Search results for: skills gained through learning

Authors: Tolga Aydin, M. Fatih Alaeddinoğlu

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People, throughout the history, have made estimates and inferences about the future by using their past experiences. Developing information technologies and the improvements in the database management systems make it possible to extract useful information from knowledge in hand for the strategic decisions. Therefore, different methods have been developed. Data mining by association rules learning is one of such methods. Apriori algorithm, one of the well-known association rules learning algorithms, is not commonly used in spatio-temporal data sets. However, it is possible to embed time and space features into the data sets and make Apriori algorithm a suitable data mining technique for learning spatio-temporal association rules. Lake Van, the largest lake of Turkey, is a closed basin. This feature causes the volume of the lake to increase or decrease as a result of change in water amount it holds. In this study, evaporation, humidity, lake altitude, amount of rainfall and temperature parameters recorded in Lake Van region throughout the years are used by the Apriori algorithm and a spatio-temporal data mining application is developed to identify overflows and newly-formed soil regions (underflows) occurring in the coastal parts of Lake Van. Identifying possible reasons of overflows and underflows may be used to alert the experts to take precautions and make the necessary investments.

Keywords: apriori algorithm, association rules, data mining, spatio-temporal data

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Authors: Fang Gong

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Distance measures are an indispensable part of many instance-based learning (IBL) and machine learning (ML) algorithms. The value difference metrics (VDM) and inverted specific-class distance measure (ISCDM) are among the top-performing distance measures that address nominal attributes. VDM performs well in some domains owing to its simplicity and poorly in others that exist missing value and non-class attribute noise. ISCDM, however, typically works better than VDM on such domains. To maximize their advantages and avoid disadvantages, in this paper, a selection approach: a discriminative model for nominal attributes-based distance measures is proposed. More concretely, VDM and ISCDM are built independently on a training dataset at the training stage, and the most credible one is recorded for each training instance. At the test stage, its nearest neighbor for each test instance is primarily found by any of VDM and ISCDM and then chooses the most reliable model of its nearest neighbor to predict its class label. It is simply denoted as a discriminative distance measure (DDM). Experiments are conducted on the 34 University of California at Irvine (UCI) machine learning repository datasets, and it shows DDM retains the interpretability and simplicity of VDM and ISCDM but significantly outperforms the original VDM and ISCDM and other state-of-the-art competitors in terms of accuracy.

Keywords: distance measure, discriminative model, nominal attributes, nearest neighbor

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Authors: Lorena Alemán de la Garza, Marcela Georgina Gómez-Zermeño

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This paper presents the SolarSPELL case study that aims to generate information on the use of indicators that help evaluate the pedagogical quality of a digital library resources. SolarSPELL is a solar-powered digital library with WiFi connectivity. It offers a variety of open educational resources selected for their potential for the digital transformation of educational practices and the achievement of the 2030 Agenda for Sustainable Development, adopted by all United Nations Member States. The case study employed a quantitative methodology and the research instrument was applied to 55 teachers, directors and librarians. The results indicate that it is possible to strengthen the pedagogical quality of open educational resources, through actions focused on improving temporal and technological parameters. They also reveal that users believe that SolarSPELL improves the teaching-learning processes and motivates the teacher to improve his or her development. This study provides valuable information on a tool that supports teaching-learning processes and facilitates connectivity with renewable energies that improves the teacher training in active methodologies for ecosystem learning.

Keywords: educational innovation, digital library, pedagogical quality, solar energy, teacher training, sustainable development

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Authors: Seada Hussen Adem, Frie Ayalew Yimam

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Due to its high biomass output and potential to produce renewable energy, water hyacinth, a rapidly expanding aquatic weed, has gained recognition as a prospective bioenergy feedstock. Through a variety of conversion processes, such as anaerobic digestion, combustion, and gasification, this study suggests using water hyacinth to generate energy. The suggested strategy helps to reduce the annoyance brought on by the excessive growth of water hyacinth in Tana water bodies in addition to offering an alternate source of energy. The study emphasizes the value of environmentally friendly methods for managing Tana water resources as well as the potential of water hyacinth as a source of bioenergy.

Keywords: anaerobic digestion, bioenergy, combustion, gasification, water hyacinth

Procedia PDF Downloads 49

Authors: Ogbeta I. Joseph, Habiba B. A. Awwalu, Otokiti Jimoh

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This paper entitled science education in Nigeria issues and challenges highlighted the role of science education to the development of science and technology in Nigeria. Science embraces every attempt of human to explore and manage the natural world, the contribution of science education to the technological development of the nation, the role of science education in ICT development, the importance of mathematics in the development of science education, the paper also analyzed the challenges facing the development of science education to include corruption, insecurity, and political instability, the paper concluded by encouraging the government and other stakeholders in educational sector to pay more attention to the teaching and learning of science in our schools. Therefore recommended the development that emphasizes should be on the teaching and learning of science base subjects in the school.

Keywords: education, science, technology and national development, challenges

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Authors: Beatriz Botella-Gil, Patricio Martínez-Barco, Lea Canales

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The Internet and ICT are an integral element of and omnipresent in our daily lives. Technologies have changed the way we see the world and relate to it. The number of companies in the ICT sector is increasing every year, and there has also been an increase in the work that occurs online, from sending e-mails to the way companies promote themselves. In social life, ICT’s have gained momentum. Social networks are useful for keeping in contact with family or friends that live far away. This change in how we manage our relationships using electronic devices and social media has been experienced differently depending on the age of the person. According to currently available data, people are increasingly connected to social media and other forms of online communication. Therefore, it is no surprise that violent content has also made its way to digital media. One of the important reasons for this is the anonymity provided by social media, which causes a sense of impunity in the victim. Moreover, it is not uncommon to find derogatory comments, attacking a person’s physical appearance, hobbies, or beliefs. This is why it is necessary to develop artificial intelligence tools that allow us to keep track of violent comments that relate to violent events so that this type of violent online behavior can be deterred. The objective of our research is to create a guide for detecting and recording violent messages. Our annotation guide begins with a study on the problem of violent messages. First, we consider the characteristics that a message should contain for it to be categorized as violent. Second, the possibility of establishing different levels of aggressiveness. To download the corpus, we chose the social network Twitter for its ease of obtaining free messages. We chose two recent, highly visible violent cases that occurred in Spain. Both of them experienced a high degree of social media coverage and user comments. Our corpus has a total of 633 messages, manually tagged, according to the characteristics we considered important, such as, for example, the verbs used, the presence of exclamations or insults, and the presence of negations. We consider it necessary to create wordlists that are present in violent messages as indicators of violence, such as lists of negative verbs, insults, negative phrases. As a final step, we will use automatic learning systems to check the data obtained and the effectiveness of our guide.

Keywords: human language technologies, language modelling, offensive language detection, violent online content

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Authors: Seyfollah Fazlollahi, Ahmad Bayan Memar

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Background: A review of manpower planning includes a training analysis based on job descriptions and job specifications which looks carefully at training from the points of view of the company, its various departments and personnel. This may show weaknesses in some departments and as a result, training is needed for the staff. Purpose: The aim of this research is to investigate the effects of training on employee’s efficiency in different aspects of work. Methodology: This is a descriptive-survey study. Statistical population was 85 official employees of University of Qom, Iran. 70 of these individuals were selected on a statistical random sampling method using Morgan&Gorki table. The instrument used in this study was a questionnaire including 22 questions. Result: Findings in this study according to data analysis indicate that majority of respondents had positive attitude towards training programs, in the job or off the job. They believed that training programs promoted and enhanced their behavior positively which leads to high efficiency in their job. In fact, data support the main hypothesis that training has positive effects on job performance and efficiency. Conclusion: It is concluded from this study and other related researches that training (on the job and off the job) has positive and effective role in human development and labor as employee’s efficiency. Employees get acquainted with different tasks of a job. Group co-operation, creativity and innovation will be enforced. Training leads to job skills, increasing knowledge and information about a job. It also increases technical and conceptual human skills, which are important in an organization. We can also mention workers' increasing positive motivation toward their job, enforcement of coordinating moral, their good human relations and good contact with clients.

Keywords: training, work efficiency, employee, human relation, job satisfaction

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Authors: Kornelia Kordiak

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The research is based on a biographical approach and serves as a foundation for understanding individual human destinies through the analysis of the context of life experiences. It focuses on the significance of childhood in shaping family micro-worlds from the perspective of biographical learning. In this case, the family micro-world is interpreted as a complex of beliefs and judgments about elements of the ‘total universe’ based on the individual's experiences. The main aim of the research is to understand the importance of childhood in shaping family micro-worlds from the perspective of reflection on biographical learning. Additionally, it contributes to a deeper understanding of the familial experiences of the studied individuals who form these family micro-worlds and the course of the biographical learning process in the subjects. Biographical research aligns with an interpretative paradigm, where individuals are treated as active interpreters of the world, giving meaning to their experiences and actions based on their own values and beliefs. The research methods used in the project—narrative interview method and analysis of personal documents—enable obtaining a multidimensional perspective on the phenomenon under study. Narrative interviews serve as the main data collection method, allowing researchers to delve into various life contexts of individuals. Analysis of these narratives identifies key moments and life patterns, as well as discovers the significance of childhood in shaping family micro-worlds. Moreover, analysis of personal documents such as diaries or photographs enriches the understanding of the studied phenomena by providing additional contexts and perspectives. The research will be conducted in three phases: preparatory, main, and final. The anticipated schedule includes preparation of research tools, selection of research sample, conducting narrative interviews and analysis of personal documents, as well as analysis and interpretation of collected research material. The narrative interview method and document analysis will be utilized to capture various contexts and interpretations of childhood experiences and family relations. The research will contribute to a better understanding of family dynamics and individual developmental processes. It will allow for the identification and understanding of mechanisms of biographical learning and their significance in shaping identity and family relations. Analysis of adult narratives will enable the identification of factors determining patterns of behavior and attitudes in adult life, which may have significant implications for pedagogical practice.

Keywords: childhood, adulthood, biographical learning, narrative interview, analysis of personal documents, family micro-worlds

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Authors: Anukul K. Thakur, Ram Bilash Choudhary, Mandira Majumder

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In this technologically driven world, it is requisite to develop better, faster and smaller electronic devices for various applications to keep pace with fast developing modern life. In addition, it is also required to develop sustainable and clean sources of energy in this era where the environment is being threatened by pollution and its severe consequences. Supercapacitor has gained tremendous attention in the recent years because of its various attractive properties such as it is essentially maintenance-free, high specific power, high power density, excellent pulse charge/discharge characteristics, exhibiting a long cycle-life, require a very simple charging circuit and safe operation. Binary and ternary composites of conducting polymers with carbon and other layered transition metal dichalcogenides have shown tremendous progress in the last few decades. Compared with bulk conducting polymer, these days conducting polymers have gained more attention because of their high electrical conductivity, large surface area, short length for the ion transport and superior electrochemical activity. These properties make them very suitable for several energy storage applications. On the other hand, carbon materials have also been studied intensively, owing to its rich specific surface area, very light weight, excellent chemical-mechanical property and a wide range of the operating temperature. These have been extensively employed in the fabrication of carbon-based energy storage devices and also as an electrode material in supercapacitors. Incorporation of carbon materials into the polymers increases the electrical conductivity of the polymeric composite so formed due to high electrical conductivity, high surface area and interconnectivity of the carbon. Further, polymeric composites based on layered transition metal dichalcogenides such as molybdenum disulfide (MoS2) are also considered important because they are thin indirect band gap semiconductors with a band gap around 1.2 to 1.9eV. Amongst the various 2D materials, MoS2 has received much attention because of its unique structure consisting of a graphene-like hexagonal arrangement of Mo and S atoms stacked layer by layer to give S-Mo-S sandwiches with weak Van-der-Waal forces between them. It shows higher intrinsic fast ionic conductivity than oxides and higher theoretical capacitance than the graphite.

Keywords: supercapacitor, layered transition-metal dichalcogenide, conducting polymer, ternary, carbon

Procedia PDF Downloads 237

Authors: Surajit Chakrabarty, Piyush Chauhan, Subhasis Panda, Sujoy Bhattacharya

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A large proportion of roads in India are not well maintained as per the laid down public safety guidelines leading to loss of direction control and fatal accidents. We propose a technique to detect speed breakers and potholes using mobile sensor data captured from multiple vehicles and provide a profile of the road. This would, in turn, help in monitoring roads and revolutionize digital maps. Incorporating randomness in the model formulation for detection of speed breakers and potholes is crucial due to substantial heterogeneity observed in data obtained using a mobile application from multiple vehicles driven by different drivers. This is accomplished with Hidden Markov Models, whose hidden state sequence is found for each time step given the observables sequence, and are then fed as input to LSTM network with peephole connections. A precision score of 0.96 and 0.63 is obtained for classifying bumps and potholes, respectively, a significant improvement from the machine learning based models. Further visualization of bumps/potholes is done by converting time series to images using Markov Transition Fields where a significant demarcation among bump/potholes is observed.

Keywords: deep learning, hidden Markov model, pothole, speed breaker

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Authors: Matthew John F. Sino Cruz, Sarah Jane M. Ferrer, Janice C. Francisco

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COVID-19 pandemic has affected more than 114 countries all over the world since it was considered a global health concern in 2020. Different sectors, including education, have shifted to remote/distant setups to follow the guidelines set to prevent the spread of the disease. One of the higher education institutes which shifted to remote setup is the Pangasinan State University (PSU). In order to continue providing quality instructions to the students, PSU designed Flexible Learning Model to still provide services to its stakeholders amidst the pandemic. The model covers the redesigning of delivering instructions in remote setup and the technology needed to support these adjustments. The primary goal of this study is to determine the insights of the PSU – Bayambang students towards the remote setup implemented during the pandemic and how they perceived the initiatives employed in relation to their experiences in flexible learning. In this study, the topic modelling approach was implemented using Latent Dirichlet Allocation. The dataset used in the study. The results show that the most common concern of the students includes time and resource management, poor internet connection issues, and difficulty coping with the flexible learning modality. Furthermore, the findings of the study can be used as one of the bases for the administration to review and improve the policies and initiatives implemented during the pandemic in relation to remote service delivery. In addition, further studies can be conducted to determine the overall sentiment of the other stakeholders in the policies implemented at the University.

Keywords: COVID-19, topic modelling, students’ sentiment, flexible learning, Latent Dirichlet allocation

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Authors: Vijaya Dixit Aasheesh Dixit

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Shipbuilding industry operates in Engineer Procure Construct (EPC) context. Product mix of a shipyard comprises of various types of ships like bulk carriers, tankers, barges, coast guard vessels, sub-marines etc. Each order is unique based on the type of ship and customized requirements, which are engineered into the product right from design stage. Thus, to execute every new project, a shipyard needs to upgrade its production expertise. As a result, over the long run, holistic learning occurs across different types of projects which contributes to the knowledge base of the shipyard. Simultaneously, in the short term, during execution of a project comprising of multiple sister ships, repetition of similar tasks leads to learning at activity level. This research aims to capture above learnings of a shipyard and incorporate learning curve effect in project scheduling and materials procurement to improve project performance. Extant literature provides support for the existence of such learnings in an organization. In shipbuilding, there are sequences of similar activities which are expected to exhibit learning curve behavior. For example, the nearly identical structural sub-blocks which are successively fabricated, erected, and outfitted with piping and electrical systems. Learning curve representation can model not only a decrease in mean completion time of an activity, but also a decrease in uncertainty of activity duration. Sister ships have similar material requirements. The same supplier base supplies materials for all the sister ships within a project. On one hand, this provides an opportunity to reduce transportation cost by batching the order quantities of multiple ships. On the other hand, it increases the inventory holding cost at shipyard and the risk of obsolescence. Further, due to learning curve effect the production scheduled of each consequent ship gets compressed. Thus, the material requirement schedule of every next ship differs from its previous ship. As more and more ships get constructed, compressed production schedules increase the possibility of batching the orders of sister ships. This work aims at integrating materials management with project scheduling of long duration projects for manufacturing of multiple sister ships. It incorporates the learning curve effect on progressively compressing material requirement schedules and addresses the above trade-off of transportation cost and inventory holding and shortage costs while satisfying budget constraints of various stages of the project. The activity durations and lead time of items are not crisp and are available in the form of probabilistic distribution. A Stochastic Mixed Integer Programming (SMIP) model is formulated which is solved using evolutionary algorithm. Its output provides ordering dates of items and degree of order batching for all types of items. Sensitivity analysis determines the threshold number of sister ships required in a project to leverage the advantage of learning curve effect in materials management decisions. This analysis will help materials managers to gain insights about the scenarios: when and to what degree is it beneficial to treat a multiple ship project as an integrated one by batching the order quantities and when and to what degree to practice distinctive procurement for individual ship.

Keywords: learning curve, materials management, shipbuilding, sister ships

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Authors: Proud Arunrangsiwed

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Crisis management strategies could be found in various curriculums, not only in schools of business, but also schools of communication. Young students, such as freshmen and sophomores of undergraduate schools, may not care about learning crisis management strategies. Moreover, crisis management strategies are not a topic art students are familiar with. The current paper discusses a way to adapt entertainment media into a crisis management lesson, and the importance of learning crisis management strategies in the school of animation. Students could learn crisis management strategies by watching movies with content about a crisis and responding to crisis responding. The students should then participate in follow up discussions related to the strategies that were used to address the crisis, as well as their success in solving the crisis.

Keywords: situational crisis communication theory, crisis response strategies, media effect, unintentional effect

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Authors: Mauro Callejas-Cuervo, Andrea Catherine Alarcon-Aldana, Lorena Paola Castillo-Guerra

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The software industry requires highly-trained professionals, capable of developing the roles integrated in the cycle of software development. That is why a large part of the task is the responsibility of higher education institutions; often through a curriculum established to orientate the academic development of the students. It is so that nowadays there are different models that support proposals for the improvement of the curricula for the area of Software Engineering, such as ACM, IEEE, ABET, Swebok, of which the last stands out, given that it manages and organises the knowledge of Software Engineering and offers a vision of theoretical and practical aspects. Moreover, it has been applied by different universities in the pursuit of achieving coverage in delivering the different topics and increasing the professional quality of future graduates. This research presents the structure of teaching and learning guides from the objectives of training and methodological strategies immersed in the levels of learning of Bloom’s taxonomy with which it is intended to improve the delivery of the topics in the area of Requirements Engineering. Said guides were implemented and validated in a course of Requirements Engineering of the Systems and Computer Engineering programme in the Universidad Pedagógica y Tecnológica de Colombia (Pedagogical and Technological University of Colombia) using a four stage methodology: definition of the evaluation model, implementation of the guides, guide evaluation, and analysis of the results. After the collection and analysis of the data, the results show that in six out of the seven topics proposed in the Swebok guide, the percentage of students who obtained total marks within the 'High grade' level, that is between 4.0 and 4.6 (on a scale of 0.0 to 5.0), was higher than the percentage of students who obtained marks within the 'Acceptable' range of 3.0 to 3.9. In 86% of the topics and the strategies proposed, the teaching and learning guides facilitated the comprehension, analysis, and articulation of the concepts and processes of the students. In addition, they mainly indicate that the guides strengthened the argumentative and interpretative competencies, while the remaining 14% denotes the need to reinforce the strategies regarding the propositive competence, given that it presented the lowest average.

Keywords: pedagogic guide, pedagogic strategies, requirements engineering, Swebok, teaching-learning process

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Authors: Beverley Gallimore

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The Individuals with Disabilities Education Act of 2004 (IDEA) has helped to improve outcomes for students with special education needs. Through IDEA, students with Special Education Needs (SEN) have opportunities for more equitable education within the General Education classroom. However, students with disabilities lack access to instructions that can help them to maximize their fullest learning potential. Recently, educational stakeholders have emphasized Integrated Co-teaching as a tool to increase engagement and learning outcomes for students with disabilities in general education classrooms. As a result of this new approach, general and special education teachers are working collaboratively to teach students with disabilities. However, co-teaching models are not properly designed and structured to effectively benefit students with disabilities. Teachers must be oriented correctly in the co-teaching models if it is to be beneficial for students.

Keywords: CO-teaching, differentiation, equitable, collaborative

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Authors: Tracey Woods

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In the ever-evolving landscape of academia, the traditional role of the librarian has undergone a remarkable transformation. Once considered as custodians of books and gatekeepers of information, librarians have the potential to take on the vital role of facilitators of cross and inter-disciplinary projects. This shift is driven by the growing recognition of the value of interdisciplinary collaboration in addressing complex research questions in pursuit of novel solutions to real-world problems. This paper shall explore the potential of the academic librarian’s role in facilitating innovative, multi-disciplinary projects, both recognising and validating the vital role that the librarian plays in a somewhat underplayed profession. Academic libraries support teaching, the strengthening of knowledge discourse, and, potentially, the development of innovative practices. As the role of the library gradually morphs from a quiet repository of books to a community-based information hub, a potential opportunity arises. The academic librarian’s role is to build knowledge across a wide span of topics, from the advancement of AI to subject-specific information, and, whilst librarians are generally not offered the research opportunities and funding that the traditional academic disciplines enjoy, they are often invited to help build research in support of the academic. This identifies that one of the primary skills of any 21st-century librarian must be the ability to collaborate and facilitate multi-disciplinary projects. In universities seeking to develop research diversity and academic performance, there is an increasing awareness of the need for collaboration between faculties to enable novel directions and advancements. This idea has been documented and discussed by several researchers; however, there is not a great deal of literature available from recent studies. Having a team based in the library that is adept at creating effective collaborative partnerships is valuable for any academic institution. This paper outlines the development of such a project, initiated within and around an identified library-specific need: the replication of fragile special collections for object-based learning. The research was developed as a multi-disciplinary project involving the faculties of engineering (digital twins lab), architecture, design, and education. Centred around methods for developing a fragile archive into a series of tactile objects furthers knowledge and understanding in both the role of the library as a facilitator of projects, chairing and supporting, alongside contributing to the research process and innovating ideas through the bank of knowledge found amongst the staff and their liaising capabilities. This paper shall present the method of project development from the initiation of ideas to the development of prototypes and dissemination of the objects to teaching departments for analysis. The exact replication of artefacts is also balanced with the adaptation and evolutionary speculations initiated by the design team when adapted as a teaching studio method. The dynamic response required from the library to generate and facilitate these multi-disciplinary projects highlights the information expertise and liaison skills that the librarian possesses. As academia embraces this evolution, the potential for groundbreaking discoveries and innovative solutions across disciplines becomes increasingly attainable.

Keywords: Liaison librarian, multi-disciplinary collaborations, library innovations, librarian stakeholders

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Authors: Usman Asif, Klaus Schmidt

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In recent years, the development of alternate fuel vehicles has helped to reduce carbon emissions worldwide. As the number of vehicles will continue to increase in the future, the energy demand will also increase. Therefore, we must consider automotive technologies that are efficient and less harmful to the environment in the long run. Battery Electric Vehicles (BEVs) have gained popularity in recent years because of their lower maintenance, lower fuel costs, and lower carbon emissions. Nevertheless, BEVs show several disadvantages, such as slow charging times and lower range than traditional combustion-powered vehicles. These factors keep many people from switching to BEVs. The authors of this research believe that these limitations can be overcome by using fuel cell technology. Fuel cell technology converts chemical energy into electrical energy from hydrogen power and therefore serves as fuel to power the motor and thus replacing heavy lithium batteries that are expensive and hard to recycle. Also, in contrast to battery-powered electric vehicle technology, Fuel Cell Electric Vehicles (FCEVs) offer higher ranges and lower fuel-up times and therefore are more competitive with electric vehicles. However, FCEVs have not gained the same popularity as electric vehicles due to stringent legal frameworks, underdeveloped infrastructure, high fuel transport, and storage costs plus the expense of fuel cell technology itself. This research will focus on the legal frameworks for hydrogen-powered vehicles, and how a change in these policies may affect and improve hydrogen fueling infrastructure and lower hydrogen transport and storage costs. These policies may also facilitate reductions in fuel cell technology costs. In order to attain a better framework, a number of countries have developed conceptual roadmaps. These roadmaps have set out a series of objectives to increase the access of FCEVs to their respective markets. This research will specifically focus on policies in Japan, Europe, and the USA in their attempt to shape the automotive industry of the future. The researchers also suggest additional policies that may help to accelerate the advancement of FCEVs to mass-markets. The approach was to provide a solid literature review using resources from around the globe. After a subsequent analysis and synthesis of this review, the authors concluded that in spite of existing legal challenges that have hindered the advancement of fuel-cell technology in the automobile industry in the past, new initiatives that enhance and advance the very same technology in the future are underway.

Keywords: fuel cell electric vehicles, fuel cell technology, legal frameworks, policies and regulations

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Authors: Claus N. Holm, Oliver F. Grooss, Robert A. Alphinas

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This research strives to predict the remaining product life cycle of a song on radio after it has been played for one or two months. The best results were achieved using a k-d tree to calculate the most similar songs to the test songs and use a Random Forest model to forecast radio plays. An 82.78% and 83.44% accuracy is achieved for the two time periods, respectively. This explorative research leads to over 4500 test metrics to find the best combination of models and pre-processing techniques. Other algorithms tested are KNN, MLP and CNN. The features only consist of daily radio plays and use no musical features.

Keywords: hit song science, product life cycle, machine learning, radio

Procedia PDF Downloads 141

Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

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Authors: Joseph Abiodun Ayo

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The main purpose of this study was to investigate the science laboratory management practices employed in some colleges of education in Kano State, Nigeria. Four specific objectives were stated to guide the study, four research questions were investigated, four null hypothesis were tested at 0.05 level of significance. A survey design was used and science laboratory management questionnaires which solicit responses that was used in answering the research questions and testing of hypotheses. These questionnaires were distributed to the respective respondents in the sampled colleges. The respondents for the study comprised biology chemistry, physics, integrated science teacher trainers and the paraprofessionals. Data were analyzed using mean and standard deviation to answer the questions. Chi-square statistical technique was used to test the hypothesis. The findings of the study revealed that all procedures on control of laboratory activities were rarely observed. Safety procedures were occasionally practiced. On provision and procurement of laboratory equipment and materials it was observed that both academic and the paraprofessional were not fully involved. While maintenance measures were occasionally observed, furthermore science laboratory management procedures are not frequently practiced. Hence making the acquisition of science process skills by students becoming difficult. To arrest these anomalies, it is recommended that direct labor in the maintenance of laboratory equipment and other apparatus by paraprofessional is crucial. Training of academic and paraprofessional through workshops to acquire technical skills in maintenance of science laboratory equipment be instituted to increase professionalism. Periodic supervision of activities in the science laboratories should be done promptly.

Keywords: laboratory, management, standard, facility

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Authors: Azadeh Afrasyabi, Ali Khaleghi, Aliakbar Alijarahi

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Training at an early age is so important, because of tremendous changes in adolescence, including the formation of character, physical changes and other factors. One of the most sensitive sectors in this field is the children with a disability and are somehow special children who have trouble in communicating with their environment. One of the emerging technologies in the field of education that can be effectively profitable called augmented reality, where the combination of real world and virtual images in real time produces new concepts that can facilitate learning. The purpose of this paper is to propose an effective training method for special and disabled children based on augmented reality. Of course, in particular, the efficiency of augmented reality in teaching children with autism will consider, also examine the various aspect of this disease and different learning methods in this area.

Keywords: technology in education, augmented reality, special education, teaching methods

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Authors: Natascha C. D'Amico, Enzo Grossi, Giovanni Valbusa, Ala Malasevschi, Gianpiero Cardone, Sergio Papa

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Purpose: To characterize, through a radiomic approach, the nature of areas classified PI-RADS (Prostate Imaging Reporting and Data System) 3/5, recognized in multiparametric prostate magnetic resonance with T2-weighted (T2w), diffusion and perfusion sequences with paramagnetic contrast. Methods and Materials: 24 cases undergoing multiparametric prostate MR and biopsy were admitted to this pilot study. Clinical outcome of the PI-RADS 3/5 was found through biopsy, finding 8 malignant tumours. The analysed images were acquired with a Philips achieva 1.5T machine with a CE- T2-weighted sequence in the axial plane. Semi-automatic tumour segmentation was carried out on MR images using 3DSlicer image analysis software. 45 shape-based, intensity-based and texture-based features were extracted and represented the input for preprocessing. An evolutionary algorithm (a TWIST system based on KNN algorithm) was used to subdivide the dataset into training and testing set and select features yielding the maximal amount of information. After this pre-processing 20 input variables were selected and different machine learning systems were used to develop a predictive model based on a training testing crossover procedure. Results: The best machine learning system (three-layers feed-forward neural network) obtained a global accuracy of 90% ( 80 % sensitivity and 100% specificity ) with a ROC of 0.82. Conclusion: Machine learning systems coupled with radiomics show a promising potential in distinguishing benign from malign tumours in PI-RADS 3/5 areas.

Keywords: machine learning, MR prostate, PI-Rads 3, radiomics

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Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

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Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

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Authors: Gracia V. Navarro, Maria V. Gonzalez, Carlos G. Reed

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The eudaimonic perspective in philosophy and psychology suggests that a good life is closely related to developing oneself in order to contribute to the well-being and happiness of other people and of the world as a whole. Educational psychology can help to achieve this through the design and validation of educative models. Since 2004, the University of Concepcion and other Chilean universities apply an educative model to train socially responsible professionals, people that in the exercise of their profession contribute to generate equity for the development and assess the impacts of their decisions, opting for those that serve the common good. The main aim is to identify if a relationship exists between achieved learning, attitudes toward social responsibility, self-attribution of socially responsible behavior, value type, professional behavior observed and, participation in a specific model to train socially responsible (SR) professionals. The Achieved Learning and Attitudes Toward Social Responsibility Questionnaire, interview with employers and Values Questionnaire and Self-attribution of SR Behavior Questionnaire is applied to 394 students and graduates, divided into experimental and control groups (trained and not trained under the educative model), in order to identify the professional behavior of the graduates. The results show that students and graduates perceive cognitive, affective and behavioral learning, with significant differences in attitudes toward social responsibility and self-attribution of SR behavior, between experimental and control. There are also differences in employers' perceptions about the professional practice of those who were trained under the model and those who were not. It is concluded that the educative model has an impact on the learning of social responsibility and educates for a full life. It is also concluded that it is necessary to identify mediating variables of the model effect.

Keywords: educative model, good life, professional social responsibility, values

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Authors: Zabeehullah, Fahim Arif, Yawar Abbas

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Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

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Authors: Flavio Campos

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This paper describes an implementation of a STEM curriculum program using robotics as a technological resource at a private school in Brazil. Emphasized the pedagogic and didactic aspects and brings a discussion about STEM curriculum and the perspective of using robotics and the relation between curriculum, science and technologies into the learning process. The results indicate that STEM curriculum integration with robotics as a technological resource in K-12 students learning process has complex aspects, such as relation between time/space, the development of educators and the relation between robotics and other subjects. Therefore, the comprehension of these aspects could indicate some steps that we should consider when integrating STEM basis and robotics into curriculum, which can improve education for science and technology significantly.

Keywords: STEM curriculum, educational robotics, constructionist approach, education and technology

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Authors: Said Fares, Mary Fares

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It is well documented that introductory computer programming courses are difficult and that failure rates are high. The aim of this project was to reduce the high failure and withdrawal rates in learning to program. This paper presents a number of changes in module organization and instructional delivery system in teaching CS1. Daily out of class help sessions and tutoring services were applied, interactive lectures and laboratories, online resources, and timely feedback were introduced. Five years of data of 563 students in 21 sections was collected and analyzed. The primary results show that the failure and withdrawal rates were cut by more than half. Student surveys indicate a positive evaluation of the modified instructional approach, overall satisfaction with the course and consequently, higher success and retention rates.

Keywords: failure rate, interactive learning, student engagement, CS1

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Authors: Soheila Sadeghi

Abstract:

In construction projects, predicting project performance metrics accurately is essential for effective management and successful delivery. However, conventional methods often depend on fixed baseline plans, disregarding the evolving nature of project progress and external influences. To address this issue, we introduce a distinct approach based on machine learning to forecast key performance indicators, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category within an urban road reconstruction project. Our proposed model leverages time series forecasting techniques, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance by analyzing historical data and project progress. Additionally, the model incorporates external factors, including weather patterns and resource availability, as features to improve forecast accuracy. By harnessing the predictive capabilities of machine learning, our performance forecasting model enables project managers to proactively identify potential deviations from the baseline plan and take timely corrective measures. To validate the effectiveness of the proposed approach, we conduct a case study on an urban road reconstruction project, comparing the model's predictions with actual project performance data. The outcomes of this research contribute to the advancement of project management practices in the construction industry by providing a data-driven solution for enhancing project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, schedule variance, earned value management

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Authors: Chia-Ching Sun, Li-Yin Chien, Chun-Ting Hsiao

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Background: Appropriate gestation weight gain benefits pregnant women and their children; however, excessive weight gain could raise the risk of adverse health outcomes and chronicle diseases. Nevertheless, there is currently limited evidence on the effect of physical activities on pregnant women’s gestational weight gain. Purpose: This study aimed to explore the correlation between the level of physical activity and gestation weight gain during the second and third trimester of pregnancy. Methods: This longitudinal study enrolled 800 healthy pregnant women aged over 20 from six hospitals in northern Taiwan. Structured questionnaires were used to collect data twice for each participant during 14-27 and 28-40 weeks of gestation. Variables included demographic data, maternal health history, and lifestyle. The International Physical Activity Questionnaire-short form was used to measure the level of physical activity from walking and of moderate-intensity and vigorous-intensity before and during pregnancy. Weight recorded at prenatal checkups were used to calculate average weight gain in each trimester of pregnancy. T-tests, ANOVA, chi-squared tests, and multivariable logistic regression models were applied to determine the predicting factors for weight gain during the second and third trimester. Result: Participants who had achieved recommended physical activity level (150 minutes of moderate physical activity or 75 minutes of vigorous physical activity a week) before pregnancy (aOR=1.85, 95% CI=1.27-2.67) or who achieved recommended walking level (150 minutes a week) during the second trimester of pregnancy (aOR=1.43, 95% CI= 1.00-2.04) gained significantly more weight during the second trimester. Compared with those who did not reach recommended level of moderate-intensity physical activity (150 minutes a week), women who had reached that during the second trimester were more likely to be in the less than recommended weight gain group than in the recommended weight gain group (aOR=2.06, CI=1.06-4.00). However, there was no significant correlation between physical activity level and weight gain in the third trimester. Other predicting factors of excessive weight gain included education level which showed a negative correlation (aOR=0.38, CI=0.17-0.88), whereas overweight and obesity before pregnancy showed a positive correlation (OR=3.97, CI=1.23-12.78). Conclusions/implications for practice: Participants who had achieved recommended physical activity level before pregnancy significantly reduced exercise during pregnancy and gained excessive weight during the second trimester. However, women who engaged in the practice of physical activity as recommended could effectively control weight gain in the third trimester. Healthcare professionals could suggest that pregnant women who exercise maintain their pre-pregnancy level of physical activity, given activities requiring physical contact or causing falls are avoided. For those who do not exercise, health professionals should encourage them to gradually increase the level of physical activity. Health promotion strategies related to weight control and physical activity level achievement should be given to women before pregnancy.

Keywords: pregnant woman, physical activity, gestation weight gain, obesity, overweight

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Authors: Patricia Kopetz

Abstract:

Too often our youngest children with disabilities receive diagnostic labels and accompanying treatment plans based upon perceptions that the children are of limited aptitude and/or ambition. However, children of varied-ability levels who are diagnosed with ‘multiple disabilities,’ can participate and excel in school-based instruction that aligns with their desires, interests, and fortitude – criteria components not foretold by scores on standardized assessments. The paper represents theoretical work in Special Education Innovative Instruction, and includes presenting research materials, some developed by the author herself. The majority of students with disabilities are now served in general education settings in the United States, embracing inclusive practices in our schools. ‘There is now a stronger call for special education to step up and improve efficiency, implement evidence-based practices, and provide greater accountability on key performance indicators that support successful academic and post-school outcomes for students with disabilities.’ For example, in the United States, the Office of Special Education Programs (OSEP) is focusing on results-driven indicators to improve outcomes for students with disabilities. School personnel are appreciating the implications of research-driven approaches for students diagnosed with multiple disabilities, and aim to align their practices toward such focus. The paper presented will provide updates on current theoretical principles and perspectives, and explore advancements in latest, evidence-based and results-driven instructional practices that can motivate children with multiple disabilities to advance their skills and engage in learning activities that as nonconventional, innovative, and proven successful.

Keywords: childhood special education, educational technology , innovative instruction, multiple disabilities

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