Search results for: impacting student learning outcomes

Authors: Rima Al-Awadhi, Abdul Jaleel Tharayil

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On a regular basis, KOC engages into various types of Projects. However, due to very nature and complexity involved, each project experience generates a lot of ‘learnings’ that need to be factored into while drafting a new contract and thus avoid repeating the same mistakes. But, many a time these learnings are localized and remain as tacit leading to scope re-work, larger cycle time, schedule overrun, adjustment orders and claims. Also, these experiences are not readily available to new employees leading to steep learning curve and longer time to competency. This is to share our experience in designing and implementing a process driven architecture for the ‘lessons learnt’ knowledge sharing framework in KOC. It high-lights the ‘lessons learnt’ sharing process adopted, integration with the organizational processes, governance framework, the challenges faced and learning from our experience in implementing a ‘lessons learnt’ framework.

Keywords: lessons learnt, knowledge transfer, knowledge sharing, successful practices, Lessons Learnt Workshop, governance framework

Procedia PDF Downloads 563

Authors: Anna Sanczyk

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Teacher agency plays a crucial role in effective teaching, supporting diverse students, and providing an enriching learning environment; therefore, it is significant to gain a deeper understanding of language teachers’ sense of agency in teaching linguistically and culturally diverse students. This paper presents an overview of qualitative research on how language teachers exercise their agency in diverse classrooms. The analysis of the literature reveals that language teachers strive for addressing students’ needs and challenging educational inequalities, but experience educational constraints in enacting their agency. The examination of the research on language teacher agency identifies four major areas where language teachers experience challenges in enacting their agency: (1) implementing curriculum; (2) adopting school reforms and policies; (3) engaging in professional learning; (4) and negotiating various identities as professionals. The practical contribution of this literature review is that it provides a much-needed compilation of the studies on how language teachers exercise agency amid educational constraints. The discussion of the overview points to the importance of teacher identity, learner advocacy, and continuous professional learning and the critical need of promoting empowerment, activism, and transformation in language teacher education. The findings of the overview indicate that language teacher education programs should prepare teachers to be active advocates for English language learners and guide teachers to become more conscious of complexities of teaching in constrained educational settings so that they can become agentic professionals. This literature overview illustrates agency work in English language teaching contexts and contributes to understanding of the important link between experiencing educational constraints and development of teacher agency.

Keywords: advocacy, educational constraints, language teacher agency, language teacher education

Procedia PDF Downloads 162

Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

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The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

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Authors: Harun Bozna

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MOOC (massive open online course) is a groundbreaking education platform and a current buzzword in higher education. Although MOOCs offer many appreciated learning experiences to learners from various universities and institutions, they have considerably higher dropout rates than traditional education. Only about 10% of the learners who enroll in MOOCs actually complete the course. In this case, perceptions of participants and a comprehensive analysis of MOOCs have become an essential part of the research in this area. This study aims to explore the MOOCs in detail for better understanding its content, purpose and primarily drop out issues. The researcher conducted an online questionnaire to get perceptions of graduate students on their learning experiences in MOOCs and arranged a semi- structured oral interview with some participants. The participants are Turkish graduate level students doing their MA and Ph.D. in various programs. The findings show that participants are more likely to drop out courses due to lack of time and lack of pressure.

Keywords: distance education, MOOCs, drop out, perception of graduate students

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Authors: Min Chen, Rubi Gupta

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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

Procedia PDF Downloads 135

Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

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Authors: Paul Nadasdy

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In 2020, oral communication classes at many universities in Japan switched to online and hybrid lessons because of the coronavirus pandemic. Teachers had to adapt their practices immediately and deal with the challenges of the online environment. Even for experienced teachers, this still presented a problem as many had not conducted online classes before. Simultaneously, for many students, this type of learning was completely alien to them, and they had to adapt to the challenges faced by communicating in English online. This study collected data from 418 first grade students in the first semester of English communication classes at a technical university in Tokyo, Japan. Zoom software was used throughout the learning period. Though there were many challenges in the setting up and implementation of Zoom classes at the university, the results indicated that the students enjoyed the format and made the most of the circumstances. This proved the robustness of the course that was taught in regular lessons and the adaptability of teachers and students to challenges in a very short timeframe.

Keywords: zoom, hybrid lessons, communicative english, online teaching

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Authors: Kwizera Mireille, Kharesh Ahmed Al-Khadher

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This paper explores the critical issue of girls' and women's right to education, exploring the challenges they face in accessing and benefiting from quality education. Gender disparities in education have persisted globally, hindering social progress and sustainable development. The fundamental importance of education in empowering individuals and promoting gender equality is acknowledged, making it imperative to address the disparities that hinder girls' and women's educational opportunities. The paper discusses various factors contributing to these disparities, including cultural norms(common in third-world countries), socio-economic constraints, and systemic biases. Drawing on a wide range of scholarly sources, empirical studies, and reports from international organizations, this paper highlights the broader societal benefits of educating girls and women, ranging from improved health outcomes to enhanced economic development and greater social and political participation. The paper further outlines strategies and initiatives aimed at overcoming these challenges. These include policy interventions, community-based programs, and international collaborations that work towards eliminating gender-based discrimination in educational settings. The paper emphasizes the significance of not only ensuring access but also fostering an inclusive and safe learning environment that encourages girls and women to thrive academically and personally. By analyzing successful case studies and best practices from around the world, the paper offers insights into effective approaches that can be adopted to enhance girls' and women's right to education globally. Furthermore, it emphasizes the importance of raising awareness of girl's and women's education. In conclusion, this paper underscores the urgency of prioritizing and protecting the educational rights of girls and women's right to education as a fundamental human right and catalyst for gender equality. It calls for a concerted effort from governments, NGOs, educational institutions, and society as a whole to create an equitable and empowering educational landscape that contributes to gender equality and sustainable development.

Keywords: empowerment, gender equality, inclusive education, right to education

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Authors: Akella Amarendra Babu, Rama Devi Yellasiri, Natukula Sainath

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Where human beings can easily learn and adopt pronunciation variations, machines need training before put into use. Also humans keep minimum vocabulary and their pronunciation variations are stored in front-end of their memory for ready reference, while machines keep the entire pronunciation dictionary for ready reference. Supervised methods are used for preparation of pronunciation dictionaries which take large amounts of manual effort, cost, time and are not suitable for real time use. This paper presents an unsupervised adaptation model for building agile and dynamic pronunciation dictionaries online. These methods mimic human approach in learning the new pronunciations in real time. A new algorithm for measuring sound distances called Dynamic Phone Warping is presented and tested. Performance of the system is measured using an adaptation model and the precision metrics is found to be better than 86 percent.

Keywords: pronunciation variations, dynamic programming, machine learning, natural language processing

Procedia PDF Downloads 159

Authors: Ahmad Rouhani, Masood Jabbari, Sima Honarmand

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This paper introduces a method to optimal design of a hybrid Wind/Photovoltaic/Fuel cell generation system for a typical domestic load that is not located near the electricity grid. In this configuration the combination of a battery, an electrolyser, and a hydrogen storage tank are used as the energy storage system. The aim of this design is minimization of overall cost of generation scheme over 20 years of operation. The Matlab/Simulink is applied for choosing the appropriate structure and the optimization of system sizing. A teaching learning based optimization is used to optimize the cost function. An overall power management strategy is designed for the proposed system to manage power flows among the different energy sources and the storage unit in the system. The results have been analyzed in terms of technics and economics. The simulation results indicate that the proposed hybrid system would be a feasible solution for stand-alone applications at remote locations.

Keywords: hybrid energy system, optimum sizing, power management, TLBO

Procedia PDF Downloads 555

Authors: Amaraporn Puraya, Roongtiva Boonpracom, Somsak Thojampa, Sirikanok Klankhajhon, Kittisak Kumpeera

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The objective of this qualitative research was to study the elderly health care process by community participation. Data were collected by quality research methods, including secondary data study, observation, in-depth interviews, and focus group discussions and analyzed by content analysis, reflection and review of information. The research results pointed out that the important elderly health care process by community participation consisted of 2 parts, namely the community participation development process in elderly health care and the outcomes from the participation development process. The community participation development process consisted of 4 steps as follows: 1) Building the leadership team, an important social capital of the community, which started from searching for both formal and informal leaders by giving the opportunity for public participation and creating clear agreements defining roles, duties and responsibilities; 2) investigating the problems and the needs of the community, 3) designing the elderly health care activities under the concept of self-care potential development of the elderly through participation in community forums and meetings to exchange knowledge with common goals, plans and operation and 4) the development process of sustainable health care agreement at the local level, starting from opening communication channels to create awareness and participation in various activities at both individual and group levels as well as pushing activities/projects into the community development plan consistent with the local administration policy. The outcomes from the participation development process were as follows. 1) There was the integration of the elderly for doing the elderly health care activities/projects in the community managed by the elderly themselves. 2) The service system was changed from the passive to the proactive one, focusing on health promotion rather than treating diseases or illnesses. 3) The registered nurses / the public health officers can provide care for the elderly with chronic illnesses through the implementation of activities/projects of elderly health care so that the elderly can access the services more. 4) The local government organization became the main mechanism in driving the elderly health care process by community participation.

Keywords: elderly health care process, community participation, elderly, Thailand

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Authors: Svenja Pieritz, Jakab Pilaszanovich

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Collaborative Problem Solving (CPS) is recognized as being one of the most important skills of the 21st century with having a potential impact on education, job selection, and collaborative systems design. Therefore, CPS has been adopted in several standardized tests, including the Programme for International Student Assessment (PISA) in 2015. A significant challenge of evaluating CPS is the underlying interplay of cognitive and social skills, which requires a more holistic assessment. However, the majority of the existing tests are using a questionnaire-based assessment, which oversimplifies this interplay and undermines ecological validity. Two major difficulties were identified: Firstly, the creation of a controllable, real-time environment allowing natural behaviors and communication between at least two people. Secondly, the development of an appropriate method to collect and synthesize both cognitive and social metrics of collaboration. This paper proposes a more holistic and automated approach to the assessment of CPS. To address these two difficulties, a multiplayer problem-solving game called Sea of Light was developed: An environment allowing students to deploy a variety of measurable collaborative strategies. This controlled environment enables researchers to monitor behavior through the analysis of game actions and chat. The according solution for the statistical model is a combined approach of Natural Language Processing (NLP) and Bayesian network analysis. Social exchanges via the in-game chat are analyzed through NLP and fed into the Bayesian network along with other game actions. This Bayesian network synthesizes evidence to track and update different subdimensions of CPS. Major findings focus on the correlations between the evidences collected through in- game actions, the participants’ chat features and the CPS self- evaluation metrics. These results give an indication of which game mechanics can best describe CPS evaluation. Overall, Sea of Light gives test administrators control over different problem-solving scenarios and difficulties while keeping the student engaged. It enables a more complete assessment based on complex, socio-cognitive information on actions and communication. This tool permits further investigations of the effects of group constellations and personality in collaborative problem-solving.

Keywords: bayesian network, collaborative problem solving, game-based assessment, natural language processing

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Authors: George Kurian, Hongmei Chi

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Digital evidence plays a critical role in most legal investigations. In many cases, thumbnail databases show important information in that investigation. The probability of having digital evidence retrieved from a computer or smart device has increased, even though the previous user removed data and deleted apps on those devices. Due to the increase in digital forensics, the ability to store residual information from various thumbnail applications has improved. This paper will focus on investigating thumbnail information from Windows 10. Thumbnail images of interest in forensic investigations may be intact even when the original pictures have been deleted. It is our research goal to recover useful information from thumbnails. In this research project, we use various forensics tools to collect left thumbnail information from deleted videos or pictures. We examine and describe the various thumbnail sources in Windows and propose a methodology for thumbnail collection and analysis from laptops or desktops. A machine learning algorithm is adopted to help speed up content from thumbnail pictures.

Keywords: digital forensic, forensic tools, soundness, thumbnail, machine learning, OCR

Procedia PDF Downloads 113

Authors: Kate Duggan

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Introduction: Functional Communication Training (FCT) is an approach which aims to reduce behaviours of concern by teaching more effective ways to communicate. It requires identification of the function of the behaviour of concern, through gathering information from key stakeholders and completing observations of the individual’s behaviour including antecedents to, and consequences of the behaviour. Appropriate communicative alternatives are then identified and taught to the individual using systematic instruction techniques. Behaviours of concern demonstrated by individuals with autism spectrum conditions (ASC) frequently have a communication function. When contributing to positive behavior support plans, speech and language therapists and other professionals working with individuals with ASC need to identify alternative communicative behaviours which are equally reinforcing as the existing behaviours of concern. Successful implementation of FCT is dependent on an effective ‘response match’. The new way of communicating must be equally as effective as the behaviour previously used and require the same amount or less effort from the individual. It must also be understood by the communication partners the individual encounters and be appropriate to their communicative contexts. Method: Four case studies within a non-maintained special school environment were described and analysed. A response match framework was used to identify the effectiveness of functional communication training delivered by the student’s speech and language therapist, teacher and learning support assistants. The success of systematic instruction techniques used to develop new communicative behaviours was evaluated using the CODES framework. Findings: Functional communication training can be used as part of a positive behaviour support approach for students within this setting. All case studies reviewed demonstrated ‘response success’, in that the desired response was gained from the new communicative behaviour. Barriers to the successful embedding of new communicative behaviours were encountered. In some instances, the new communicative behaviour could not be consistently understood across all communication partners which reduced ‘response recognisability’. There was also evidence of increased physical or cognitive difficulty in employing the new communicative behaviour which reduced the ‘response effectivity’. Successful use of ‘thinning schedules of reinforcement’, taught students to tolerate a delay to reinforcement once the new communication behaviour was learned.

Keywords: augmentative and alternative communication, autism spectrum conditions, behaviours of concern, functional communication training

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Authors: Daral Maesincee, Jompol Thongpaen

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Thus far, most of the decisions made within the walls of Thai higher education (HE) institutions have primarily been supply-oriented. With the current supply-driven, itemized HE financing systems, the nation is struggling to systemically produce high-quality manpower that serves the market’s needs, often resulting in education mismatches and unemployment – particularly in science, technology, and innovation (STI)-related fields. With the COVID-19 pandemic challenges widening the education inequality (accessibility and quality) gap, HE becomes even more unobtainable for underprivileged students, permanently leaving some out of the system. Therefore, Thai HE needs a new financing system that produces the “right people” for the “right occupations” through the “right ways,” regardless of their socioeconomic backgrounds, and encourages the creation of non-degree courses to tackle these ongoing challenges. The “Demand-Side Financing for Thai Higher Education” policy aims to do so by offering a new paradigm of HE resource allocation via two main mechanisms: i) standardized formula-based unit-cost subsidizations that is specific to each study field and ii) student loan programs that respond to the “demand signals” from the labor market and the students, that are in line with the country’s priorities. Through in-dept reviews, extensive studies, and consultations with various experts, education committees, and related agencies, i) the method of demand signal analysis is identified, ii) the unit-cost of each student in the sample study fields is approximated, iii) the method of budget analysis is formulated, iv) the interagency workflows are established, and v) a supporting information database is created to suggest the number of graduates each HE institution can potentially produce, the study fields and skillsets that are needed by the labor market, the employers’ satisfaction with the graduates, and each study field’s employment rates. By responding to the needs of all stakeholders, this policy is expected to steer Thai HE toward producing more STI-related manpower in order to uplift Thai people’s quality of life and enhance the nation’s global competitiveness. This policy is currently in the process of being considered by the National Education Transformation Committee and the Higher Education Commission.

Keywords: demand-side financing, higher education resource, human capital, higher education

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Authors: Arun Prasad Jaganathan

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In today's dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper introduces an AI-enabled task assistance and management system designed to overcome the limitations of conventional approaches. By using artificial intelligence (AI) and machine learning (ML), the system intelligently interprets user instructions, analyzes tasks, and allocates resources based on real-time data and environmental factors. Additionally, geolocation tracking enables proactive identification of potential delays, ensuring timely interventions. With its transparent reporting mechanisms, the system provides stakeholders with clear insights into task progress, fostering accountability and informed decision-making. The paper presents a comprehensive overview of the system architecture, algorithm, and implementation, highlighting its potential to revolutionize task management across diverse industries.

Keywords: artificial intelligence, machine learning, task allocation, operational efficiency, resource optimization

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Authors: C. A. Bradley, D. Patsios, D. Berridge

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This paper presents evidence to better understand the risk and protective factors related to sibling conflict and the patterns of association between sibling conflict and negative adjustment outcomes by incorporating additional familial and societal factors within statistical models of risk and adjustment. It was conducted through the secondary analysis of a large representative cross-sectional dataset of children in the UK. The original study includes proxy interviews for young children and self-report interviews for adolescents. The study applies an ecological systems framework for the analyses. Hierarchical regression models assess risk and protective factors and adjustment outcomes associated with sibling conflict. Interactions reveal differential effect between contextual risk factors and the social context of influence. The general pattern of findings suggested that, although factors affecting likelihood of experiencing sibling conflict were often determined by child age, some remained consistent across childhood. These factors were often conditional on each other, reinforcing the importance of an ecological framework. Across both age-groups, sibling conflict was associated with siblings closer in age; male sibling groups; most advantaged socio-economic group; and exposure to community violence, such as witnessing violent assault or robbery. The study develops the evidence base on the influence of ethnicity and socio-economic group on sibling conflict by exploring interactions between social context. It also identifies key new areas of influence – such as family structure, disability, and community violence in exacerbating or reducing risk of conflict. The study found negative associations between sibling conflict and young children’s mental well-being and adolescents' mental well-being and anti-social behaviour, but also more context specific associations – such as sibling conflict moderating the negative impact of adversity and high risk experiences for young children such as parental violence toward the child.

Keywords: adjustment, conflict, ecological systems, family systems, risk and protective factors, sibling

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Authors: Sambit Supriya Dash, Rahul Ravi R, Vikram Ramanan, Vinayak Malhotra

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Combustion in itself is a complex phenomenon that involves the interaction and interplay of multiple phenomena, the combined effect of which gives rise to the common flame that we see and use in our daily life applications from cooking to propelling our vehicles to space. The most important thing that goes unnoticed about these flames is the effect of the various phenomena from its surrounding environment that affects its behavior and properties. These phenomena cause a variety of energy interactions that lead to various types of energy transformations which in turn affect the flame behavior. This paper focuses on experimentally investigating the effect of one such phenomenon, which is the acoustics or sound energy on diffusion flames. The subject in itself is extensively studied upon as thermo-acoustics globally, whereas the current work focuses on studying its effect on soot formation on diffusion flames. The said effect is studied in this research work by the use of a butane as fuel, fitted with a nozzle that houses 3 arrays consisting of 4 holes each that are placed equidistant to each other and the resulting flame impinged with sound from two independent and similar sound sources that are placed equidistant from the centre of the flame. The entire process is systematically video graphed using a 60 fps regular CCD and analysed for variation in flame heights and flickering frequencies where the fuel mass flow rate is maintained constant and the configuration of entrainment holes and frequency of sound are varied, whilst maintaining constant ambient atmospheric conditions. The current work establishes significant outcomes on the effect of acoustics on soot formation; it is noteworthy that soot formation is the main cause of pollution and a major cause of inefficiency of current propulsion systems. This work is one of its kinds, and its outcomes are widely applicable to commercial and domestic appliances that utilize combustion for energy generation or propulsion and help us understand them better, so that we can increase their efficiency and decrease pollution.

Keywords: thermoacoustics, entrainment, propulsion system, efficiency, pollution

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Authors: Bernardita Tornero

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Cognitive Acceleration in Mathematics Education (CAME) programmes have been used successfully for promoting the development of thinking skills in school students for the last 30 years. Given that the approach has had a tremendous impact on the thinking capabilities of participating students, this study explored the experience of using the programme with prospective primary teachers in Chile. Therefore, this study not only looked at the experience of prospective primary teachers during the CAME course as learners, but also examined how they perceived the approach from their perspective as future teachers, as well as how they could transfer the teaching strategies they observed to their future classrooms. Given the complexity of the phenomenon under study, this research used a mixed methods approach. For this reason, the impact that the CAME course had on prospective teachers’ thinking skills was not only approached by using a test that assessed the participants’ improvements in these skills, but their learning and teaching experiences were also recorded through qualitative research tools (learning journals, interviews and field notes). The main findings indicate that, at the end of the CAME course, prospective teachers not only demonstrated higher thinking levels, but also showed positive attitudinal changes towards teaching and learning in general, and towards mathematics in particular. The participants also had increased confidence in their ability to teach mathematics and to promote thinking skills in their students. In terms of the CAME methodology, prospective teachers not only found it novel and motivating, but also commented that dealing with the thinking skills topic during a university course was both unusual and very important for their professional development. This study also showed that, at the end of the CAME course, prospective teachers felt they had developed strategies that could be used in their classrooms in the future. In this context, the relevance of the study is not only that it described the impact and the positive results of the first experience of using a CAME approach with prospective teachers, but also that some of the conclusions have significant implications for the teaching of thinking skills and the training of primary school teachers.

Keywords: cognitive acceleration, formal reasoning, prospective teachers, initial teacher training

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Authors: Adishirin Mammadov

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Polymers and polymer composites play vital roles in diverse industries, including food and beverage packaging, transportation innovations, and medical advancements. However, the advancements in polymer technology bring certain risks, particularly concerning water and soil pollution due to the presence of polymers. The creation of new polymers is a critical aspect of this field. While the primary focus is on improving their physical and chemical properties, ensuring their ecological compatibility is equally important. An advanced method for developing innovative polymer types involves integrating fillers with diverse characteristics, offering advantages such as cost reduction and improved quality indicators. In the conducted research, efforts were made to enhance environmental aspects by employing waste fillers. Specifically, low-density polyethylene (LDPE) was used as the polymer, and waste from cocoon factories was chosen as the filler. Following a process of cleaning, drying, and crushing the filler to specific dimensions, it was incorporated into polyethylene through a mechanical-chemical method under laboratory conditions. The varied rheological properties of the resulting polyethylene compositions examined at temperatures ranging from 145 to 165 degrees Celsius. These compositions demonstrated different rheological properties at various temperature intervals. Achieving homogeneity in the obtained compositions is crucial in the polymers mechanochemical process. Beyond rheological properties, swelling rates in different environments and percentages of mass loss at different temperatures learned using the differential thermal analysis method. The research revealed that, to a certain extent, the physico-chemical properties of polyethylene were not significantly affected by the polymer compositions. This suggests that incorporating cocoon waste enables cost reduction in composite production while positively impacting the environment.

Keywords: polyethylene, polymer, composites, filler, reology

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Authors: Shakila Singh

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Dominant discourses constitute heterosexuality as natural, normal and the only legitimate sexuality, and diverse sexual subjectivities as abnormal, unnatural and socially taboo. Similarly, the cisgender subject is reified. There are ongoing debates about the inclusion and suitability of sexuality education in the school curriculum and research show that teachers are not adequately prepared to teach about such issues in the classroom. Not surprising then, that many young people enter these institutions having had limited previous exposure to, or education about, sex, gender and sexuality diversity. This paper discusses the presence of heterosexism and cissexism at multiple layers in higher education institutions, impacting students and staff. Increasing knowledge and awareness of sex, gender and sexuality diversities is also crucial to challenging existing perceptions of sex, gender and sexuality diversities that marginalise and subordinate a large proportion of students and staff. There is a persistent disjuncture between dominant discourses that generally position higher education institutions as socially progressive, open environments and the discourses that legitimate the ascendency of heterosexual and cisgender identities. This paper argues that such disjuncture must be addressed by providing inclusive physical and emotional spaces if universities are to affirm every individual and produce graduates across all disciplines with the cultural capability to engage with increasingly diverse communities. Given the key role of language in shaping cultural and social attitudes, using gender-inclusive language is a powerful way to promote gender equality and eradicate gender bias. This means speaking and writing in a way that does not discriminate against a particular sex, gender or sexual identity and does not perpetuate gender stereotypes. Individuals must be allowed to present themselves and identify in ways they choose and be addressed by their chosen pronouns.

Keywords: heteronormativity, inclusivity, gender, universities

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Authors: Mohammed A. M. Ibrahim

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This paper investigate the utilization of wire and wireless networks to be platform for distributed educational monitoring system. Universities in developing countries suffer from a lot of shortages(staff, equipment, and finical budget) and optimal utilization of the wire and wireless network, so universities can mitigate some of the mentioned problems and avoid the problems that maybe humble the education processes in many universities by using our implementation of the examinations system as a test-bed to utilize the network as a solution to the shortages for academic staff in Taiz University. This paper selects a two areas first one quizzes activities is only a test bed application for wireless network learning environment system to be distributed among students. Second area is the features and the security of wireless, our tested application implemented in a promising area which is the use of WLAN in higher education for leering environment.

Keywords: networking wire and wireless technology, wireless network security, distributed computing, algorithm, encryption and decryption

Procedia PDF Downloads 325

Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Peter Williams

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Mobile digital technology, in the form of smart phones, tablets, laptops and their accompanying functionality/apps etc., is becoming ever more used by people with Learning Disabilities (LD) - for entertainment, to communicate and socialize, and enjoy self-expression. Despite this, there has been very little research into the experiences of such technology by this cohort, it’s role in articulating personal identity and self-advocacy and the barriers encountered in negotiating technology in everyday life. The proposed talk describes research funded by the British Academy addressing these issues. It aims to explore: i) the experiences of people with LD in using mobile technology in their everyday lives – the benefits, in terms of entertainment, self-expression and socialising, and possible greater autonomy; and the barriers, such as accessibility or usability issues, privacy or vulnerability concerns etc. ii) how the technology, and in particular the software/apps and interfaces, can be improved to enable the greater access to entertainment, information, communication and other benefits it can offer. It is also hoped that results will inform parents, carers and other supporters regarding how they can use the technology with their charges. Rather than the project simply following the standard research procedure of gathering and analysing ‘data’ to which individual ‘research subjects’ have no access, people with Learning Disabilities (and their supporters) will help co-produce an accessible, annotated and hyperlinked living e-archive of their experiences. Involving people with LD as informants, contributors and, in effect, co-researchers will facilitate digital inclusion and empowerment. The project is working with approximately 80 adults of all ages who have ‘mild’ learning disabilities (people who are able to read basic texts and write simple sentences). A variety of methods is being used. Small groups of participants have engaged in simple discussions or storytelling about some aspect of technology (such as ‘when my phone saved me’ or ‘my digital photos’ etc.). Some individuals have been ‘interviewed’ at a PC, laptop or with a mobile device etc., and asked to demonstrate their usage and interests. Social media users have shown their Facebook pages, Pinterest uploads or other material – giving them an additional focus they have used to discuss their ‘digital’ lives. During these sessions, participants have recorded (or employed the researcher to record) their observations on to the e-archive. Parents, carers and other supporters are also being interviewed to explore their experiences of using mobile technology with the cohort, including any difficulties they have observed their charges having. The archive is supplemented with these observations. The presentation will outline the methods described above, highlighting some of the special considerations required when working inclusively with people with LD. It will describe some of the preliminary findings and demonstrate the e-archive with a commentary on the pages shown.

Keywords: inclusive research, learning disabilities, methods, technology

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Authors: William Gallagher, Harriet Bothwell, Lowri Evans, Kevin Jones

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Background: Worldwide, a third of adult deaths are caused by cardiovascular disease, a high proportion occurring in the developing world. Contributing to these poor outcomes are suboptimal assessments, treatments and monitoring of the acutely unwell patient. Successful training in trauma and neonates is recognised in the developing world but there is little literature supporting adult resuscitation. As far as the authors are aware no literature has been published on resuscitation training in Uganda since 2000 when a resuscitation training officer ran sessions in neonatal and paediatric resuscitation. The aim of this project was to offer training in Basic Life Support ( BLS) to staff and healthcare students based at Villa Maria Hospital in the Kalungu District, Central Uganda. This project was undertaken as a student selected component (SSC) offered by Swindon Academy, based at the Great Western Hospital, to medical students in their fourth year of the undergraduate programme. Methods: Semi-structured, informal interviews and focus groups were conducted with different clinicians in the hospital. These interviews were designed to focus on the level of training and understanding of BLS. A training session was devised which focused on BLS (excluding the use of an automatic external defribrillator) involving pre and post-training questionnaires and clinical assessments. Three training sessions were run for different cohorts: a pilot session for 5 Ugandan medical students, a second session for a group of 8 nursing and midwifery students and finally, a third was devised for physicians. The data collected was analysed in excel. Paired T-Tests determined statistical significance between pre and post-test scores and confidence before and after the sessions. Average clinical skill assessment scores were converted to percentages based on the area of BLS being assessed. Results: 27 participants were included in the analysis. 14 received ‘small group training’ whilst 13 received’ large group training’ 88% of all participants had received some form of resuscitation training. Of these, 46% had received theory training, 27% practical training and only 15% received both. 12% had received no training. On average, all participants demonstrated a significant increase of 5.3 in self-assessed confidence (p <0.05). On average, all participants thought the session was very useful. Analysis of qualitative date from clinician interviews in ongoing but identified themes identified include rescue breaths being considered the most important aspect resuscitation and doubts of a ‘good’ outcome from resuscitation. Conclusions: The results of this small study reflect the need for regular formal training in BLS in low resource settings. The active engagement and positive opinions concerning the utility of the training are promising as well as the evidence of improvement in knowledge.

Keywords: basic life support, education, resuscitation, sub-Saharan Africa, training, Uganda

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Authors: Yun-Husan Huang

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Many English for Specific Purposes (ESP) textbooks are distributed globally as the content development is often obliged to compromises between commercial and pedagogical demands. Therefore, the issue of regional application and usefulness of globally published ESP textbooks has received much debate. For ESP instructors, textbook selection is definitely a priority consideration for curriculum design. An appropriate ESP textbook can facilitate teaching and learning, while an inappropriate one may cause a disaster for both teachers and students. This study aims to investigate the regional application and usefulness of an ESP textbook for information technology (IT). Participants were 51 sophomores majoring in Applied Informatics and Multimedia at a university in Taiwan. As they were non-English majors, their English proficiency was mostly at elementary and elementary-to-intermediate levels. This course was offered for two semesters. The textbook selected was Oxford English for Information Technology. At class end, the students were required to complete a survey comprising five choices of Very Easy, Easy, Neutral, Difficult, and Very Difficult for each item. Based on the content design of the textbook, the survey investigated how the students viewed the difficulty of grammar, listening, speaking, reading, and writing materials of the textbook. In terms of difficulty, results reveal that only 22% of them found the grammar section difficult and very difficult. For listening, 71% responded difficult and very difficult. For general reading, 55% responded difficult and very difficult. For speaking, 56% responded difficult and very difficult. For writing, 78% responded difficult and very difficult. For advanced reading, 90% reported difficult and very difficult. These results indicate that, except the grammar section, more than half of the students found the textbook contents difficult in terms of listening, speaking, reading, and writing materials. Such contradictory results between the easy grammar section and the difficult four language skills sections imply that the textbook designers do not well understand the English learning background of regional ESP learners. For the participants, the learning contents of the grammar section were the general grammar level of junior high school, while the learning contents of the four language skills sections were more of the levels of college English majors. Implications from the findings are obtained for instructors and textbook designers. First of all, existing ESP textbooks for IT are few and thus textbook selections for instructors are insufficient. Second, existing globally published textbooks for IT cannot be applied to learners of all English proficiency levels, especially the low level. With limited textbook selections, third, instructors should modify the selected textbook contents or supplement extra ESP materials to meet the proficiency level of target learners. Fourth, local ESP publishers should collaborate with local ESP instructors who understand best the learning background of their students in order to develop appropriate ESP textbooks for local learners. Even though the instructor reduced learning contents and simplified tests in curriculum design, in conclusion, the students still found difficult. This implies that in addition to the instructor’s professional experience, there is a need to understand the usefulness of the textbook from learner perspectives.

Keywords: ESP textbooks, ESP materials, ESP textbook design, learner perspectives on ESP textbooks

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Authors: M. D. D. Chathurangi, M. G. K. Nayanathara, K. M. H. M. M. Gunapala, G. M. R. G. Dayananda, Kavinga Yapa Abeywardena, Deemantha Siriwardana

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Due to the growing popularity of social media platforms at present, there are various concerns, mostly cyberbullying, spam, bot accounts, and the spread of incorrect information. To develop a risk score calculation system as a thorough method for deciphering and exposing unethical social media profiles, this research explores the most suitable algorithms to our best knowledge in detecting the mentioned concerns. Various multiple models, such as Naïve Bayes, CNN, KNN, Stochastic Gradient Descent, Gradient Boosting Classifier, etc., were examined, and the best results were taken into the development of the risk score system. For cyberbullying, the Logistic Regression algorithm achieved an accuracy of 84.9%, while the spam-detecting MLP model gained 98.02% accuracy. The bot accounts identifying the Random Forest algorithm obtained 91.06% accuracy, and 84% accuracy was acquired for fake news detection using SVM.

Keywords: cyberbullying, spam behavior, bot accounts, fake news, machine learning

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Authors: Anum Obaid, Bushra Noor, Zoshia Zainab

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In South Asian countries, Pakistan faces significant public health challenges regarding maternal and neonatal health (MNH). Despite global efforts to improve maternal, newborn, child, and health (MNCH) outcomes through initiatives like the Millennium Development Goals (MDGs) and Sustainable Development Goals (SDGs), high maternal and neonatal mortality rates persist. In patriarchal societies, cultural norms, family dynamics, and gender roles heavily influence healthcare accessibility and decision-making processes, often leading to delayed and inadequate maternal care. Addressing these socio-cultural barriers and enhancing healthcare resources is crucial to improving maternal health outcomes in areas like Faisalabad. A qualitative study was conducted involving two groups of informants: gynecologists practicing in private clinics and first-time pregnant women receiving care in government hospitals. Data collection included obtaining institutional permission, conducting semi-structured in-depth interviews, and using non-probability sampling techniques. A proactive strategy to overcome maternal health challenges involves using aversion therapy and disseminating knowledge among family members. This approach aims to foster a deep understanding within the family unit regarding the importance of maternal well-being, thereby creating a supportive environment and facilitating informed decision-making related to healthcare access and lifestyle choices. The findings indicate that maternal health is compromised both physiologically and psychologically, with significant implications for the baby's health. Mental well-being, in particular, is profoundly affected, largely due to familial behavior and entrenched cultural taboos.

Keywords: maternal health, neonatal health, socio-cultural norms, primigravida women, gynecologist, familial conduct, cultural taboos

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Authors: Anum Obaid, Bushra Noor, Zoshia Zainab

Abstract:

In South Asian countries, Pakistan faces significant public health challenges regarding maternal and neonatal health (MNH). Despite global efforts to improve maternal, newborn, child, and health (MNCH) outcomes through initiatives like the Millennium Development Goals (MDGs) and Sustainable Development Goals (SDGs), high maternal and neonatal mortality rates persist. In patriarchal societies, cultural norms, family dynamics, and gender roles heavily influence healthcare accessibility and decision-making processes, often leading to delayed and inadequate maternal care. Addressing these socio-cultural barriers and enhancing healthcare resources is crucial to improving maternal health outcomes in areas like Faisalabad. A qualitative study was conducted involving two groups of informants: gynecologists practicing in private clinics and first-time pregnant women receiving care in government hospitals. Data collection included obtaining institutional permission, conducting semi-structured in-depth interviews, and using non-probability sampling techniques. A proactive strategy to overcome maternal health challenges involves using aversion therapy and disseminating knowledge among family members. This approach aims to foster a deep understanding within the family unit regarding the importance of maternal well-being, thereby creating a supportive environment and facilitating informed decision-making related to healthcare access and lifestyle choices. The findings indicate that maternal health is compromised both physiologically and psychologically, with significant implications for the baby's health. Mental well-being, in particular, is profoundly affected, largely due to familial behavior and entrenched cultural taboos.

Keywords: maternal health, neonatal health, socio-cultural norms, primigravida women, gynecologist, familial conduct, cultural taboos.

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Authors: Bharatendra Rai

Abstract:

Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

Procedia PDF Downloads 306