Search results for: scholarship of teaching and learning (SoTL)

Authors: Sonia Arradaza-Pajaron

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English as a second language is widely spoken in the Philippines. In fact, it is used as a medium of instruction in school. However, Filipino students, in general, are still not proficient in the use of the language. Since it plays a very crucial role in the learning and comprehension of some subjects in the school where important key concepts and in English, it is imperative to look into other factors that may affect such concern. This study may post an answer to the said concern because it aimed to investigate the association between a psychological construct, known as emotional intelligence, and the English language performance of the 55 senior high school students. The study utilized a descriptive correlational method to determine the significant relationship of variables with preliminary data, like GPA in English subject as baseline information of their performance. Results revealed that the respondents had an average GPA in the English subject; however, improving from their first-year high school level to the fourth year. Their English performance resulted to an above average level with a notable higher performance in the speaking test than in the written. Further, a strong correlation between English performance and emotional intelligence was manifested. Based on the findings, it can be concluded that students with higher emotional intelligence their English language performance is expected to be the same. It can be said further that when students’ emotional intelligence (EI components) is facilitated well through various classroom activities, a better English performance would just be spontaneous among them.

Keywords: English language performance, emotional intelligence, EI components, emotional literacy, emotional quotient competence, emotional quotient outcomes, values and beliefs

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Authors: Veronica Kah Jo Wong, Yoke Mui Lim, Nurul Sakina Mokhtar Azizi

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The impact on the construction industry in Malaysia is unprecedented, as the government implemented a lockdown to restrict human movement in an effort to stop COVID-19 from spreading. Quantity surveyor (QS), as one of the key construction professionals, found that the working practices and environments for quantity surveyors today have changed due to the current pandemic. The QS profession must deal not only with changes in project issues but also with a different working environment in which most people are required to work from home and follow the standard operating procedures. Therefore, QS should be flexible, agile, and have the capability to adapt to the current working practices by strengthening their competencies. Adapting to the current and recovering environment of COVID-19 may result in the emergence of a new competence such as skill and knowledge for QS in order to maintain the quality of performance in the delivery of their professional services. Thus, this paper's objective is to investigate the changes in knowledge and skills in quantity surveyors. The data will be collected through interviews with registered professional QS to gain better insights that are specific in this industry, and the findings will be verified using the Delphi method. It is hoped that new knowledge and skill will be found from the study and will not only contribute to the betterment of the professional QS but also in guiding higher learning institutions to incorporate the new competencies into their curriculum.

Keywords: competency, COVID-19 pandemic, Malaysia, quantity surveying

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Authors: Kuixi Du, Thomas J. Lipscomb

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The proficiency in Mathematics skills is fundamental to success in the STEM disciplines. In the US, beginning college students who are placed in remedial/developmental Mathematics courses frequently struggle to achieve academic success. Therefore, Mathematics remediation in college has become an important concern, and providing Mathematics remediation is a prevalent way to help the students who may not be fully prepared for college-level courses. Programs vary, however, and the effectiveness of a particular remedial Mathematics program must be empirically demonstrated. The purpose of this study was to apply the sharp regression discontinuity (RD) technique to determine the effectiveness of the Jack Leaps Summer (JLS) Mathematic remediation program in supporting improved Mathematics learning outcomes among newly admitted Mathematics students in the South Dakota State University. The researchers studied the newly admitted Fall 2019 cohort of Mathematics majors (n=423). The results indicated that students whose pretest score was lower than the cut-off point and who were assigned to the JLS program experienced significantly higher scores on the post-test (Math 101 final score). Based on these results, there is evidence that the JLS program is effective in meeting its primary objective.

Keywords: causal inference, mathematisc remedial program evaluation, quasi-experimental research design, regression discontinuity design, cohort studies

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Authors: Zahra Hamedani

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Many wonderful technological developments in recent years has opened up the possibility of using intelligent buildings for a number of important applications, ranging from minimizing resource usage as well as increasing building efficiency to maximizing comfort, adaption to inhabitants and responsiveness to environmental changes. The concept of an intelligent building refers to the highly embedded, interactive environment within which by exploiting the use of artificial intelligence provides the ability to know its configuration, anticipate the optimum dynamic response to prevailing environmental stimuli, and actuate the appropriate physical reaction to provide comfort and efficiency. This paper contains a general identification of the intelligence paradigm and its impacts on the architecture arena, that with examining the performance of artificial intelligence, a mechanism to analyze and finally for decision-making to control the environment will be described. This mechanism would be a hierarchy of the rational agents which includes decision-making, information, communication and physical layers. This multi-agent system relies upon machine learning techniques for automated discovery, prediction and decision-making. Then, the application of this mechanism regarding adaptation and responsiveness of intelligent building will be provided in two scales of environmental and user. Finally, we review the identifications of sustainability and evaluate the potentials of intelligent building systems in the creation of sustainable architecture and environment.

Keywords: artificial intelligence, intelligent building, responsiveness, adaption, sustainability

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Authors: Toyin Mary Adewumi, Cina Mosito

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Part of enabling learners with special education needs (SEN) to succeed is providing them with adequate support. Support is all activities in a school that enhance its capacity to respond to diversity by making learning contexts and lessons accessible to all learners. The paper reports findings of support provided by teachers to learners with SEN and the pockets of good practice found in the support provided by teachers to these learners in schools in the Amathole West District, Eastern Cape. A purposeful sample, comprising eight teachers, eight principals in eight schools, including one provincial and two district education officials, was selected. Thematic analysis was used for analyzing data gathered through semi-structured interviews. The results established that despite the challenges such as lack of qualifications and training in special education needs, learners with SEN received varied support from teachers which include extra exercises, extra time, special attention during break times or after school hours and homework. The study reveals pockets of good practice in some selected primary schools particularly in the poverty-stricken locations in the Amathole West District. This paper recommends adequate training for teachers for the support of learners with SEN.

Keywords: good practice, learner, special education needs, inclusion, support

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Authors: Cheng-yu Hsieh, Bo Zhang, Ahmed Hambaba

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Stock market prediction has long been an area of interest for both expert analysts and investors, driven by its complexity and the noisy, volatile conditions it operates under. This research examines the efficacy of combining the Patch Time Series Transformer (PatchTST) with wavelet transforms, specifically focusing on Haar and Daubechies wavelets, in forecasting the adjusted closing price of the S&P 500 index for the following day. By comparing the performance of the augmented PatchTST models with traditional predictive models such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformers, this study highlights significant enhancements in prediction accuracy. The integration of the Daubechies wavelet with PatchTST notably excels, surpassing other configurations and conventional models in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE). The success of the PatchTST model paired with Daubechies wavelet is attributed to its superior capability in extracting detailed signal information and eliminating irrelevant noise, thus proving to be an effective approach for financial time series forecasting.

Keywords: deep learning, financial forecasting, stock market prediction, patch time series transformer, wavelet transform

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Authors: Bandar Alahmadi, Manohar Mareboyana, Lethia Jackson

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Today, there are many applications that are using computer vision models, such as face recognition, image classification, and object detection. The accuracy of these models is very important for the performance of these applications. One challenge that facing the computer vision models is the adversarial examples attack. In computer vision, the adversarial example is an image that is intentionally designed to cause the machine learning model to misclassify it. One of very well-known method that is used to attack the Convolution Neural Network (CNN) is Fast Gradient Sign Method (FGSM). The goal of this method is to find the perturbation that can fool the CNN using the gradient of the cost function of CNN. In this paper, we introduce a novel model that can attack Regional-Convolution Neural Network (R-CNN) that use FGSM. We first extract the regions that are detected by R-CNN, and then we resize these regions into the size of regular images. Then, we find the best perturbation of the regions that can fool CNN using FGSM. Next, we add the resulted perturbation to the attacked region to get a new region image that looks similar to the original image to human eyes. Finally, we placed the regions back to the original image and test the R-CNN with the attacked images. Our model could drop the accuracy of the R-CNN when we tested with Pascal VOC 2012 dataset.

Keywords: adversarial examples, attack, computer vision, image processing

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Authors: Jessica Lopez Espejel, Mahaman Sanoussi Yahaya Alassan, Walid Dahhane, El Hassane Ettifouri

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Pretrained transformer-based models have shown high performance in natural language generation tasks. However, a new wave of interest has surged: automatic programming language code generation. This task consists of translating natural language instructions to a source code. Despite the fact that well-known pre-trained models on language generation have achieved good performance in learning programming languages, effort is still needed in automatic code generation. In this paper, we introduce JaCoText, a model based on Transformer neural network. It aims to generate java source code from natural language text. JaCoText leverages the advantages of both natural language and code generation models. More specifically, we study some findings from state of the art and use them to (1) initialize our model from powerful pre-trained models, (2) explore additional pretraining on our java dataset, (3) lead experiments combining the unimodal and bimodal data in training, and (4) scale the input and output length during the fine-tuning of the model. Conducted experiments on CONCODE dataset show that JaCoText achieves new state-of-the-art results.

Keywords: java code generation, natural language processing, sequence-to-sequence models, transformer neural networks

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Authors: Yuhua Wang, Mu Li, Jinggen Liu

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Aim: To investigate the different effects of heroin and milk in activating the corticostriatal system that plays a critical role in reward reinforcement learning. Methods: Male SD rats were trained daily for 15 d to self-administer heroin or milk tablets in a classic runway drug self-administration model. Immunohistochemical assay was used to quantify Arc protein expression in the medial prefrontal cortex (mPFC), the nucleus accumbens (NAc), the dorsomedial striatum (DMS) and the ventrolateral striatum (VLS) in response to chronic self-administration of heroin or milk tablets. NMDA receptor antagonist MK801 (0.1 mg/kg) or dopamine D1 receptor antagonist SCH23390 (0.03 mg/kg) were intravenously injected at the same time as heroin was infused intravenously. Results: Runway training with heroin resulted in robust enhancement of Arc expression in the mPFC, the NAc and the DMS on d 1, 7, and 15, and in the VLS on d 1 and d 7. However, runway training with milk led to increased Arc expression in the mPFC, the NAc and the DMS only on d 7 and/or d 15 but not on d 1. Moreover, runway training with milk failed to induce increased Arc protein in the VLS. Both heroin-seeking behavior and Arc protein expression were blocked by MK801 or SCH23390 administration. Conclusion: The VLS is likely to be critically involved in drug-seeking behavior. The NMDA and D1 receptor-dependent Arc expression is important in drug-seeking behavior.

Keywords: arc, mesocorticolimbic system, drug rewarding behavior, NMDA receptor

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Authors: Gidado Lawal Likko

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The study examined the relationship of achievement goals, metacognitive awareness and academic success among students of colleges of education in North Western Nigeria. The study was guided by three objectives. The first two were to find out whether students’ achievement goals and metacognitive awareness correlate with their academic success. 358 students comprising 242 males (67.6%) and 116 females (32.4%) were studied. Correlation survey was employed in the conduct of the study. The instruments used to collect data were students’ bio data form, achievement goals inventory (Roedel, Schraw and Plake, 1994), metacognitive awareness inventory (Schraw & Dennison, 1994) and students’ CGPA (NCCE minimum standard, 2013) was used as the index of academic success. Pearson Product Moment and regression analysis were the statistical techniques used to analyze the data. Results of the analysis indicated that students’ achievement goals (r=0.554, p=0.004) and metacognitive awareness (r= 0.67, p=0.001) positively correlated with their academic success. Similarly, significant relationship exists between achievement goals and metacognitive awareness (r=0.77, p=0.000). Part of the recommendations is the need for the management of all colleges of education to have educational interventions aimed at developing students’ metacognitive awareness which will foster purposeful self-regulation of their learning. This could be achieved by periodic assessment of students’ metacognitive awareness which will serve as feedback as they move from one educational level to another.

Keywords: academic success, goal orientation, metacognitive awareness, prospective teachers

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Authors: Roly B. Bayo-Ang

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This research investigated the effect of science intervention modules in Filipino and in English on mastery of content (MOC) and conceptual understanding of Grade 9 students in Chemistry. Analysis of content mastery scores revealed a significant difference in the mean scores of the control and experimental group, t (46) = -2.14, p < 0.05. The experimental group achieved an MPS of > 75% in three of the five lessons while none in the control group. Analysis of the pretest and posttest scores of the control group in the test for conceptual understanding (TCU) showed no significant difference, t (18), =1.44, p > 0.05, while pretest and posttest scores of experimental group revealed significant difference, t (29) = -5.08, p < 0.05. Comparison of posttest scores of control and experimental group revealed no significant difference t (42) =1.67, p > 0.05. Performance in TCU and MOC of the control group are not significantly correlated, r (17) =.307, p > 0.05; but significantly correlated, r (27) =.571, p < 0.05, for the experimental group. The intervention module in Filipino promotes conceptual understanding and mastery of content than the module in English.

Keywords: action research, conceptual understanding, mastery of content, Filipino module

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Authors: Szu-Yin Chu

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Self-determination has been particularly influential in obtaining a better quality of life through successful transition processes for students with disabilities. The development of self-determination through learning has raised attention at an early age. This study used a survey questionnaire to construct the understanding of the self-determination in Taiwan, learn the perspectives about the environmental and situational contexts where the respondents expect children to display self-determination skills in different cultures. Specifically, the research questions are: (a) What are Taiwanese families’ general perspectives about the development of foundations of self-determination for young children with special needs? and (b) how does families’ demographic background (i.e., income level, educational background) and child characteristics (i.e., age, emotional or behavior problems) impact Taiwanese families’ perspectives on the foundations of self-determination across three critical components (i.e., choice-making and problem-solving, self-regulation, and engagement) for young children with special needs? Data from 125 participants were gathered and analyzed. The findings suggested that Taiwanese families showed very positive attitudes toward promoting a foundation of self-determination for young children with special needs. Families’ income level and child’s severity of emotional/behavioral problems were two variables that were found to impact families’ views on their child’s foundational self-determination skills. Implications for future research and practice in supporting families to promote foundations of self-determination for young children with special needs will be provided.

Keywords: disabilities, self-determination, Taiwan, young children

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Authors: Rungrot Trongsakul

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This paper was aimed to study the model of education cooperation during Thailand and neighbor countries, especially the countries which the territory-cohesion border with Thailand used “Soft Power” to enhance the good relationship. This research employed qualitative method, analyzed and synthesized the content of cooperation projects, policies, laws, relevant theories, relevant research papers and documents and used SWOT analysis. The research findings revealed that Thailand’s education cooperation projects with neighbor countries had two characteristics: 1) education cooperation projects/programs were a part in economic cooperation projects, and 2) there were directly education cooperation projects. The suggested education cooperation model was based on the concept of “Soft Power”, thus the determination of action plans or projects as key factors of public and private organizations should be based on sincere participation among people, communities and relevant organizations of the neighbor countries. Adoption of education-cultural exchange, learning and sharing process is a key to strengthen good relationship of the countries’ cooperation. The roles of education in this included sharing and acceptance of culture and local wisdom, human resource development, knowledge management, integration and networking building could enhance relationship between agents of related organizations of Thailand and neighbors countries.

Keywords: education, soft-power, relationship, cooperation, Thailand neighboring countries

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Authors: Cosmas Nathanailides, Fotini Kakali, Kostas Karipoglou

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The filleting yield and the chemical composition of farmed sea bass (Dicentrarchus labrax); rainbow trout (Oncorynchus mykiss) and meagre (Argyrosomus regius) was investigated in farmed fish in NW Greece. The results provide an estimate of the quantity of fish required to produce one kilogram of fillet weight, an estimation which is required for the operational management of fish processing companies. Furthermore in this work, the ratio of feed input required to produce one kilogram of fish fillet (FFCR) is presented for the first time as a useful indicator of the ecological footprint of consuming farmed fish. The lowest lipid content appeared in meagre (1,7%) and the highest in trout (4,91%). The lowest fillet yield and fillet yield feed conversion ratio (FYFCR) was in meagre (FY=42,17%, FFCR=2,48), the best fillet yield (FY=53,8%) and FYFCR (2,10) was exhibited in farmed rainbow trout. This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: ARCHIMEDES III. Investing in knowledge society through the European Social Fund.

Keywords: farmed fish, flesh quality, filleting yield, lipid

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Authors: Tanmay Bisen, Aastha Shayla

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This study introduces an advanced approach to tree canopy detection in urban environments and a regional analysis of Northern and Central Kolkata that delves into the intricate relationship between urban development and environmental conservation. Leveraging high-resolution drone imagery from diverse urban green spaces in Kolkata, we fine-tuned the deep forest model to enhance its precision and accuracy. Our results, characterized by an impressive Intersection over Union (IoU) score of 0.90 and a mean average precision (mAP) of 0.87, underscore the model's robustness in detecting and classifying tree crowns amidst the complexities of aerial imagery. This research not only emphasizes the importance of model customization for specific datasets but also highlights the potential of drone-based remote sensing in urban forestry studies. The study investigates the spatial distribution, density, and environmental impact of trees in Northern and Central Kolkata. The findings underscore the significance of urban green spaces in met-ropolitan cities, emphasizing the need for sustainable urban planning that integrates green infrastructure for ecological balance and human well-being.

Keywords: urban greenery, advanced spatial distribution analysis, drone imagery, deep learning, tree detection

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Authors: Abdullah Bal, Sevdenur Bal

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This paper purposes a new type of hardware application for training of cellular neural networks (CNN) using optical joint transform correlation (JTC) architecture for image feature extraction. CNNs require much more computation during the training stage compare to test process. Since optoelectronic hardware applications offer possibility of parallel high speed processing capability for 2D data processing applications, CNN training algorithm can be realized using Fourier optics technique. JTC employs lens and CCD cameras with laser beam that realize 2D matrix multiplication and summation in the light speed. Therefore, in the each iteration of training, JTC carries more computation burden inherently and the rest of mathematical computation realized digitally. The bipolar data is encoded by phase and summation of correlation operations is realized using multi-object input joint images. Overlapping properties of JTC are then utilized for summation of two cross-correlations which provide less computation possibility for training stage. Phase-only JTC does not require data rearrangement, electronic pre-calculation and strict system alignment. The proposed system can be incorporated simultaneously with various optical image processing or optical pattern recognition techniques just in the same optical system.

Keywords: CNN training, image processing, joint transform correlation, optoelectronic hardware

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Authors: Madhavi

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All linguistic units are context-dependent. They occur in particular settings, from which they derive much of their import, and are recognized by speakers as distinct entities only through a process of abstraction. Most of the words have several concepts associated with them and convey a number of meanings in different contexts in any language. For instance, there are different uses of the word good as an adjective from English. The adjective good expresses many senses like (1) ‘high quality of someone or something’ (2) ‘efficient’ (3) ‘virtuous’ (4) ‘reliable’ etc. These senses will be analyzed by using cognitive semantics framework. The context has the power to insulate one meaning from all the other meanings in communication. This paper will provide a cognitive semantic analysis. The basic tenet of cognitive semantics is the sense of a word is the way we conceptualize it. Our conceptualization is based on the physical experience we go through. Cognitive semantics tries to capture this conceptualization in terms of some categories like schema, frame, and domain. Cognitive semantics is a subfield of cognitive linguistics. Cognitive linguistics studies the language creation, learning, and usage by the reference to human cognition. The semantic structure is conceptual structure which is related to the concepts which are the elements of reason and constitute the meanings of words and linguistic expressions. Cognitive semantics studies how our mind works for the meaning of any word and how it perceives meaning from the environment through senses and works to map with the knowledge which already exists in our mind through experience. In the present paper, the senses are further classified into some categories.

Keywords: cognitive, contexts, semantics, senses

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Authors: Yao-Hong Tsai

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Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: unmanned aerial vehicle, object tracking, deep learning, collision avoidance

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Authors: Mu-Yen Chen, Min-Hsuan Fan, Chia-Chen Chen, Siang-Yu Jhong

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In recent years, real estate prediction or valuation has been a topic of discussion in many developed countries. Improper hype created by investors leads to fluctuating prices of real estate, affecting many consumers to purchase their own homes. Therefore, scholars from various countries have conducted research in real estate valuation and prediction. With the back-propagation neural network that has been popular in recent years and the orthogonal array in the Taguchi method, this study aimed to find the optimal parameter combination at different levels of orthogonal array after the system presented different parameter combinations, so that the artificial neural network obtained the most accurate results. The experimental results also demonstrated that the method presented in the study had a better result than traditional machine learning. Finally, it also showed that the model proposed in this study had the optimal predictive effect, and could significantly reduce the cost of time in simulation operation. The best predictive results could be found with a fewer number of experiments more efficiently. Thus users could predict a real estate transaction price that is not far from the current actual prices.

Keywords: artificial neural network, Taguchi method, real estate valuation model, investors

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Authors: Lukman Omobola Adisa

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Spelling is rule bound in a written discourse. It, however, calls into question, when such convention is grossly contravened in a formal setting revered as citadel of learning, despite availability of computer spell-checker, human knowledge, and lexicon. The foregoing reveals the extent of decadence pervading education sector in Nigeria. It is on this premise that this study reviews the effect of spelling on communicative competence of the University of Ibadan Registry Staff. The theoretical framework basically evaluates diverse scholars’ views on communicative competence and how spelling influences the intended meaning of a word/ sentence as a result of undue infringement on grammatical (spelling) rule. Newsletter, bulletin, memo, and letter are four print materials purposively selected while the methodology adopted is content analysis. Similarly, five categories, though not limited to, through which spelling blunders are committed are considered: effect of spelling (omission, addition, and substitution); sound ( homophone); transposition (heading/body: content) and ambiguity (capitalisation, space, and acronym). Subsequently, the analyses, findings, and recommendations are equally looked into. Summarily, the study x-rays effective role(s) plays by spelling in enhancing communicative competence through appropriate usage of linguistic registers.

Keywords: communicative competence, content analysis, effect of spelling, linguistics registers

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Authors: Amna Khaliq

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This research project centers on Canadian business leaders’ phenomenological online education expansion by navigating the challenges faced by strategic leaders concerning the expansion of online education in the Canadian higher education sector from a business perspective. The study identifies the problems and opportunities of faculty members’ transition from traditional face-to-face to online instruction, particularly in the context of technology-enhanced learning (TEL), and their influence on the growth strategies of Canadian educational institutions. It explores strategic leaders’ approaches and the impact of emerging technologies to assist with developing and executing business strategies to expand online education in Canada. As online education has gained prominence in the country, this research addresses a relevant business problem for educational institutions. The research employs a phenomenological approach in the qualitative research design to conduct this investigation. The study interviews eighteen faculty members engaged in online education in Canada. The interview data is analyzed to answer the three research questions for strategic leaders to expand online education with higher education institutions in Canada. The recommendations include 1) data privacy, infrastructure, security, and technology, 2) support and training for student engagement, 3) accessibility and inclusion, and 4) collaboration among institutions associated with expanding online education.

Keywords: strategic leadership, Canada, education, technology

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Authors: Muhammad Hamza

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This research is based on the effect of self-questioning strategy on reading comprehension of second language learners at medium level. This research is conducted to find out the effects of self-questioning strategy and how self-questioning strategy helps English learners to improve their reading comprehension. In this research study the researcher has analyzed that how much self-questioning is effective in the field of learning second language and how much it helps second language learners to improve their reading comprehension. For this purpose, the researcher has studied different reading strategies, analyzed, collected data from certificate level class at NUML, Peshawar campus and then found out the effects of self-questioning strategy on reading comprehension of ESL learners. The researcher has randomly selected the participants from certificate class. The data was analyzed through pre-test and post-test and then in the final stage the results of both tests were compared. After the pre-test and post-test, the result of both pre-test and post-test indicated that if the learners start to use self-questioning strategy before reading a text, while reading a text and after reading a particular text there’ll be improvement in comprehension level of ESL learners. The present research has addressed the benefits of self-questioning strategy by taking two tests (pre and post-test).After the result of post-test it is revealed that the use of the self-questioning strategy has a significant effect on the readers’ comprehension thus, they can improve their reading comprehension by using self-questioning strategy.

Keywords: strategy, self-questioning, comprehension, intermediate level ESL learner

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Authors: Dolapo Adeyemo

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Technology can be designed specifically for geriatrics and persons with disabilities or ICT accessibility solutions. Both solutions stand to benefit from advances in Artificial intelligence, which are computer systems that perform tasks that require human intelligence. Tasks such as decision making, visual perception, speech recognition, and even language translation are useful in both situation and will provide significant benefits to people with temporarily or permanent disabilities. This research’s goal is to review innovations focused on the use of artificial intelligence that bridges the accessibility gap in technology from a user-centered perspective. A mixed method approach that utilized a comprehensive review of academic literature on the subject combined with semi structure interviews of users, developers, and technology product owners. The internet of things and artificial intelligence technology is creating new opportunities in the assistive technology space and proving accessibility to existing technology. Device now more adaptable to the needs of the user by learning the behavior of users as they interact with the internet. Accessibility to devices have witnessed significant enhancements that continue to benefit people with disabilities. Examples of other advances identified are prosthetic limbs like robotic arms supported by artificial intelligence, route planning software for the visually impaired, and decision support tools for people with disabilities and even clinicians that provide care.

Keywords: ICT, IOT, accessibility solutions, universal design

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Authors: T. L. Granzyk, S. Scarborough, J. DeCosmo

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The use of health-related or medical narratives has gained increasing anecdotal and research-based support as a successful device for changing health behavior and outcomes. These narratives, in the form of oral storytelling, short films, and educational documentaries, for example, are most effective when including empathetic characters that transport viewers into the story and command both their attention and emotional response. This case study outlines how and why one large health system created a short narrative film for their internal Sepsis Awareness campaign, which told the dramatic story of a patient recovering from a missed sepsis diagnosis, leaving her a quad-amputee. Results include positive global anecdotal response to the film from healthcare professionals and patients, as well as use of the film to support legislation, ultimately passed in favor of the formation of Sepsis Awareness Workgroups in Maryland. Authors conclude that narrative films can be used successfully to initiate healthcare legislation and to increase internal and external awareness of health-related areas in need of greater improvement and support. As such, healthcare leaders and stakeholders would benefit from learning how to intentionally create, cultivate, and curate narratives from within their own health systems that elicit an empathetic response.

Keywords: healthcare policy, healthcare narratives, sepsis awareness, short films

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Authors: Laila Alkhayat

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In this study, the researcher explored the positive and negative effects of children watching YouTube on changing social values from the perspective of parents in Kuwait. This study also explored whether any correlation exists between changed values from watching YouTube and the following variables: relationship with a child, social situation, school level, gender, and age. The researcher collected data from 286 questionnaires distributed randomly to parents in Kuwait. The results of the study show that parents face many disadvantages when dealing with children watching YouTube, such as children spending too much time in front of screens, inability to organize bedtime, and children’s social isolation. However, the researcher found some positives come from watching YouTube, such as learning new information, enabling children to search for new information, and introducing children to the culture of their society and other cultures around them. Moreover, this study found that boys are more likely to have negative viewing habits than girls. Given the results, this study shows that the biggest impact on social values from children watching YouTube is that they are preoccupied with watching YouTube and they waste time, which makes them feel disturbed, and this affects the value of time management and delays children’s sleeping times. This study concludes that watching YouTube simultaneously has negative and positive effects on changing social values, but it plays a negative role in changing social values of children from the parents’ perspective.

Keywords: YouTube, children, social value, social media effects

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Authors: Badru Hasan Segujja, Nassiwa Shamim

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This study “Territorial Influence of Religious Based Armed Conflicts in Africa” was in place to identify the influence of religious based armed conflicts, their parsistance and their impact on African societies. The study employed a qualitative research methodology, as data from respondents was descriptively recorded using random sampling technics. The study discovered that, the world is experiencing religious based armed violence where actors fight under the umbrella of freedom fighters where the African continent in particular has been at the pic of such armed violence almost since each countries independence to date. Because of this situation, the Continent is torn apart as families are traumatized by the memories of their dear ones who never survived in yesterdays’ faith based armed violence. The study disvovered that, some of these faith based armed conflicts are caused by factors ranging from undemocratic practices due to poor governance, poverty, Unemployment, religious extremism and radicalism which later turn into intractable violence. Religious armed groups such as, Holly Spirit Movement (HSM), Allied Democratic Forces (ADF) and Lords Resistance Army (LRA) in Uganda and now Eastern DRC and Central African Republic, ALSHABAB in East Africa, SELEKE and ANTI BALAKA in Central African Republic, BOKO HARAM in Nigeria, JANJAWEED in Sudan and Republic of Chad, Sudaneess Peoples Liberation Army (SPLA) in Southern Sudan, Alqaida Mission in Islamic Magreeb (AQIIM) in Mali coupled with acute racism of Hutu and Tutsi in Rwanda or Burundi and Xenophobic Nationalism in (South Africa). The study futher discovered that, the component of “freedom fighters” has strongly made these groups maintain the ground without fear of any repucation, which situation has resulted into children and women becoming disproportionally victims and the response of international communities to the violence is inadequate. The study concludes that, dialogue for peace is better than going for wars. The study recommends that, in order to restore peace on the African continent and elsewhere in the world, UN should recommend the teaching of peace values in schools, pre-conflict early warnings must be well attended, actors must refrain from using religious lebles, democracy, unemployment and poverty issues should as well be addressed to avoid unnessesary conflicts.

Keywords: influence, religious, armed, conflicts

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Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

Abstract:

Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

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Authors: Jurica Hižak

Abstract:

Getting to know the agents, i.e., identifying the free riders in a population, can be considered one of the main challenges in establishing cooperation. An ordinary memory-one agent such as Tit-for-tat may learn “who is who” in the population through direct interactions. Past experiences serve them as a landmark to know with whom to cooperate and against whom to retaliate in the next encounter. However, this kind of learning is risky and expensive. A cheaper and less painful way to detect free riders may be achieved by gossiping. For this reason, as part of this research, a special type of Tit-for-tat agent was designed – a “Gossip-Tit-for-tat” agent that can share data with other agents of its kind. The performances of both strategies, ordinary Tit-for-tat and Gossip-Tit-for-tat, against Always-defect have been compared in the finite-game framework of the Iterated Prisoner’s Dilemma via the Moran process. Agents were able to move in a random-walk fashion, and they were programmed to play Prisoner’s Dilemma each time they met. Moreover, at each step, one randomly selected individual was eliminated, and one individual was reproduced in accordance with the Moran process of selection. In this way, the size of the population always remained the same. Agents were selected for reproduction via the roulette wheel rule, i.e., proportionally to the relative fitness of the strategy. The absorption probability was calculated after the population had been absorbed completely by cooperators, which means that all the states have been occupied and all of the transition probabilities have been determined. It was shown that gossip increases absorption probabilities and therefore enhances the evolution of cooperation in the population.

Keywords: cooperation, gossip, indirect reciprocity, Moran process, prisoner’s dilemma, tit-for-tat

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Authors: Elvin Karaaslan Klose

Abstract:

In the field of Visual Culture, Art Education students are given the opportunity to discuss topics of interest that are closer to their own social life and media consumption habits. In contrast to the established corpus of literature and sources about Art History, educators are challenged to find topics and examples from Popular Culture and Contemporary Art that provide familiarity, depth and inspiration for students’ future practice, both as educators as well as artists. In order to establish a welcoming and fruitful discussion environment at the beginning of an introductory Visual Culture Education course with fourth year Art Education students, the class watched and subsequently discussed the movie “Vantage Point”. Using the descriptive method and content analysis; video recordings, discussion transcripts and learning diaries were summarized to highlight students’ critical points of view towards commonly experienced but rarely reflected on topics of Popular and Visual Culture. As an introduction into more theory-based forms of discussion, watching and intensely discussing a movie has proven useful by proving a combination of a familiar media type with an unfamiliar educational context. Resulting areas of interest have served as a starting point for later research, discussion and artistic production in the scope of an introductory Visual Culture Education course.

Keywords: visual culture, critical pedagogy, media literacy, art education

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Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

Abstract:

The physics-informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on a strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary conditions to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of the Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful in studying various optical phenomena.

Keywords: deep learning, optical soliton, physics informed neural network, partial differential equation

Procedia PDF Downloads 59