagery, human readers, machine learning, Tanzania

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Authors: Yuri Katz, Kun Liu, Arunram Atmacharan

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Due to illiquidity, mispricing on Credit Markets is inevitable. This creates huge challenges to banks and investors as they seek to find new ways of risk valuation and portfolio management in a post-credit crisis world. Here, we analyze the difference in behavior of the spread-to-maturity in investment and high-yield categories of US corporate bonds between 2014 and 2023. Deviation from the theoretical dependency of this measure in the universe under study allows to identify multiple cases of mispriced credit risk. Remarkably, we observe mispriced bonds in both categories of credit ratings. This identification is supported by the application of the state-of-the-art machine learning model in more than 90% of cases. Noticeably, the ML-driven model-based forecasting of a category of bond’s credit ratings demonstrate an excellent out-of-sample accuracy (AUC = 98%). We believe that these results can augment conventional valuations of credit portfolios.

Keywords: credit risk, credit ratings, bond pricing, spread-to-maturity, machine learning

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Authors: Shieh Shiow-fang

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Superstitious thoughts or actions often occur during athletic competitions. Often "superstitious rituals" have a positive impact on the performance of competitive athletes. Athletes affirm the many psychological benefits of religious beliefs mostly in a positive way. Method: By snowball sampling, we recruited 10 experienced competitive athletes as participants. We used in-person and online one-to-one in-depth interview to collect their experiences about sport superstition. The total interview time was 795 minutes. We analyzed the raw data with the grounded theory processes suggested by Strauss and Corbin (1990). Results: The factors affecting athlete performance are ritual beliefs, taboo awareness, learning norms, and spontaneous attribution behaviors. Conclusion: We concluded that sports superstition reflects several psychological implications. The analysis results of this paper can provide another research perspective for the future study of sports superstition behavior.

Keywords: superstition, taboo awareness, learning norms, competitive athlete

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Authors: Edgar Mauricio Ruiz Osuna, Claudia Yaneth Herrera Bolivar, Sandra Liliana Gomez Vasquez

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The increasing needs of professionals with skills in software development in industry are incremental, therefore, the relevance of an educational process in line with the strengthening of these competencies, are part of the responsibilities of universities with careers related to the area of Informatics and Systems. In this sense, it is important to consider that in the National Science, Technology and Innovation Plan for the development of the Electronics, Information Technologies and Communications (2013) sectors, it is established as a weakness in the SWOT Analysis of the Software sector and Services, Deficiencies in training and professional training. Accordingly, UNIMINUTO's Computer Technology Program has addressed the analysis of students' performance in software development, identifying various problems such as dropout in programming subjects, academic averages, as well as deficiencies in strategies and competencies developed in the area of programming. As a result of this analysis, it was determined to design a collaborative learning platform in basic programming using heat maps as a tool to support didactic feedback. The pilot phase allows to evaluate in a programming course the ALGORINFO platform as a didactic resource, through an interactive and collaborative environment where students can develop basic programming practices and in turn, are fed back through the analysis of time patterns and difficulties frequent in certain segments or program cycles, by means of heat maps. The result allows the teacher to have tools to reinforce and advise critical points generated on the map, so that students and graduates improve their skills as software developers.

Keywords: collaborative platform, learning, feedback, programming, heat maps

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Authors: Vivienne Cameron

Abstract:

Background: Between 2010 and 2017, teachers in a suburban public school district struggled to get students to consistently produce adequate writing samples as measured by the Pennsylvania state writing rubric for measuring focus, content, organization, style, and conventions. A common thread in all of the data was the need to develop stamina in the student writers. Method: All of the teachers used the traditional writing process model (prewrite, draft, revise, edit, final copy) during writing instruction. One teacher taught the writing process using word processing and incentivizing with publication instead of the traditional pencil/paper/grading method. Students did not have instruction in typing/keyboarding. The teacher submitted resulting student work to real-life contests, magazines, and publishers. Results: Students in the test group increased both the quantity and quality of their writing over a seven month period as measured by the Pennsylvania state writing rubric. Reluctant writers, as well as students with autism spectrum disorder, benefited from this approach. This outcome was repeated consistently over a five-year period. Interpretation: Removing the burden of pencil and paper allowed students to participate in the writing process more fully. Writing with pencil and paper is physically tiring. Students are discouraged when they submit a draft and are instructed to use the Add, Remove, Move, Substitute (ARMS) method to revise their papers. Each successive version becomes shorter. Allowing students to type their papers frees them to quickly and easily make changes. The result is longer writing pieces in shorter time frames, allowing the teacher to spend more time working on individual needs. With this additional time, the teacher can concentrate on teaching focus, content, organization, style, conventions, and audience. S/he also has a larger body of works from which to work on whole group instruction such as developing effective leads. The teacher submitted the resulting student work to contests, magazines, and publishers. Although time-consuming, the submission process was an invaluable lesson for teaching about audience and tone. All students in the test sample had work accepted for publication. Students became highly motivated to succeed when their work was accepted for publication. This motivation applied to special needs students, regular education students, and gifted students.

Keywords: elementary-age students, reluctant writers, teaching strategies, writing process

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Authors: Valentin Père, Mathieu Milhé, Fabien Baillon, Jean-Louis Dirion

Abstract:

Microgrids offer potential solutions to meet the need for local grid stability and increase isolated networks autonomy with the integration of intermittent renewable energy production and storage facilities. In such a context, sizing production and storage for a given network is a complex task, highly depending on input data such as power load profile and renewable resource availability. This work aims at developing an operating cost computation methodology for different microgrid designs based on the use of deep reinforcement learning (RL) algorithms to tackle the optimal operation problem in stochastic environments. RL is a data-based sequential decision control method based on Markov decision processes that enable the consideration of random variables for control at a chosen time scale. Agents trained via RL constitute a promising class of Energy Management Systems (EMS) for the operation of microgrids with energy storage. Microgrid sizing (or design) is generally performed by minimizing investment costs and operational costs arising from the EMS behavior. The latter might include economic aspects (power purchase, facilities aging), social aspects (load curtailment), and ecological aspects (carbon emissions). Sizing variables are related to major constraints on the optimal operation of the network by the EMS. In this work, an islanded mode microgrid is considered. Renewable generation is done with photovoltaic panels; an electrochemical battery ensures short-term electricity storage. The controllable unit is a hydrogen tank that is used as a long-term storage unit. The proposed approach focus on the transfer of agent learning for the near-optimal operating cost approximation with deep RL for each microgrid size. Like most data-based algorithms, the training step in RL leads to important computer time. The objective of this work is thus to study the potential of Batch-Constrained Q-learning (BCQ) for the optimal sizing of microgrids and especially to reduce the computation time of operating cost estimation in several microgrid configurations. BCQ is an off-line RL algorithm that is known to be data efficient and can learn better policies than on-line RL algorithms on the same buffer. The general idea is to use the learned policy of agents trained in similar environments to constitute a buffer. The latter is used to train BCQ, and thus the agent learning can be performed without update during interaction sampling. A comparison between online RL and the presented method is performed based on the score by environment and on the computation time.

Keywords: batch-constrained reinforcement learning, control, design, optimal

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Authors: Carole A. South-Winter

Abstract:

Research-based learning gives students the opportunity to experience problems that require critical thinking and idea development. The skills they gain in working through these problems 'hands-on,' develop into attributes that benefit their careers in the professional field. The partnerships developed between students and industries give advantages to both sides. The students gain knowledge and skills that will increase their likelihood of success in the future and the industries are given research on new advancements that will give them a competitive advantage in their given field of work. The future of these partnerships is dependent on the success of current programs, enabling the enhancement and improvement of the research efforts. Once more students can complete research, there will be an increase in reliability of the results for each industry. The overall goal is to continue the support for research-based learning and the partnerships formed between students and industries.

Keywords: global healthcare, industry partnerships, research-driven decisions, short-term study abroad

Procedia PDF Downloads 112

Authors: Ruth Green, Samantha Milton, Rinal Desai

Abstract:

Background/Aims: Eye pain/swelling/redness is a common presentation to Barnet General Hospital (a district general hospital), pediatric emergency department, and is managed by both the pediatric and emergency teams. The management of each child differs dramatically depending on the healthcare professional who reviews them. There also appears to be confusion in diagnosis between periorbital cellulitis, pre-septal cellulitis, and orbital cellulitis. Pre septal cellulitis refers to an inflammation of the eyelids and soft tissue anterior to the orbital septum. In contrast, orbital cellulitis is a serious, rapidly progressive infection of soft tissues located posterior to the orbital septum. Pre-septal cellulitis is more prevalent and less serious than orbital cellulitis, although it may be part of a continuous spectrum if untreated. Pre-septal cellulitis should there be diagnosed and treated urgently to prevent spread to the septum. For the purpose of the audit, the term periorbital cellulitis has been used as an umbrella term for all spectrums of this infection. The audit aimed to look at, how as a whole, the department is diagnosing and managing orbital and pre-septal cellulitis. Gold Standard: Patients of the same age and diagnosis should be treated with the same medication, advice, and follow-up. Method: Data was collected retrospectively from pediatric patients ( < 18years) who attended the emergency department from June 2019 to February 2020 who had been coded as pre-septal cellulitis, periorbital cellulitis, orbital cellulitis, or eye pain/swelling/redness. Demographics, signs and symptoms, management, and follow-up were recorded for all patients with any of the diagnoses of pre-septal, periorbital, or orbital cellulitis. A Microsoft Excel spreadsheet was used to record the anonymised data. Results: There were vast discrepancies in the diagnosis, management, and follow-up of patients with periorbital cellulitis. Conclusion/Discussion: The audit concluded there is no uniform approach to managing periorbital cellulitis in Barnet General Hospital Paediatric Emergency Department. Healthcare professionals misdiagnosed conjunctivitis as periorbital cellulitis, and adequate steps did not appear to be documented on excluding red flag signs and symptoms of patients presenting. There was no consistency in follow-up, with some patients having timely phone reviews or clinical reviews for mild symptoms. Advice given by the staff was appropriate, and patients did return when symptoms got worse and were treated accordingly. Plan: Given the inconsistency, a gold standard care pathway or local easily accessible clinical guideline can be developed to help with the diagnosis and management of periorbital cellulitis. Along with this, a teaching session can be carried out for the staff of the pediatric team and emergency department to disseminate the teaching. Following the introduction of a guideline and teaching sessions, patients notes can be re-reviewed to check improvement in patient care.

Keywords: periorbital cellulitis, preseptal cellulitis, orbital cellulitis, erythematous eyelid

Procedia PDF Downloads 113

Authors: Ambali Alade Odebowale, Andargachew Mekonnen Berhe, Haroldo T. Hattori, Andrey E. Miroshnichenko

Abstract:

Solar thermophotovoltaic systems (STPV) have emerged as a promising solution to overcome the Shockley-Queisser limit, a significant impediment in the direct conversion of solar radiation into electricity using conventional solar cells. The STPV system comprises essential components such as an optical concentrator, selective emitter, and a thermophotovoltaic (TPV) cell. The pivotal element in achieving high efficiency in an STPV system lies in the design of a spectrally selective emitter or absorber. Traditional methods for designing and optimizing selective emitters are often time-consuming and may not yield highly selective emitters, posing a challenge to the overall system performance. In recent years, the application of machine learning techniques in various scientific disciplines has demonstrated significant advantages. This paper proposes a novel nanostructure composed of four-layered materials (SiC/W/SiO2/W) to function as a selective emitter in the energy conversion process of an STPV system. Unlike conventional approaches widely adopted by researchers, this study employs a machine learning-based approach for the design and optimization of the selective emitter. Specifically, a random forest algorithm (RFA) is employed for the design of the selective emitter, while the optimization process is executed using genetic algorithms. This innovative methodology holds promise in addressing the challenges posed by traditional methods, offering a more efficient and streamlined approach to selective emitter design. The utilization of a machine learning approach brings several advantages to the design and optimization of a selective emitter within the STPV system. Machine learning algorithms, such as the random forest algorithm, have the capability to analyze complex datasets and identify intricate patterns that may not be apparent through traditional methods. This allows for a more comprehensive exploration of the design space, potentially leading to highly efficient emitter configurations. Moreover, the application of genetic algorithms in the optimization process enhances the adaptability and efficiency of the overall system. Genetic algorithms mimic the principles of natural selection, enabling the exploration of a diverse range of emitter configurations and facilitating the identification of optimal solutions. This not only accelerates the design and optimization process but also increases the likelihood of discovering configurations that exhibit superior performance compared to traditional methods. In conclusion, the integration of machine learning techniques in the design and optimization of a selective emitter for solar thermophotovoltaic systems represents a groundbreaking approach. This innovative methodology not only addresses the limitations of traditional methods but also holds the potential to significantly improve the overall performance of STPV systems, paving the way for enhanced solar energy conversion efficiency.

Keywords: emitter, genetic algorithm, radiation, random forest, thermophotovoltaic

Procedia PDF Downloads 39