Search results for: learning management systems

Authors: Pakhil Singh, Rahul Singh, Mohammad Saad Alam, Yasser Rafat

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The battery management system is an essential package/system which ensures optimum performance and safety of a battery by monitoring the key essential parameters of the battery like the voltage, current, temperature, state of charge, state of health during charging and discharging. This can be accomplished using outputs of various sensors employed to serve the purpose. The increasing demand for electricity generation from renewable energy sources requires proper storage and hence a proper monitoring system as well. A battery management system is required in wide applications ranging from renewable energy storage systems, off-grid solar PV applications to electric vehicles. The aim of this paper is to study the parameters used in monitoring various battery operating conditions and proposes the usage of the internet of things (IoT) to implement a reliable battery management system.

Keywords: electric vehicles, internet of things, sensors, state of charge, state of health

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Authors: Berlian Eka Kurnia, Mohamad Very Setiawan, Rahmat Fadhli

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Archives play an important role in organization’s continuity, especially related to the learning activities in the past. Archive management is considered accessible when the archive can be used when needed. University archive can support research activities for institutions, besides, archive management services also have to pay attention to the accessibility that became a barometer of how easy users get the data or information from an archive, use and understand it. This study identifies about the accessibility of archive services at the Universitas Gadjah Mada, with case study method. Universitas Gadjah Mada archives not only provide a service to the academicians, but also for public. Universitas Gadjah Mada archive can be traced online and offline. Online searching archives can be acceessed through an application “SIKS” and offline searching can be accessed by "finding aids" printed. Although Universitas Gadjah Mada Archives has its own procedures to access the archive directly, but they also remain guided by National Archive of Indonesia.

Keywords: archival institution, university archive, archive’s accessibility, archive management

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Authors: Wen-Chao Tang, Tang-Yi Liu, Ming Gao, Gang Xu, Hua-Yuan Yang

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This study developed a method for teaching acupuncture manipulation in small groups optimized with the theory of motor learning. Sixty acupuncture students and their teacher participated in our research. Motion videos were recorded of their manipulations using the lifting-thrusting method. These videos were analyzed using Simi Motion software to acquire the movement parameters of the thumb tip. The parameter velocity curves along Y axis was used to generate small teaching groups clustered by a self-organized map (SOM) and K-means. Ten groups were generated. All the targeted instruction based on the comparative results groups as well as the videos of teacher and student was provided to the members of each group respectively. According to the theory and research of motor learning, the factors or technologies such as video instruction, observational learning, external focus and summary feedback were integrated into this teaching method. Such efforts were desired to improve and enhance the effectiveness of current acupuncture teaching methods in limited classroom teaching time and extracurricular training.

Keywords: acupuncture, group teaching, video instruction, observational learning, external focus, summary feedback

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Authors: Siddharth Vashisth, Varun Mathur

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This review provides an overview of the impact that implementation of various Knowledge Management (KM) strategies has had on the growth of a department in a Public Sector Company in India. In a regulated utility controlled by the government, the growth of an organization such as Hindustan Petroleum Corporation Limited (HPCL) had depended largely on the efficiencies of the systems and its people. However, subsequent to the de-regularization & to the entry of the private competition, the need for a ‘systematic templating’ of knowledge was recognized. This necessitated the introduction of Knowledge Management Centre (KMC). Projects & Pipelines Department (P&P) of HPCL introduced KMC that contributed significantly towards KM by adopting various strategies such as standardization, leveraging information system, competency enhancement, and improvements & innovations. These strategies gave both tangible as well as intangible benefits towards KM. Knowledge, technology & people are the three pillars that need to be catered for effective knowledge management in any organization. In HPCL, the initiative of KMC has served as an intermediary between these three major pillars as each activity of the strategy was centered on them and contributed significantly to their growth and up-gradation, ensuring overall growth of KM in the department.

Keywords: knowledge, knowledge management, public sector organization, standardization, technology, people, skill, information system, innovation, competency, impact

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Authors: T. S. Terence Ma, Irene T. Ho

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Conducting adequate assessment of the challenges students with ASD (Autism Spectrum Disorder) face and the support they need is imperative for promoting their school adjustment. Students with ASD often show deficits in communication, social interaction, emotional regulation, and self-management in learning. While targeting these areas in intervention is often helpful, we argue that not enough attention has been paid to weak self-regulation being a key factor underlying their manifest difficulty in all these areas. Self-regulation refers to one’s ability to moderate their behavioral or affective responses without assistance from others. Especially for students with high functioning autism, who often show problems not so much in acquiring the needed skills but rather in applying those skills appropriately in everyday problem-solving, self-regulation becomes a key to successful adjustment in daily life. Therefore, a greater understanding of the construct of self-regulation, its relationship with other daily skills, and its role in school functioning for students with ASD would generate insights on how students’ school adjustment could be promoted more effectively. There were two focuses in this study. Firstly, we examined the extent to which self-regulation is a distinct construct that is differentiable from other daily skills and the most salient indicators of this construct. Then we tested a model of relationships between self-regulation and other daily school skills as well as their relative and combined effects on school adjustment. A total of 1,345 Grade1 to Grade 6 students with ASD attending mainstream schools in Hong Kong participated in the research. In the first stage of the study, teachers filled out a questionnaire consisting of 136 items assessing a wide range of student skills in social, emotional and learning areas. Results from exploratory factor analysis (EFA) with 673 participants and subsequent confirmatory factor analysis (CFA) with another group of 672 participants showed that there were five distinct factors of school skills, namely (1) communication skills, (2) pro-social behavior, (3) emotional skills, (4) learning management, and (5) self-regulation. Five scales representing these skill dimensions were generated. In the second stage of the study, a model postulating the mediating role of self-regulation for the effects of the other four types of skills on school adjustment was tested with structural equation modeling (SEM). School adjustment was defined in terms of the extent to which the student is accepted well in school, with high engagement in school life and self-esteem as well as good interpersonal relationships. A 5-item scale was used to assess these aspects of school adjustment. Results showed that communication skills, pro-social behavior, emotional skills and learning management had significant effects on school adjustment only indirectly through self-regulation, and their total effects were found to be not high. The results indicate that support rendered to students with ASD focusing only on the training of well-defined skills is not adequate for promoting their inclusion in school. More attention should be paid to the training of self-management with an emphasis on the application of skills backed by self-regulation. Also, other non-skill factors are important in promoting inclusive education.

Keywords: autism, assessment, factor analysis, self-regulation, school adjustment

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Authors: Michael McMahon

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The use of media (video/animation/graphics) has long been used by athletes, coaches, and sports scientists to analyse and improve performance in technical skills and team tactics. Sports educators are increasingly open to the use of technology to support coach and learner development. However, an overreliance is a concern., This paper is part of a larger Ph.D. study looking into these new challenges for Sports Educators. Most notably, how to exploit the deep-learning potential of Digital Media among expert learners, how to instruct sports educators to create effective media content that fosters deep learning, and finally, how to make the process manageable and cost-effective. Central to the study is Richard Mayers Cognitive Theory of Multimedia Learning. Mayers Multimedia Learning Theory proposes twelve principles that shape the design and organization of multimedia presentations to improve learning and reduce cognitive load. For example, the Prior Knowledge principle suggests and highlights different learning outcomes for Novice and Non-Novice learners, respectively. Little research, however, is available to support this principle in modified domains (e.g., sports tactics and strategy). As a foundation for further research, this paper compares and contrasts a range of contemporary multimedia sports coaching content and assesses how they perform as learning tools for Strategic and Tactical Play Acquisition among elite sports practitioners. The stress tests applied are guided by Mayers's twelve Multimedia Learning Principles. The focus is on the elite athletes and whether current coaching digital media content does foster improved sports learning among this cohort. The sport of Gaelic Football was selected as it has high strategic and tactical play content, a wide range of Practitioner skill levels (Novice to Elite), and also a significant volume of Multimedia Coaching Content available for analysis. It is hoped the resulting data will help identify and inform the future instructional content design and delivery for Sports Practitioners and help promote best design practices optimal for different levels of expertise.

Keywords: multimedia learning, e-learning, design for learning, ICT

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Ruthanne Tobin

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The shift to a personalized learning (PL) curriculum in Canada represents an innovative approach to teaching and learning that is also evident in various initiatives across the 32-nation OECD. The premise behind PL is that empowering individual learners to have more input into how they access and construct knowledge, and express their understanding of it, will result in more meaningful school experiences and academic success. In this paper presentation, the author reports on a document analysis of the new curriculum in the province of British Columbia. Three theoretical frameworks are used to analyze the new curriculum. Framework 1 focuses on five dominant aspects (FDA) of PL at the classroom level. Framework 2 focuses on conceptualizing and enacting personalized learning (CEPL) within three spheres of influence. Framework 3 focuses on the integration of three types of knowledge (content, technological, and pedagogical). Analysis is ongoing, but preliminary findings suggest that the new curriculum addresses framework 1 quite well, which identifies five areas of personalized learning: 1) assessment for learning; 2) effective teaching and learning; 3) curriculum entitlement (choice); 4) school organization; and 5) “beyond the classroom walls” (learning in the community). Framework 2 appears to be less well developed in the new curriculum. This framework speaks to the dynamics of PL within three spheres of interaction: 1) nested agency, comprised of overarching constraints [and enablers] from policy makers, school administrators and community; 2) relational agency, which refers to a capacity for professionals to develop a network of expertise to serve shared goals; and 3) students’ personalized learning experience, which integrates differentiation with self-regulation strategies. Framework 3 appears to be well executed in the new PL curriculum, as it employs the theoretical model of technological, pedagogical content knowledge (TPACK) in which there are three interdependent bodies of knowledge. Notable within this framework is the emphasis on the pairing of technologies with excellent pedagogies to significantly assist students and teachers. This work will be of high relevance to educators interested in innovative school reform.

Keywords: curriculum reform, K-12 school change, innovations in education, personalized learning

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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: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Kassem Saleh

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An alarming number of software projects fail to deliver the required functionalities within the provided budget and timeframe and with the required qualities. Some of the main reasons for this problem include bad stakeholder management, poor communications and informal change management. Informal processes to identify, engage and control stakeholders lead to these reasons. Recently, to emphasize its importance, the Project Management Institute (PMI) updated the Project Management Body of Knowledge (PMBoK) to explicitly include the stakeholder management knowledge area. This knowledge area consists of four processes to identify stakeholders, plan stakeholder management, and manage and control stakeholder engagement. The use of appropriate techniques for stakeholder management in software projects will definitely lead to higher quality and successful software. In this paper, we describe some of the proven techniques that can be used during the execution of the four processes for stakeholder management. Development of collaboration tools for automating these processes are recommended and need to be integrated in available software project management tools.

Keywords: project management, stakeholder management, software development, project management body of knowledge

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Authors: Mamatha Hosapatna, Anne D. Souza, Antony Sylvan Dsouza, Vrinda Hari Ankolekar

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Our study intends to know the effect of the overall teaching program of Anatomy on a students learning. The advancement of various teaching methodologies in the present era has led to progressive changes in education. A student should be able to correlate well between the theory and practical knowledge attained even in the early years of their education in medicine and should be able to implement the same in patient care. The present study therefore aims to assess the impact the current anatomy teaching program has on a students learning and to what extent is it successful in making the learning program effective. Specific objectives of our study to assess the impact of overall teaching program of Anatomy in a students’ learning. Description of process proposed: A questionnaire will be constructed and the students will be asked to put forth their views regarding the Anatomy teaching program and its method of assessment. Suggestions, if any will also be encouraged to be put forth. Type of study is cross sectional observations. Target population is the first year MBBS students and sample size is 250. Assessment plan is to obtaining students responses using questionnaire. Calculating percentages of the responses obtained. Tabulation of the results will be done.

Keywords: anatomy, observational study questionnaire, observational study, M.B.B.S students

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Authors: Gilbert Makanda

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The problem of the teaching of mathematics is studied using differential equations. A mathematical model for knowledge acquisition in mathematics is developed. In this study we adopt the mathematical model that is normally used for disease modelling in the teaching of mathematics. It is assumed that teaching is 'infecting' students with knowledge thereby spreading this knowledge to the students. It is also assumed that students who gain this knowledge spread it to other students making disease model appropriate to adopt for this problem. The results of this study show that increasing recruitment rates, learning contact with teachers and learning materials improves the number of knowledgeable students. High dropout rates and forgetting taught concepts also negatively affect the number of knowledgeable students. The developed model is then solved using Matlab ODE45 and \verb"lsqnonlin" to estimate parameters for the actual data.

Keywords: differential equations, knowledge acquisition, least squares, dynamical systems

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Authors: Taghreed Alghamdi, Wendy Hall, David Millard

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Massive Open Online Courses (MOOCs) have recently gained widespread interest in the academic world, starting a wide range of discussion of a number of issues. One of these issues, using MOOCs in teaching and learning in the higher education by integrating MOOCs’ contents with traditional face-to-face activities in blended learning format, is called blended MOOCs (bMOOCs) and is intended not to replace traditional learning but to enhance students learning. Most research on MOOCs has focused on students’ perception and institutional threats whereas there is a lack of published research on academics’ experiences and practices. Thus, the first aim of the study is to develop a classification of blended MOOCs models by conducting a systematic literature review, classifying 19 different case studies, and identifying the broad types of bMOOCs models namely: Supplementary Model and Integrated Model. Thus, the analyses phase will emphasize on these different types of bMOOCs models in terms of adopting MOOCs by lecturers. The second aim of the study is to improve the understanding of lecturers’ acceptance of bMOOCs by investigate the factors that influence academics’ acceptance of using MOOCs in traditional learning by distributing an online survey to lecturers who participate in MOOCs platforms. These factors can help institutions to encourage their lecturers to integrate MOOCs with their traditional courses in universities.

Keywords: acceptance, blended learning, blended MOOCs, higher education, lecturers, MOOCs, professors

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Authors: Jeff Clarine, Chang-Shyh Peng, Daisy Sang

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Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.

Keywords: bioassay, machine learning, preprocessing, virtual screen

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Authors: S. Vadivukkarasi, K. Karthi, M. Karthick, C. Dinesh, S. Santhosh, A. Yogaraj

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The appointment system was designed to minimize patient’s idle time overlooking patients waiting time in hospitals. This is no longer valid in today’s consumer oriented society. Long waiting times for treatment in the outpatient department followed by short consultations has long been a complaint. Nowadays, customers use waiting time as a decisive factor in choosing a service provider. Queuing theory constitutes a very powerful tool because queuing models require relatively little data and are simple and fast to use. Because of this simplicity and speed, modelers can be used to quickly evaluate and compare various alternatives for providing service. The application of queuing models in the analysis of health care systems is increasingly accepted by health care decision makers. Timely access to care is a key component of high-quality health care. However, patient delays are prevalent throughout health care systems, resulting in dissatisfaction and adverse clinical consequences for patients as well as potentially higher costs and wasted capacity for providers. Arguably, the most critical delays for health care are the ones associated with health care emergencies. The allocation of resources can be divided into three general areas: bed management, staff management, and room facility management. Effective and efficient patient flow is indicated by high patient throughput, low patient waiting times, a short length of stay at the hospital and overtime, while simultaneously maintaining adequate staff utilization rates and low patient’s idle times.

Keywords: appointment system, patient scheduling, bed management, queueing calculation, system analysis

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Authors: Edward B. Ssekulima, Amir Etemadi

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This paper presents a Systems Thinking and System of Systems (SoS) viewpoint for managing requirements complexity in the grid integration of Variable Renewable Energy (VRE). To achieve a SoS approach, it is often necessary to inculcate a Systems Thinking (ST) perspective in the planning and design of the attendant system. We show how this approach can support the enhanced integration of VRE (wind, solar small hydro) for which intermittency is a key inhibiting factor to their sustainable grid integration. The results indicate that a ST and SoS approach are a critical tool for decision makers in the planning, design and deployment of VRE Sources for their sustainable grid-integration in accordance with relevant techno-economic, social and environmental requirements.

Keywords: sustainable grid-integration, system of systems, systems thinking, variable energy resources

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Authors: Danielle Sandler dos Passos, Helder Coelho, Flávia Mori Sarti

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Analysing the world banking sector, we realize that traditional risk measurement methodologies no longer reflect the actual scenario with uncertainty and leave out events that can change the dynamics of markets. Considering this, regulators and financial institutions began to search more realistic models. The aim is to include external influences and interdependencies between agents, to describe and measure the operationalization of these complex systems and their risks in a more coherent and credible way. Within this context, X-Events are more frequent than assumed and, with uncertainties and constant changes, the concept of antifragility starts to gain great prominence in comparison to others methodologies of risk management. It is very useful to analyse whether a system succumbs (fragile), resists (robust) or gets benefits (antifragile) from disorder and stress. Thus, this work proposes the creation of the Banking Antifragility Index (BAI), which is based on the calculation of a triangular fuzzy number – to "quantify" qualitative criteria linked to antifragility.

Keywords: adaptive complex systems, X-Events, risk management, antifragility, banking antifragility index, triangular fuzzy number

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Authors: Parsa Mahdavi, M. Amin Hariri-Ardebili

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The development of accurate machine learning and deep learning models traditionally demands hands-on expertise and a solid background to fine-tune hyperparameters. With the continuous expansion of datasets in various scientific and engineering domains, researchers increasingly turn to machine learning methods to unveil hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of the resultant meta-models and, consequently, the interpretation of the findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. AutoML encompasses crucial stages such as data preparation, feature engineering, hyperparameter optimization, and neural architecture search. This paper provides a comprehensive overview of the principles underpinning AutoML, surveying several widely-used AutoML platforms. Additionally, the paper offers a glimpse into the application of AutoML on various engineering datasets. By comparing these results with those obtained through classical machine learning methods, the paper quantifies the uncertainties inherent in the application of a single ML model versus the holistic approach provided by AutoML. These examples showcase the efficacy of AutoML in extracting meaningful patterns and insights, emphasizing its potential to revolutionize the way we approach and analyze complex datasets.

Keywords: automated machine learning, uncertainty, engineering dataset, regression

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Authors: Amrita Anil Agashe, Sumant Tapas, Ajay Verma Yogesh Sonavane, Sourabh Yeravar

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Due to advancement in MEMS technology today wireless sensors network has gained a lot of importance. The wide range of its applications includes environmental and habitat monitoring, object localization, target tracking, security surveillance etc. Wireless sensor networks consist of tiny sensor devices called as motes. The constrained computation power, battery power, storage capacity and communication bandwidth of the tiny motes pose challenging problems in the design and deployment of such systems. In this paper, we propose a ubiquitous framework for Real-Time Tracking, Sensing and Management System using IITH motes. Also, we explain the algorithm that we have developed for location management in wireless sensor networks with the aspect of mobility. Our developed framework and algorithm can be used to detect emergency events and safety threats and provides warning signals to handle the emergency.

Keywords: mobility management, motes, multihop, wireless sensor networks

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Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

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Authors: Harvard Najoan, Sarwono Sutikno, Yusep Rosmansyah

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Most enterprises are now using information technology services as their assets to support business objectives. These kinds of services are provided by the internal service provider (inside the enterprise) or external service provider (outside enterprise). To deliver quality information technology services, the service provider (which from now on will be called ‘organization’) either internal or external, must have a standard for service management system. At present, the standard that is recognized as best practice for service management system for the organization is international standard ISO/IEC 20000:2011. The most important part of this international standard is the first part or ISO/IEC 20000-1:2011-Service Management System Requirement, because it contains 22 for organization processes as a requirement to be implemented in an organizational environment in order to build, manage and deliver quality service to the customer. Assessing organization management processes is the first step to implementing ISO/IEC 20000:2011 into the organization management processes. This assessment needs Process Assessment Model (PAM) as an assessment instrument. PAM comprises two parts: Process Reference Model (PRM) and Measurement Framework (MF). PRM is built by transforming the 22 process of ISO/IEC 20000-1:2011 and MF is based on ISO/IEC 33020. This assessment instrument was designed to assess the capability of service management process in Divisi Teknologi dan Sistem Informasi (Information Systems and Technology Division) as an internal organization of PT Pos Indonesia. The result of this assessment model can be proposed to improve the capability of service management system.

Keywords: ISO/IEC 20000-1:2011, ISO/IEC 33020:2015, process assessment, process capability, service management system

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Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

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Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

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Authors: Bryony Pound

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This study investigated how views of green or urban space affect learning performance. It provides evidence of the value of visual access to greenspace in work and learning environments, and builds on the extensive research into the cognitive and learning-related benefits of access to green and natural spaces, particularly in learning environments. It demonstrates that benefits of visual access to natural spaces whilst learning can produce statistically significant faster responses than those facing urban views after only 5 minutes. The primary hypothesis of this research was that a greenspace view would improve short-term learning. Participants were randomly assigned to either a view of parkland or of urban buildings from the same room. They completed a psychological test of two stages. The first stage consisted of a presentation of words from eight different categories (four manmade and four natural). Following this a 2.5 minute break was given; participants were not prompted to look out of the window, but all were observed doing so. The second stage of the test involved a word recognition/false memory test of three types. Type 1 was presented words from each category; Type 2 was non-presented words from those same categories; and Type 3 was non-presented words from different categories. Participants were asked to respond with whether they thought they had seen the words before or not. Accuracy of responses and reaction times were recorded. The key finding was that reaction times for Type 2 words (highest difficulty) were significantly different between urban and green view conditions. Those with an urban view had slower reaction times for these words, so a view of greenspace resulted in better information retrieval for word and false memory recognition. Importantly, this difference was found after only 5 minutes of exposure to either view, during winter, and with a sample size of only 26. Greenspace views improve performance in a learning task. This provides a case for better visual access to greenspace in work and learning environments.

Keywords: benefits, greenspace, learning, restoration

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Authors: Nahla Khatib

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This study investigated several learning difficulties that affected the classroom learning experience of preservice students who are studying general science and methods of teaching science students at Faculty of Educational Studies at the Arab Open University (AOU) in Amman, Jordan. The focus questions for this study were to find answers for the following: 1. What are the main areas of learning difficulty among preservice students towards science education? 2. What are the main aspects of reducing obstacles towards success in science education? To achieve this goal, the researcher prepared a questionnaire which included 30 items to point out the learning difficulties among preservice students towards science education. The questionnaire was distributed among students enrolled in the general science courses 1&2 and methods of teaching science courses at the beginning of the spring semester of year (2013-2014). After collecting the filled questionnaire a descriptive statistical analysis was carried out (means and standard deviation) for the items of the questionnaire. After analyzing the data statistically our findings showed that student control–factors as well as course controlled factor, factors related to the nature of science, and factors related to the role of instructor affected student success toward science education. The study was concluded with a number of recommendations.

Keywords: nature of science, preservice teachers, science education, learning difficulties

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Authors: Anne-Lena Kampen, Øivind Kure

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Machine learning (ML) can be implemented in Wireless Sensor Networks (WSNs) as a central solution or distributed solution where the ML is embedded in the nodes. Embedding improves privacy and may reduce prediction delay. In addition, the number of transmissions is reduced. However, quality factors such as prediction accuracy, fault detection efficiency and coordinated control of the overall system suffer. Here, we discuss and highlight the trade-offs that should be considered when choosing between embedding and centralized ML, especially for multihop networks. In addition, we present estimations that demonstrate the energy trade-offs between embedded and centralized ML. Although the total network energy consumption is lower with central prediction, it makes the network more prone for partitioning due to the high forwarding load on the one-hop nodes. Moreover, the continuous improvements in the number of operations per joule for embedded devices will move the energy balance toward embedded prediction.

Keywords: central machine learning, embedded machine learning, energy consumption, local machine learning, wireless sensor networks, WSN

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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

Procedia PDF Downloads 534

Authors: Hahido Samaras

Abstract:

In many cases, especially with the pandemic playing a major role in fast-tracking the growth of the digital industry, online learning has become a necessity or even a standard educational model nowadays, reliably overcoming barriers such as location, time and cost and frequently combined with a face-to-face format (e.g., in blended learning). This being the case, it is evident that students in many parts of the world, as well as their parents, will increasingly need to become aware of the pros and cons of online versus traditional courses. This fast-growing mode of learning, accelerated during the years of the pandemic, presents an abundance of exciting options especially matched for a large number of secondary school students in remote places of the world where access to stimulating educational settings and opportunities for a variety of learning alternatives are scarce, adding advantages such as flexibility, affordability, engagement, flow and personalization of the learning experience. However, online learning can also present several challenges, such as a lack of student motivation and social interactions in natural settings, digital literacy, and technical issues, to name a few. Therefore, educational researchers will need to conduct further studies focusing on the benefits and weaknesses of online learning vs. traditional learning, while instructional designers propose ways of enhancing student motivation and engagement in virtual environments. Similarly, teachers will be required to become more and more technology-capable, at the same time developing their knowledge about their students’ particular characteristics and needs so as to match them with the affordances the technology offers. And, of course, schools, education programs, and policymakers will have to invest in powerful tools and advanced courses for online instruction. By developing digital courses that incorporate intentional opportunities for community-building and interaction in the learning environment, as well as taking care to include built-in design principles and strategies that align learning outcomes with learning assignments, activities, and assessment practices, rewarding academic experiences can derive for all students. This paper raises various issues regarding the effectiveness of online learning on students by reviewing a large number of research studies related to the usefulness and impact of online learning following the COVID-19-induced digital education shift. It also discusses what students, teachers, decision-makers, and parents have reported about this mode of learning to date. Best practices are proposed for parties involved in the development of online learning materials, particularly for secondary school students, as there is a need for educators and developers to be increasingly concerned about the impact of virtual learning environments on student learning and wellbeing.

Keywords: blended learning, online learning, secondary schools, virtual environments

Procedia PDF Downloads 79

Authors: Jonathan J. Foo, Keng Hao Chew

Abstract:

Interest in virtual reality (VR) technologies as virtual learning environments have been on the rise in recent years. With thanks to the aggressively competitive consumer electronics environment, VR technology has been made affordable and accessible to the average person with developments like Google Cardboard and Oculus Go. While the promise of virtual access to unique virtual learning environments with the benefits of experiential learning sounds extremely attractive, there are still concerns over user comfort in the psychomotor, cognitive, and affective domains. Reports of motion sickness and short durations create doubt and have stunted its growth. In this paper, a multidimensional framework is proposed for the evaluation of VR learning environments within the three dimensions: tactual quality, didactic quality, and autodidactic quality. This paper further proposes a mixed-methods experimental research plan that sets out to evaluate a virtual reality training simulator in the context of amateur sports fencing. The study will investigate if an immersive VR learning environment can effectively simulate an authentic learning environment suitable for instruction, practice, and assessment while providing the user comfort in the tactual, didactic, and autodidactic dimensions. The models and recommendations developed for this study are designed in the context of fencing, but the potential impact is a guide for the future design and evaluation of all VR developments across sports and technical classroom education.

Keywords: autodidactic quality, didactic quality, tactual quality, virtual reality

Procedia PDF Downloads 114

Authors: Sara M. Ismael, Ali H. Al-Badi

Abstract:

The rapid development and growth of technology has changed the method of obtaining information for educators and learners. Technology has created a new world of collaboration and communication among people. Incorporating new technology into the teaching process can enhance learning outcomes. Billions of individuals across the world are now connected together, and are cooperating and contributing their knowledge and intelligence. Time is no longer wasted in waiting until the teacher is ready to share information as learners can go online and get it immediately. The objectives of this paper are to understand the reasons why changes in teaching and learning methods are necessary, to find ways of improving them, and to investigate the challenges that present themselves in the adoption of new ICT tools in higher education institutes. To achieve these objectives two primary research methods were used: questionnaires, which were distributed among students at higher educational institutes and multiple interviews with faculty members (teachers) from different colleges and universities, which were conducted to find out why teaching and learning methodology should change. The findings show that both learners and educators agree that educational technology plays a significant role in enhancing instructors’ teaching style and students’ overall learning experience; however, time constraints, privacy issues, and not being provided with enough up-to-date technology do create some challenges.

Keywords: e-books, educational technology, educators, e-learning, learners, social media, Web 2.0, LMS

Procedia PDF Downloads 252