Search results for: learning management systems

Authors: Loreta Kaklauskiene, Arturas Kaklauskas

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The Integrated Human Resources and Work Environment Management (HOWE) System optimises employee productivity, improves the work environment, and, at the same time, meets the employer’s strategic goals. The HOWE system has been designed to ensure an organisation can successfully compete in the global market, thanks to the high performance of its employees. The HOWE system focuses on raising workforce productivity and improving work conditions to boost employee performance and motivation. The methods used in our research are linear correlation, INVAR multiple criteria analysis, digital twin, and affective computing. The HOWE system is based on two patents issued in Lithuania (LT 6866, LT 6841) and one European Patent application (No: EP 4 020 134 A1). Our research analyses ways to make human resource management more efficient and boost labour productivity by improving and adapting a personalised work environment. The efficiency of human capital and labour productivity can be increased by applying personalised workplace improvement systems that can optimise lighting colours and intensity, scents, data, information, knowledge, activities, media, games, videos, music, air pollution, humidity, temperature, vibrations, and other workplace aspects. HOWE generates and maintains a personalised workspace for an employee, taking into account the person’s affective, physiological and emotional (APSE) states. The purpose of this project was to create a HOWE for the customisation of quality control in smart workspaces taking into account the user’s APSE states in an integrated manner as a single unit. This customised management of quality control covers the levels of lighting and colour intensities, scents, media, information, activities, learning materials, games, music, videos, temperature, energy efficiency, the carbon footprint of a workspace, humidity, air pollution, vibrations and other aspects of smart spaces. The system is based on Digital Twins technology, seen as a logical extension of BIM.

Keywords: human resource management, health economics, work environment, organizational behaviour and employee productivity, prosperity in work, smart system

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Authors: Lynette Nickleberry Stewart

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The present presentation seeks to explore distinction across disciplines in the appropriateness of accelerated courses and suggestions for implementing accelerated courses in various disciplines. Grounded in a review of research on accelerated learning (AL), this presentation will discuss the intradisciplinary appropriateness of accelerated courses for various topics and student types, and make suggestions for implementing augmented courses. Meant to inform an emerging ‘handbook’ of accelerated course development, facilitators will lead participants in a discussion of personal challenges and triumphs in their attempts at accelerated course design.

Keywords: adult learning, abbreviated courses, accelerated learning, course design

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Authors: Bahman Gorjian

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This study investigated the effects of closed-captioning on vocabulary learning and listening comprehension of English-language movies. Captioning is thus an effective language-learning tool for persons learning English as a second language. Because students may learn a foreign language "passively," utilizing subtitles on television could make learning English enjoyable for them. Closed captioning is an electrical technique that converts spoken words from a television program's audio into written text that mimics subtitles in another language. The findings of this study showed the importance of using closed-captioning software when learning a foreign language. As a result, these must be considered when teaching EFL/ESL. The influence of watching movies with closed captions on vocabulary and hearing is compared in this study. This goal can be reached by employing a closed-captioned movie as a teaching tool in the classroom. This research was critical because it demonstrates the advantages of closed-captioning programs in EFL classrooms for both teachers and students. The study's findings assisted teachers in better understanding how to employ closed captioning as a teaching tool in the classroom. The effects will be seen as even more significant for language learners who use the method.

Keywords: closed-captions, listening, comprehension, vcabulary

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Authors: K. C. Wong

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This paper demonstrates a number of examples in the hope to shed some light on the possibility of designing future operating systems in a more adaptation-based manner. A modern operating system, we conceive, should possess the capability of 'learning' in such a way that it can dynamically adjust its services and behavior according to the current status of the environment in which it operates. In other words, a modern operating system should play a more proactive role during the session of providing system services to users. As such, a modern operating system is expected to create a computing environment, in which its users are provided with system services more matching their dynamically changing needs. The examples demonstrated in this paper show that user's resource access patterns 'learned' and determined during a session can be utilized to improve system performance and hence to provide users with a better and more effective computing environment. The paper also discusses how to use the frequency, the continuity, and the duration of resource accesses in a session to quantitatively measure and determine user's resource access patterns for the examples shown in the paper.

Keywords: adaptation-based systems, operating systems, resource access patterns, system performance

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Authors: Nadjet Brahmia

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The growing needs of displacements led advanced countries in this field install new specific transport systems, able to palliate any deficiencies, especially when regular public transport does not adequately meet the requests of users. In this context, on-demand transportation (ODT) are very efficient; they rely on techniques based on the location of trip generators which should be assured effectively with the use of operators responsible of the advance reservation, planning and organization, and studying the different ODT criteria (organizational, technical, geographical, etc.). As the advanced countries in the field of transport, some developing countries are involved in the adaptation of the new technologies to reduce the deficit in their communication system. This communication presents the study of an ODT implementation in the west of Algeria, by developing the Geomatics side of the study. This part requires the use of specific systems (such as GIS, RDBMS…), so we developed the process through an application in an environment of mobility by using the computer tools dedicated to the management of the entities related to the transport field.

Keywords: ODT, geomatics, GIS, transport systems

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Authors: A. D. Tayal

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The energy industry is undergoing significant disruption. This research outlines that, whilst challenging; this disruption is also an emerging opportunity for electricity utilities. One such opportunity is leveraging the developments in data analytics and machine learning. As the uptake of renewable energy technologies and complimentary control systems increases, electricity grids will likely transform towards dense microgrids with high penetration of renewable generation sources, rich in network and customer data, and linked through intelligent, wireless communications. Data digitisation and analytics have already impacted numerous industries, and its influence on the energy sector is growing, as computational capabilities increase to manage big data, and as machines develop algorithms to solve the energy challenges of the future. The objective of this paper is to address how far the uptake of renewable technologies can go given the constraints of existing grid infrastructure and provides a qualitative assessment of how higher levels of renewable energy penetration can be facilitated by incorporating even broader technological advances in the fields of data analytics and machine learning. Western Australia is used as a contextualised case study, given its abundance and diverse renewable resources (solar, wind, biomass, and wave) and isolated networks, making a high penetration of renewables a feasible target for policy makers over coming decades.

Keywords: data, innovation, renewable, solar

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Authors: Tomas Premoli, Sareh Rowlands

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In this study, we investigate the impact of various convolutional neural network (CNN) architectures on the accuracy of diagnosing Alzheimer’s disease (AD) using patient MRI scans. Alzheimer’s disease is a debilitating neurodegenerative disorder that affects millions worldwide. Early, accurate, and non-invasive diagnostic methods are required for providing optimal care and symptom management. Deep learning techniques, particularly CNNs, have shown great promise in enhancing this diagnostic process. We aim to contribute to the ongoing research in this field by comparing the effectiveness of different CNN architectures and providing insights for future studies. Our methodology involved preprocessing MRI data, implementing multiple CNN architectures, and evaluating the performance of each model. We employed intensity normalization, linear registration, and skull stripping for our preprocessing. The selected architectures included VGG, ResNet, and DenseNet models, all implemented using the Keras library. We employed transfer learning and trained models from scratch to compare their effectiveness. Our findings demonstrated significant differences in performance among the tested architectures, with DenseNet201 achieving the highest accuracy of 86.4%. Transfer learning proved to be helpful in improving model performance. We also identified potential areas for future research, such as experimenting with other architectures, optimizing hyperparameters, and employing fine-tuning strategies. By providing a comprehensive analysis of the selected CNN architectures, we offer a solid foundation for future research in Alzheimer’s disease diagnosis using deep learning techniques. Our study highlights the potential of CNNs as a valuable diagnostic tool and emphasizes the importance of ongoing research to develop more accurate and effective models.

Keywords: Alzheimer’s disease, convolutional neural networks, deep learning, medical imaging, MRI

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Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

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Authors: Muhammmad Riandhy Anindika Yudhy, Harry Patria, Ramadhani Santoso

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Loss of containment is the primary hazard that process safety management is concerned within the oil and gas industry. Escalation to more serious consequences all begins with the loss of containment, starting with oil and gas release from leakage or spillage from primary containment resulting in pool fire, jet fire and even explosion when reacted with various ignition sources in the operations. Therefore, the heart of process safety management is avoiding loss of containment and mitigating its impact through the implementation of safeguards. The most effective safeguard for the case is an early detection system to alert Operations to take action prior to a potential case of loss of containment. The detection system value increases when applied to a long surface pipeline that is naturally difficult to monitor at all times and is exposed to multiple causes of loss of containment, from natural corrosion to illegal tapping. Based on prior researches and studies, detecting loss of containment accurately in the surface pipeline is difficult. The trade-off between cost-effectiveness and high accuracy has been the main issue when selecting the traditional detection method. The current best-performing method, Real-Time Transient Model (RTTM), requires analysis of closely positioned pressure, flow and temperature (PVT) points in the pipeline to be accurate. Having multiple adjacent PVT sensors along the pipeline is expensive, hence generally not a viable alternative from an economic standpoint.A conceptual approach to combine mathematical modeling using computational fluid dynamics and a supervised machine learning model has shown promising results to predict leakage in the pipeline. Mathematical modeling is used to generate simulation data where this data is used to train the leak detection and localization models. Mathematical models and simulation software have also been shown to provide comparable results with experimental data with very high levels of accuracy. While the supervised machine learning model requires a large training dataset for the development of accurate models, mathematical modeling has been shown to be able to generate the required datasets to justify the application of data analytics for the development of model-based leak detection systems for petroleum pipelines. This paper presents a review of key leak detection strategies for oil and gas pipelines, with a specific focus on crude oil applications, and presents the opportunities for the use of data analytics tools and mathematical modeling for the development of robust real-time leak detection and localization system for surface pipelines. A case study is also presented.

Keywords: pipeline, leakage, detection, AI

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Authors: Kari Björn

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Introduction of games has expanded the application area of computer-aided learning tools to wide variety of age groups of learners. Serious games engage the learners into a real-world -type of simulation and potentially enrich the learning experience. Institutional background of a Bachelor’s level engineering program in Information and Communication Technology is introduced, with detailed focus on one of its majors, Health Technology. As part of a Customer Oriented Software Application thematic semester, one particular course of “eHealth Business and Solutions” is described and reflected in a gamified framework. Learning a consistent view into vast literature of business management, strategies, marketing and finance in a very limited time enforces selection of topics relevant to the industry. Health Technology is a novel and growing industry with a growing sector in consumer wearable devices and homecare applications. The business sector is attracting new entrepreneurs and impatient investor funds. From engineering education point of view the sector is driven by miniaturizing electronics, sensors and wireless applications. However, the market is highly consumer-driven and usability, safety and data integrity requirements are extremely high. When the same technology is used in analysis or treatment of patients, very strict regulatory measures are enforced. The paper introduces a course structure using gamification as a tool to learn the most essential in a new market: customer value proposition design, followed by a market entry game. Students analyze the existing market size and pricing structure of eHealth web-service market and enter the market as a steering group of their company, competing against the legacy players and with each other. The market is growing but has its rules of demand and supply balance. New products can be developed with an R&D-investment, and targeted to market with unique quality- and price-combinations. Product cost structure can be improved by investing to enhanced production capacity. Investments can be funded optionally by foreign capital. Students make management decisions and face the dynamics of the market competition in form of income statement and balance sheet after each decision cycle. The focus of the learning outcome is to understand customer value creation to be the source of cash flow. The benefit of gamification is to enrich the learning experience on structure and meaning of financial statements. The paper describes the gamification approach and discusses outcomes after two course implementations. Along the case description of learning challenges, some unexpected misconceptions are noted. Improvements of the game or the semi-gamified teaching pedagogy are discussed. The case description serves as an additional support to new game coordinator, as well as helps to improve the method. Overall, the gamified approach has helped to engage engineering student to business studies in an energizing way.

Keywords: engineering education, integrated curriculum, learning experience, learning outcomes

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Authors: Ivan Traina, Luigi Sangalli, Fabio Tognon, Angelo Lascioli

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Currently, challenges regarding preschooler children are mainly focused on a sedentary lifestyle. Also, motor activity in infancy is seen as a tool for the separate acquisition of cognitive and socio-emotional skills rather than considering neuromotor development as a tool for improving learning abilities. The paper utilized an observational research method to shed light on the results of practicing neuromotor exercises in preschool children with disability as well as provide implications for practice.

Keywords: children with disability, learning abilities, inclusion, neuromotor development

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Authors: Shashi Pingolia

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The paper discusses best practices developed and followed by 07 'Maharatna' Central Public sector Enterprises in India. The paper begins with brief analyses of the contribution of ‘Maharatna’ companies in the growth story of India Inc. Progressively; it enlists Human Resource practices and approach of these 'Maharatna' companies in the areas such as Recruitment, Pay structure, Employee Benefits and Development, Rewards and Recognition practices, Performance Management Systems, etc. In the later part of the paper, HR factors that led some of these 'Maharatna' companies from average employers to 'Best Place at Work' are discussed in brief.

Keywords: central public sector enterprises in India, Maharatna companies in India, human resource management, best place to work

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Authors: Uttam Neupane, Narendra Budha, Subash Kumar Bhattarai

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Material management is an essential part in construction project management. There are a number of material management problems in the Nepalese construction industry, which contribute to an inefficient material management system. Ineffective material management can cause waste of time and money thus increasing the problem of time and cost overrun. An assessment of material management system with gap and solution was carried out on 20 construction projects implemented by the Federal Level Project Implementation Unit (FPIU); Kaski district of Nepal. To improve the material management process, the respondents have provided possible solutions to overcome the gaps seen in the current material management process. The possible solutions are preparation of material schedule in line with the construction schedule for material requirement planning, verifications of material and locating of source, purchasing of the required material in advance before commencement of work, classifying the materials, and managing the inventory based on their usage value and eliminating and reduction in wastages during the overall material management process.

Keywords: material management, construction site, inventory, construction project

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Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

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Authors: Nela Milosevic, Sladjana Barjaktarovic Rakocevic, Sladjana Benkovic, Nemanja Milanovic

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Nowadays, performance measurement systems play a key role in evaluating the strategic performances of an organization. On the other hand, there has been a shift towards the Balanced Scorecard (BSC), which has been recognized as a valuable managerial approach. The main goal of this paper is to analyze the main performances of Serbian banks measured at the branches level, through the usage of the Balanced Scorecard framework. Although an extensive number of practitioners have an interest in the Balanced Scorecard approach, little empirical research has been conducted on the implementation of its concept in the service sector like banks, especially within developing countries. From the beginning of August till the end of September 2015, authors have been conducting in-depth interviews among a number of experts from the most successful banks in Serbia. The results show that the non-financial measures, especially, customer oriented indicators and product/ service oriented indicators, seem to be very important factors for improving not only the financial situation within the bank, but also overall business performances. Additionally, the findings prove that there is the cause-effect relationship between non-financial and financial dimensions of the Balanced Scorecard. Having in mind that the banks are still using outdated performance evaluation systems, such as annual, quarterly and monthly reports, we hope that this paper will contribute to the knowledge of how banks in Serbia may apply the Balanced Scorecard approach to evaluate their performance on the most efficient and effective way.

Keywords: balanced scorecard approach, bank management, performance measurement systems, strategic performances

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Authors: Fiona Jensen, Barb Goodwin, Nancy Kleiman, Rhonda Usunier

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Background: Clinical simulation is now a common component in many health profession curricula in preparation for clinical practice. In the Rady Faculty of Health Sciences (RFHS) college leads in simulation and interprofessional (IP) education, planned an eight hour simulated hospital day shift, where seventy students from six health professions across two campuses, learned with each other in a safe, realistic environment. Learning about interprofessional collaboration, an expected competency for many health professions upon graduation, was a primary focus of the simulation event. Method: Faculty representatives from the Colleges of Nursing, Medicine, Pharmacy and Rehabilitation Sciences (Physical Therapy, Occupation Therapy, Respiratory Therapy) and Pharmacy worked together to plan the IP event in a simulation facility in the College of Nursing. Each college provided a faculty mentor to guide the same profession students. Students were placed in interprofessional teams consisting of a nurse, physician, pharmacist, and then sharing respiratory, occupational, and physical therapists across the team depending on the needs of the patients. Eight patient scenarios were role played by health profession students, who had been provided with their patient’s story shortly before the event. Each team was guided by a facilitator. Results and Outcomes: On the morning of the event, all students gathered in a large group to meet mentors and facilitators and have a brief overview of the six competencies for effective collaboration and the session objectives. The students assuming their same profession roles were provided with their patient’s chart at the beginning of the shift, met with their team, and then completed professional specific assessments. Shortly into the shift, IP team rounds began, facilitated by the team facilitator. During the shift, each patient role-played a spontaneous health incident, which required collaboration between the IP team members for assessment and management. The afternoon concluded with team rounds, a collaborative management plan, and a facilitated de-brief. Conclusions: During the de-brief sessions, students responded to set questions related to the session learning objectives and expressed many positive learning moments. We believe that we have a sustainable simulation IP collaborative learning opportunity, which can be embedded into curricula, and has the capacity to grow to include more health profession faculties and students. Opportunities are being explored in the RFHS at the administrative level, to offer this event more frequently in the academic year to reach more students. In addition, a formally structured event evaluation tool would provide important feedback and inform the qualitative feedback to event organizers and the colleges about the significance of the simulation event to student learning.

Keywords: simulation, collaboration, teams, interprofessional

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Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, S. Meenakshi Sundaram

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Alzheimer’s disease is one of the prevalent kind of ailment, expected for impudent reconciliation or an effectual therapy is to be accredited hitherto. Probable detonation of patients in the upcoming years, and consequently an enormous deal of apprehension in early discovery of the disorder, this will conceivably chaperon to enhanced healing outcomes. Complex impetuosity of the brain is an observant symbolic of the disease and a unique recognition of genetic sign of the disease. Machine learning alongside deep learning and decision tree reinforces the aptitude to absorb characteristics from multi-dimensional data’s and thus simplifies automatic classification of Alzheimer’s disease. Susceptible testing was prophesied and realized in training the prospect of Alzheimer’s disease classification built on machine learning advances. It was shrewd that the decision trees trained with deep neural network fashioned the excellent results parallel to related pattern classification.

Keywords: Alzheimer's diagnosis, decision trees, deep neural network, machine learning, pattern classification

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Authors: Georges Bou Ghantous

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This literature review examines the transformative impact of Artificial Intelligence (AI) on higher education, highlighting both the potential benefits and challenges associated with its adoption. The review reveals that AI significantly enhances personalized learning by tailoring educational experiences to individual student needs, thereby boosting engagement and learning outcomes. Automated grading systems streamline assessment processes, allowing educators to focus on improving instructional quality and student interaction. AI's data-driven insights provide valuable analytics, helping educators identify trends in at-risk students and refine teaching strategies. Moreover, AI promotes enhanced instructional innovation through the adoption of advanced teaching methods and technologies, enriching the educational environment. Administrative efficiency is also improved as AI automates routine tasks, freeing up time for educators to engage in research and curriculum development. However, the review also addresses the challenges that accompany AI integration, such as data privacy concerns, algorithmic bias, dependency on technology, reduced human interaction, and ethical dilemmas. This balanced exploration underscores the need for careful consideration of both the advantages and potential hurdles in the implementation of AI in higher education.

Keywords: administrative efficiency, data-driven insights, data privacy, ethical dilemmas, higher education, personalized learning

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Authors: Geraldine O’Neill

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Work-integrated learning is a valuable learning experience for students in higher education. However, the fairness of the assessment process has been identified as a challenge. This study explored solutions to this challenge through interviews with expert authors in the field and workshops across nine different disciplines in Ireland. In keeping with the use of a participatory and action research methodology, the key stakeholders in the process, the students, educators, and practitioners, identified some solutions. The solutions included the need to: clarify the assessments’ expectations; enhance the flexibility of the competencies, reduce the number of competencies; use grading scales with lower specificity; support practitioner training, and empower students in the assessment process. The results are discussed as they relate to interactional, procedural, and distributive fairness.

Keywords: competencies, fairness, grading scales, work-integrated learning

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Authors: Erick C. Valverde, Wansu Lim

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Facial emotion recognition (FER) system has been recently developed for more advanced computer vision applications. The ability to identify human emotions would enable smart healthcare facility to diagnose mental health illnesses (e.g., depression and stress) as well as better human social interactions with smart technologies. The FER system involves two steps: 1) face detection task and 2) facial emotion recognition task. It classifies the human expression in various categories such as angry, disgust, fear, happy, sad, surprise, and neutral. This system requires intensive research to address issues with human diversity, various unique human expressions, and variety of human facial features due to age differences. These issues generally affect the ability of the FER system to detect human emotions with high accuracy. Early stage of FER systems used simple supervised classification task algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN). These conventional FER systems have issues with low accuracy due to its inefficiency to extract significant features of several human emotions. To increase the accuracy of FER systems, deep learning (DL)-based methods, like convolutional neural networks (CNN), are proposed. These methods can find more complex features in the human face by means of the deeper connections within its architectures. However, the inference speed and computational costs of a DL-based FER system is often disregarded in exchange for higher accuracy results. To cope with this drawback, an optimized DL-based FER system is proposed in this study.An extreme version of Inception V3, known as Xception model, is leveraged by applying different network optimization methods. Specifically, network pruning and quantization are used to enable lower computational costs and reduce memory usage, respectively. To support low resource requirements, a 68-landmark face detector from Dlib is used in the early step of the FER system.Furthermore, a DL compiler is utilized to incorporate advanced optimization techniques to the Xception model to improve the inference speed of the FER system. In comparison to VGG-Net and ResNet50, the proposed optimized DL-based FER system experimentally demonstrates the objectives of the network optimization methods used. As a result, the proposed approach can be used to create an efficient and real-time FER system.

Keywords: deep learning, face detection, facial emotion recognition, network optimization methods

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Authors: Ahmed T. Alahmar

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There is an increasing trend toward student-centred interactive e-learning methods and students’ feedback is a valuable tool for improving learning methods. The aim of this study was to explore the attitude of second year pharmacy students at the University of Babylon, Iraq, towards lectures, exams and e-learning. Materials and methods: Ninety pharmacy students were surveyed by paper questionnaire about their preference for lecture format, use of e-files, theoretical lectures versus practical experiments, lecture and lab time. Students were also asked about their predilection for Moodle-based online exams, different types of exam questions, exam time and other extra academic activities. Results: Students prefer to read lectures on paper (73.3%), use of PowerPoint file (76.7%), short lectures of less than 10 pages (94.5%), practical experiments (66.7%), lectures and lab time of less than two hours (89.9% and 96.6 respectively) and intra-lecture discussions (68.9%). Students also like to have paper-based exam (73.3%), short essay (40%) or MCQ (34.4%) questions and also prefer to do extra activities like reports (22.2%), seminars (18.6%) and posters (10.8%). Conclusion: Second year pharmacy students have different attitudes toward traditional and electronic leaning and assessment methods. Using multimedia, e-learning and Moodle are increasingly preferred methods among some students.

Keywords: pharmacy, students, lecture, exam, e-learning, Moodle

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Authors: Meiju Chen, Chaoyu Guo, Chia Wei Tang

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The present study's purpose is twofold: first, to construct indicators for learning-oriented school education and, second, to conduct a survey to examine how learning-oriented education has been implemented in junior high schools after the launch of the 12-year compulsory curriculum. For indicator system construction, we compiled relevant literature to develop a preliminary indicator list model and then conducted two rounds of a questionnaire survey to gain comprehensive feedback from experts to finalize our indicator model. In the survey's first round, 12 experts were invited to evaluate the indicators' appropriateness. Based on the experts' consensus, we determined our final indicator list and used it to develop the Fuzzy Delphi questionnaire to finalize the indicator system and each indicator's relative value. For the fact-finding survey, we collected 454 valid samples to examine how the concept of learning-oriented education is adopted and implemented in the junior high school context. We also used this data in our importance-performance analysis to explore the strengths and weaknesses of school education in Taiwan. The results suggest that the indicator system for learning-oriented school education must consist of seven dimensions and 34 indicators. Among the seven dimensions, 'student learning' and 'curriculum planning and implementation' are the most important yet underperforming dimensions that need immediate improvement. We anticipate that the indicator system will be a useful tool for other countries' evaluation of schools' performance in learning-oriented education.

Keywords: learning-oriented education, school education, fuzzy Delphi method, importance-performance analysis

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Authors: Samuel Quashie

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Ph.D. Construction Management methodology and mixed methods are organized to guide the researcher to assemble and assess data in the research activities. Construction management research is close to business management and social science research. It also contributes to researching the phenomenon and answering the research question, generating an integrated management system for post-disaster reconstruction in construction and related industries. Research methodology and methods drive the research to achieve the goal or goals, contribute to knowledge, or increase knowledge. This statement means the research methodology, mixed methods, aim, objectives, and processes address the research question, facilitate its achievement and foundation to conduct the study. Mixed methods use project-based case studies, interviews, observations, literature and archival document reviews, research questionnaires, and surveys, and evaluation of integrated systems used in the construction industry and related industries to address the research work. The research mixed methods (qualitative, quantitative) define the research topic and establish a more in-depth study. The research methodology is action research, which involves the collaboration of participants and service users to collect and evaluate data, studying the phenomenon, research question(s) to improve the situation in post-disaster reconstruction phase management.

Keywords: methodology, Ph.D. research, post-disaster reconstruction, mixed-methods qualitative and quantitative

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Authors: Yi-Chuan Cheng, Li-Chi Huang, Yu-Shan Chang

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Introduction: The main purpose of this study was to explore the relationship between the learning number, care knowledge, care skills and maternal confidence in preterm infant care in Taiwan. Background: Preterm infants care has been stressful for mother caring at home. Many programs have been applied for improving the infant care maternal confident. But less to know the learning behavior in mothers of preterm infant. Methods: The sample consisted of 55 mothers with preterm infants were recruited in a neonatal intermediate unit at a medical center in central Taiwan. The self-reported questionnaires including knowledge and skills of preterm infant care scales and maternal confidence scale were used to evaluation, which were conducted during hospitalization, before hospital discharge, and one month after discharge. We performed by using Pearson correlation of the collected data using SPSS 18. Results: The study showed that the learning number and knowledge in preterm infant care was a significant positive correlation (r = .40), and the skills and confidence preterm infant care was positively correlated (r = .89). Conclusions: Study results showed the mother had more learning number in preterm infant care will be stronger knowledge, and the skills and confidence in preterm infant care were also positively correlated. Thus, we found the learning behavior change significant care knowledge. And the maternal confidence change significant with skill on preterm infant’s care. But bondage still needs further study and develop the participation in hospital-based instructional programs, which could lead to greater long-term retention of learning.

Keywords: learning behavior, care knowledge, care skills, maternal confidence

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Authors: Deborah Asibu Abu

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Motivation is the process that initiates, guides, and maintains goal-oriented behaviors. It is one of the most important ingredients in teaching and learning yet it does not come by chance; it involves necessary strategies appropriate to achieve a common goal. In learning, the psychological attention of the student is very important. This helps them to imagine whatever is being taught for a simple understanding, nonetheless, many students will be able to imagine how the environment is in social studies or how the bones or plant is, in integrated Science but will find it difficult to imagine what subject-verb agreement or phrases and clauses actually looks like until they are motivated or with the use of TLM’s to stir their interest to learn and forever remember. For students to be able to receive the motivation they need, there must be an effective relationship between the teacher and the student as well as the use of strategies for effectual execution of achievable goals. Every teacher must understand the importance of motivation by applying various kinds of teaching methodology, especially in the English Language as a subject. Hence this paper suggests some important factors necessary for student’s motivation in teaching and learning English Language, it handles what teaching method is, types of motivation, educational curriculum structure of many, what suitable teaching methods can achieve, appropriate teachers’ disposition, learning environment as tool for motivation and some other domestic factors that can also influence student motivation.

Keywords: english language, teacher-student relationship, curriculum structure, learning environment

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Authors: Kyle De Freitas, Margaret Bernard

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Enabling data mining analysis among a wider audience of educators is an active area of research within the educational data mining (EDM) community. The paper proposes a framework for developing an environment that caters for educators who have little technical data mining skills as well as for more advanced users with some data mining expertise. This framework architecture was developed through the review of the strengths and weaknesses of existing models in the literature. The proposed framework provides a modular architecture for future researchers to focus on the development of specific areas within the EDM process. Finally, the paper also highlights a strategy of enabling analysis through either the use of predefined questions or a guided data mining process and highlights how the developed questions and analysis conducted can be reused and extended over time.

Keywords: educational data mining, learning management system, learning analytics, EDM framework

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Authors: Heba Abdelmotaal, Magdy Abdel-Kader

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Purpose: This study explores whether a firm’s effective political risk management is preventing real and accrual earnings management . Design/methodology/approach: Based on a sample of 130 firms operating in Egypt during the period 2008-2013, two hypotheses are tested using the panel data regression models. Findings: The empirical findings indicate a significant relation between real and accrual earnings management and political risk. Originality/value: This paper provides a statistically evidence on the effects of the political risk management failure on the mangers’ engagement in the real and accrual earnings management practices, and its impact on the firm’s performance.

Keywords: political risk, risk management failure, real activities manipulation, accrual earnings management

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Authors: Jinan Laurentia Woo

Abstract:

This paper explores the intersection of technology, music education, and social-emotional learning (SEL) with a focus on Latin America. It delves into the impact of music education on social-emotional skills development, highlighting the universal significance of music across various life stages. The integration of artificial intelligence (AI) in music education is discussed, emphasizing its potential to enhance learning experiences. The paper also examines the implementation of SEL strategies in Latin American public schools, emphasizing the importance of fostering social-emotional well-being in educational settings. Challenges such as unequal access to technology and education in the region are addressed, calling for further research and investment in tech-assisted music education.

Keywords: music education, social emotional learning, educational technology, Latin America, artificial intelligence, music

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Authors: S. Priya

Abstract:

A project based learning assignment in English for Specific Purposes (ESP) context based on Communicative English as prescribed in the university syllabus for engineering students and its learning outcome from ESP context is the focus of analysis through this paper. The task based on Project Based Learning (PBL) was conducted in the digital language lab which had audio visual aids to support the team presentation. The total strength of 48 students of Mechanical Branch were divided into 6 groups, each consisting of 8 students. The group members were selected on random numbering basis. They were given a group task to represent a power point presentation on a topic related to their core branch. They had to discuss the issue and choose their topic and represent in a given format. It provided the individual role of each member in the presentation. A brief overview of the project and the outcome of its technical aspects were also had to be included. Each group had to highlight the contributions of that innovative technology through their presentation. The power point should be provided in a CD format. The variations in the choice of subjects, their usage of digital technologies, co-ordination for competition, learning experience of first time stage presentation, challenges of team cohesiveness were some criteria observed as their learning experience. For many other students undergoing the stages of planning, preparation and practice as steps for presentation had been the learning outcomes as given through their feedback form. The evaluation pattern is distributed for individual contribution and group effectiveness which promotes quality of presentation. The evaluated skills are communication skills, group cohesiveness, and audience response, quality of technicality and usage of technical terms. This paper thus analyses how project based learning improves the communication, life skills and technical skills in English for Specific learning context through PBL.

Keywords: language lab, ESP context, communicative skills, life skills

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Authors: Chithra A. V., Somoshree Datta, Sandeep Nithyanandan

Abstract:

Sign Language Recognition (SLR) is the process of interpreting and translating sign language into spoken or written language using technological systems. It involves recognizing hand gestures, facial expressions, and body movements that makeup sign language communication. The primary goal of SLR is to facilitate communication between hearing- and speech-impaired communities and those who do not understand sign language. Due to the increased awareness and greater recognition of the rights and needs of the hearing- and speech-impaired community, sign language recognition has gained significant importance over the past 10 years. Technological advancements in the fields of Artificial Intelligence and Machine Learning have made it more practical and feasible to create accurate SLR systems. This paper presents a distinct approach to SLR by framing it as a video classification problem using Deep Learning (DL), whereby a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has been used. This research targets the integration of sign language recognition into healthcare settings, aiming to improve communication between medical professionals and patients with hearing impairments. The spatial features from each video frame are extracted using a CNN, which captures essential elements such as hand shapes, movements, and facial expressions. These features are then fed into an RNN network that learns the temporal dependencies and patterns inherent in sign language sequences. The INCLUDE dataset has been enhanced with more videos from the healthcare domain and the model is evaluated on the same. Our model achieves 91% accuracy, representing state-of-the-art performance in this domain. The results highlight the effectiveness of treating SLR as a video classification task with the CNN-RNN architecture. This approach not only improves recognition accuracy but also offers a scalable solution for real-time SLR applications, significantly advancing the field of accessible communication technologies.

Keywords: sign language recognition, deep learning, convolution neural network, recurrent neural network

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