Search results for: collaborative learning approach

Authors: Anishur Rahman, Maria Areias, Diogo Simões, Ana Figeuiredo, Filipa Figueiredo, João Nunes

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The circular economy (CE) has gained increased awareness among academics, businesses, and decision-makers as it stimulates resource circularity in the production and consumption systems. A large epistemological study has explored the principles of CE, but scant attention eagerly focused on analysing how CE is evaluated, consented to, and enforced using economic metabolism data and business intelligent framework. Economic metabolism involves the ongoing exchange of materials and energy within and across socio-economic systems and requires the assessment of vast amounts of data to provide quantitative analysis related to effective resource management. Limited concern, the present work has focused on the regional flows pilot region from Portugal. By addressing this gap, this study aims to promote eco-innovation and sustainability in the regions of Intermunicipal Communities Região de Coimbra, Viseu Dão Lafões and Beiras e Serra da Estrela, using this data to find precise synergies in terms of material flows and give companies a competitive advantage in form of valuable waste destinations, access to new resources and new markets, cost reduction and risk sharing benefits. In our work, emphasis on applying artificial intelligence (AI) and, more specifically, on implementing state-of-the-art deep learning algorithms is placed, contributing to construction a business intelligent approach. With the emergence of new approaches generally highlighted under the sub-heading of AI and machine learning (ML), the methods for statistical analysis of complex and uncertain production systems are facing significant changes. Therefore, various definitions of AI and its differences from traditional statistics are presented, and furthermore, ML is introduced to identify its place in data science and the differences in topics such as big data analytics and in production problems that using AI and ML are identified. A lifecycle-based approach is then taken to analyse the use of different methods in each phase to identify the most useful technologies and unifying attributes of AI in manufacturing. Most of macroeconomic metabolisms models are mainly direct to contexts of large metropolis, neglecting rural territories, so within this project, a dynamic decision support model coupled with artificial intelligence tools and information platforms will be developed, focused on the reality of these transition zones between the rural and urban. Thus, a real decision support tool is under development, which will surpass the scientific developments carried out to date and will allow to overcome imitations related to the availability and reliability of data.

Keywords: circular economy, artificial intelligence, economic metabolisms, machine learning

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Authors: Shamie Kumar

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Background: Radiologists and radiographers (RR) need to educate themselves and their colleagues to ensure that AI is integrated safely, useful, and in a meaningful way with the direction it always benefits the patients. AI education and training are fundamental to the way RR work and interact with it, such that they feel confident using it as part of their clinical practice in a way they understand it. Methodology: This exploratory research will outline the current educational and training gaps for radiographers and radiologists in AI radiology diagnostics. It will review the status, skills, challenges of educating and teaching. Understanding the use of artificial intelligence within daily clinical practice, why it is fundamental, and justification on why learning about AI is essential for wider adoption. Results: The current knowledge among RR is very sparse, country dependent, and with radiologists being the majority of the end-users for AI, their targeted training and learning AI opportunities surpass the ones available to radiographers. There are many papers that suggest there is a lack of knowledge, understanding, and training of AI in radiology amongst RR, and because of this, they are unable to comprehend exactly how AI works, integrates, benefits of using it, and its limitations. There is an indication they wish to receive specific training; however, both professions need to actively engage in learning about it and develop the skills that enable them to effectively use it. There is expected variability amongst the profession on their degree of commitment to AI as most don’t understand its value; this only adds to the need to train and educate RR. Currently, there is little AI teaching in either undergraduate or postgraduate study programs, and it is not readily available. In addition to this, there are other training programs, courses, workshops, and seminars available; most of these are short and one session rather than a continuation of learning which cover a basic understanding of AI and peripheral topics such as ethics, legal, and potential of AI. There appears to be an obvious gap between the content of what the training program offers and what the RR needs and wants to learn. Due to this, there is a risk of ineffective learning outcomes and attendees feeling a lack of clarity and depth of understanding of the practicality of using AI in a clinical environment. Conclusion: Education, training, and courses need to have defined learning outcomes with relevant concepts, ensuring theory and practice are taught as a continuation of the learning process based on use cases specific to a clinical working environment. Undergraduate and postgraduate courses should be developed robustly, ensuring the delivery of it is with expertise within that field; in addition, training and other programs should be delivered as a way of continued professional development and aligned with accredited institutions for a degree of quality assurance.

Keywords: artificial intelligence, training, radiology, education, learning

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Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Zahra Alikhani Koopaei

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In the fifth lesson of the third-grade mathematics book, students are introduced to the concept of multiplication. However, some students showed a lack of interest in learning this topic. To address this, a simple electronic multiplication table was designed with the aim of making the concept of multiplication entertaining and engaging for students. It provides them with moments of excitement during the learning process. To achieve this goal, a device was created that produced a bell sound when two wire ends were connected. Each wire end was connected to a specific number in the multiplication table, and the other end was linked to the corresponding answer. Consequently, if the answer is correct, the bell will ring. This study employs interactive and engaging methods to teach mathematics, particularly to students who have previously shown little interest in the subject. By integrating game-based learning and critical thinking, we observed an increase in understanding and interest in learning multiplication compared to before using this method. This further motivated the students. As a result, the intelligent multiplication table was successfully designed. Students, under the instructor's supervision, could easily construct the device during the lesson. Through the implementation of these operations, the concept of multiplication was firmly established in the students' minds. Engaging multiple intelligences in each student enhances a more stable and improved understanding of the concept of multiplication.

Keywords: intelligent multiplication table, design, construction, education, increased interest, students

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Authors: J. Mano Ranjini

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Each and every child is special, born with a unique talent to explore this world. The word Dyslexia is derived from the Greek language in which “dys” meaning poor or inadequate and “lexis” meaning words or language. Dyslexia describes about a different kind of mind, which is often gifted and productive, that learns the concept differently. The main aim of the study is to bring the positive outcome of the reading level by examining the effectiveness of Visual Auditory Kinesthetic Tactile technique on Reading Level among Dyslexic Children at Helikx Open School and Learning Centre. A Quasi experimental one group pretest post test design was adopted for this study. The Reading Level was assessed by using the Schonell Graded Word Reading Test. Thirty subjects were drawn by using purposive sampling technique and the intervention Visual Auditory Kinesthetic Tactile technique was implemented to the Dyslexic Children for 30 consecutive days followed by the post Reading Level assessment revealed the improvement in the mean score value of reading level by 12%. Multi-sensory (VAKT) teaching uses all learning pathways in the brain (visual, auditory, kinesthetic-tactile) in order to enhance memory and learning and the ability in uplifting emotional, physical and societal dimensions. VAKT is an effective method to improve the reading skill of the Dyslexic Children that ensures the enormous significance of learning thereby influencing the wholesome of the child’s life.

Keywords: visual auditory kinesthetic tactile technique, reading level, dyslexic children, Helikx Open School

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Authors: Suraj Gururaj, Sumantha Udupa U.

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Browsing the Web has emerged as the de facto activity performed on the Internet. Although browsing gets tracked, the manageability aspect of Web browsing history is very poor. In this paper, we have a workable solution implemented by using machine learning and natural language processing techniques for efficient manageability of user’s browsing history. The significance of adding such a capability to a Web browser is that it ensures efficient and quick information retrieval from browsing history, which currently is very challenging. Our solution guarantees that any important websites visited in the past can be easily accessible because of the intelligent and automatic classification. In a nutshell, our solution-based paper provides an implementation as a browser extension by intelligently classifying the browsing history into most relevant category automatically without any user’s intervention. This guarantees no information is lost and increases productivity by saving time spent revisiting websites that were of much importance.

Keywords: adhoc retrieval, Chrome extension, supervised learning, tile, Web personalization

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Authors: Virginia Guillén Cañas, Miren Agurtzane Ortiz-Jauregi, Sonia Ruiz De Azua, Naiara Ozamiz

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We want to focus on relationship of the communicational skills in several key aspects of medicine. The most relevant competencies of a health professional are an adequate communication capacity, which will influence the satisfaction of professionals and patients, therapeutic compliance, conflict prevention, clinical outcomes’ improvement and efficiency of health services. We define empathy as it as Sympathy and connection to others and capability to communicate this understanding. Some outcomes favoring empathy are female gender, younger age, and specialty choice. Third gender or third sex is a concept in which allows a person not to be categorized in a dual way but as a continuous variable, giving the choice of moving along it. This point of view recognizes three or more genders. The subject of Ethics and Clinical Communication is dedicated to sensitizing students about the importance and effectiveness of a good therapeutic relationship. We are also interested in other communicational aspects related to empathy as active listening, assertivity and basic and advanced Social Skills. Objectives: 1. To facilitate the approach of the student in the Medicine Degree to the reality of the medical profession 2. Analyze interesting outcome variables in communication 3. Interactive process to detect the areas of improvement in the learning process of the Physician throughout his professional career needs. Design: A comparative study with a cross-sectional approach was conducted in successive academic year cohorts of health professional students at a public Basque university. Four communicational aspects were evaluated through these questionnaires in Basque, Spanish and English: The active listening questionnaire, the TECA empathy questionnaire, the ACDA questionnaire and the EHS questionnaire Social Skills Scale. Types of interventions for improving skills: Interpersonal skills training intervention, Empathy intervention, Writing about experiential learning, Drama through role plays, Communicational skills training, Problem-based learning, Patient interviews ´videos, Empathy-focused training, Discussion. Results: It identified the need for a cross cultural adaptation and no gender distinction. The students enjoyed all the techniques in comparison to the usual master class. There was medium participation but these participative methodologies are not so usual in the university. According to empathy, men have a greater empathic capacity to fully understand women (p < 0.05) With regard to assertiveness there have been no differences between men and women in self-assertiveness but nevertheless women are more heteroassertive than men. Conclusions: These findings suggest that educational interventions with adequate feedback can be effective in maintaining and enhancing empathy in undergraduate medical students.

Keywords: physician's communicational skills, patient satisfaction, third gender, cross cultural adaptation

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Authors: A. Alshabeb

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Vocabulary is one of the elements that links the four skills of reading, writing, speaking, and listening and is very critical in learning a foreign language. This study aims to determine how Saudi Arabian EFL students learn English vocabulary via Twitter. The study adopts a mixed sequential research design in collecting and analysing data. The results of the study provide several recommendations for vocabulary learning. Moreover, the study can help teachers to consider the possibilities of using Twitter further, and perhaps to develop new approaches to vocabulary teaching and to support students in their use of social media.

Keywords: social media, twitter, vocabulary, web 2

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Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

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The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

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Authors: Arun Raja, Kevin Thomas, Sreyas Tribhu, S. P. Anbuudayasankar

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This paper deals with the application of six sigma methodology in supply chain (SC) and logistics. A detailed cram about how the SC can be improved and its impact on the organization are dealt with and also how the quality plays a vital role in improving SC and logistics are identified. A simulation has been performed using the ARENA software to determine the process efficiency of a bottle manufacturing unit. Further, a Value Stream Mapping (VSM) analysis has been executed on the manufacturing process flow model and the manner by which Value Engineering (VE) holds a significant importance for quality assertion on the products is also studied.

Keywords: supply chain, six sigma, value engineering, logistics, quality

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Authors: Alona Faktor

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In this work, we present a NN-based computational model that can perform attention shifts according to high-level instruction. The instruction specifies the type of attentional shift using explicit geometrical relation. The instruction also can be of cognitive nature, specifying more complex human-human interaction or human-object interaction, or object-object interaction. Applying this approach sequentially allows obtaining a structural description of an image. A novel data-set of interacting humans and objects is constructed using a computer graphics engine. Using this data, we perform systematic research of relational segmentation shifts.

Keywords: cognitive science, attentin, deep learning, generalization

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Authors: Jonathan Bolduc

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Studies have demonstrated the effectiveness of music training approaches to help children with language disabilities. Because music is closely associated with a number of cognitive functions, including language, it has been hypothesized that musical skills transfer to other domains. Research suggests that music training strengthens basic auditory processing skills in dyslexic children and may ameliorate phonological deficits. Furthermore, music instruction has the particular advantage of being non-literacy-based, thus removing the frustrations that can be associated with reading and writing activities among children with specific learning disabilities. In this study, we assessed the effect of implementing an intensive music program on the development of language skills (phonological and reading) in 4- to 9-year-old children. Seventeen children (N=17) participated in the study. The experiment took place over 6 weeks in a controlled environment. Eighteen lessons of 40 minutes were offered during this period by two music specialists. The Dalcroze, Orff, and Kodaly approaches were used. A series of qualitative measures were implemented to document the contribution of music training to this population. Currently, the data is being analyzed. The first results show that learning music seems to significantly improve verbal memory. We already know that language disabilities are considered one of the main causes of school dropout as well as later professional and social failure. We aim to corroborate that an integrated music education program can provide children with language disabilities with the same opportunities to develop and succeed in school as their classmates. Scientifically, the results will contribute to advance the knowledge by identifying the more effective music education strategies to improve the overall development of children worldwide.

Keywords: music education, music, art education, language diasabilities

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Authors: Angela Meyer

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The operation and maintenance cost for wind parks make up a major fraction of the park’s overall lifetime cost. To minimize the cost and risk involved, an optimal operation and maintenance strategy requires continuous monitoring and analysis. In order to facilitate this, we present a decision support system that automatically scans the stream of telemetry sensor data generated from the turbines. By learning decision boundaries and normal reference operating states using machine learning algorithms, the decision support system can detect anomalous operating behavior in individual wind turbines and diagnose the involved turbine sub-systems. Operating personal can be alerted if a normal operating state boundary is exceeded. The presented decision support system and method are applicable for any turbine type and manufacturer providing telemetry data of the turbine operating state. We demonstrate the successful detection and diagnosis of anomalous operating states in a case study at a German onshore wind park comprised of Vestas V112 turbines.

Keywords: anomaly detection, decision support, machine learning, monitoring, performance optimization, wind turbines

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Authors: Bruno Pinto Vieira, Marco Antonio Calijorne Soares, Armando Sérgio de Aguiar Filho

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Nowadays, a massive amount of information has been produced by different data sources, including mobile devices and transactional systems. In this scenario, concerns arise on how to maintain or establish data quality, which is now treated as a product to be defined, measured, analyzed, and improved to meet consumers' needs, which is the one who uses these data in decision making and companies strategies. Information that reaches low levels of quality can lead to issues that can consume time and money, such as missed business opportunities, inadequate decisions, and bad risk management actions. The step of selecting, identifying, evaluating, and selecting data sources with significant quality according to the need has become a costly task for users since the sources do not provide information about their quality. Traditional data quality control methods are based on user experience or business rules limiting performance and slowing down the process with less than desirable accuracy. Using advanced machine learning algorithms, it is possible to take advantage of computational resources to overcome challenges and add value to companies and users. In this study, machine learning is applied to data quality analysis on different datasets, seeking to compare the performance of the techniques according to the dimensions of quality assessment. As a result, we could create a ranking of approaches used, besides a system that is able to carry out automatically, data quality assessment.

Keywords: machine learning, data quality, quality dimension, quality assessment

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Authors: Kuek Teik Sheng, Leaw Zee Guan, Lim Wah Kien, Ting Tin Tin

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Educational technology such as YouTube and Kahoot have arisen as an alternative to effective learning among higher education institutions. There are many researches done in carrying out experiments to test different educational technologies and received positive feedback from students. Yet, similar study is hardly found in Malaysia especially study that includes the latest educational technologies. As a developing country, it is crucial to ensure that these emerging technologies are assisting students in learning process before it is widely adopted in institutions. This paper conducted a study to explore the perception of higher education institution students on the current educational technologies in Malaysia which include online educational games, online videos/course, social media, presentation tools and resource management tool. Some of these technologies have not been looked into its potential in effective learning process. An online survey using questionnaire is conducted among a target of 300 university/college. In the survey, the result shows that majority of the target students in Malaysia agree that the current educational technologies help them in learning, understanding and manage their studies. It is necessary to discover students’ perceptions on the educational technologies in order to provide guidelines for the educators/institutions in selecting appropriate technology to conduct the lecture/tutorial efficiently and effectively.

Keywords: education, educational technology, Facebook, PowerPoint, YouTube

Procedia PDF Downloads 230

Authors: Christina Van Wingerden

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This paper focuses on a sense of belonging, isolation, and the use of a learning management system as a “third space” for connection and community. Given student use of learning management systems (LMS) for courses on campuses, moderate to high use of social media and hand-held devices, the author explores the possibilities of LMS as a third space. The COVID-19 pandemic has exacerbated student experiences of isolation, and research indicates that students who experience a sense of belonging have a greater likelihood for academic retention and success. The impacts on students of an LMS designed for student employee orientation and training were examined through a mixed methods approach, including a survey, individual interviews, and focus groups. The sample involved 250-450 undergraduate student employees at a US northwestern university. The goal of the study was to find out the efficiency and effectiveness of the orientation information for a wide range of student employees from multiple student affairs departments. And unexpected finding emerged within the study in 2015 and was noted again as a finding in the 2017 study. Students reported feeling like they individually connected to the department, and further to the university because of the LMS orientation. They stated they could see themselves as part of the university community and like they belonged. The orientation, through the LMS, was designed for and occurred online (asynchronous), prior to students traveling and beginning university life for the academic year. The students indicated connection and belonging resulting from some of the design features. With the onset of COVID-19 and prolonged sheltering in place in North America, as well as other parts of the world, students have been precluded from physically gathering to educate and learn. COVID-19 essentially paused face-to-face education in 2020. Media, governments, and higher education outlets have been reporting on widespread college student stress, isolation, loneliness, and sadness. In this context, the author conducted a current mixed methods study (online survey, online interviews) of students in advanced degree programs, like Ph.D. and Ed.D. specifically investigating isolation and sense of belonging. As a part of the study a prototype of a Canvas site was experienced by student interviewees for their reaction of this Canvas site prototype as a “third” space. Some preliminary findings of this study are presented. Doctoral students in the study affirmed the potential of LMS as a third space for community and social academic connection.

Keywords: COVID-19, isolation, learning management system, sense of belonging

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Authors: Htay Htay Yi, Zin May Aye

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Network security is role of the ICT environment because malicious users are continually growing that realm of education, business, and then related with ICT. The network security contravention is typically described and examined centrally based on a security event management system. The firewalls, Intrusion Detection System (IDS), and Intrusion Prevention System are becoming essential to monitor or prevent of potential violations, incidents attack, and imminent threats. In this system, the firewall rules are set only for where the system policies are needed. Dataset deployed in this system are derived from the testbed environment. The traffic as in DoS and PortScan traffics are applied in the testbed with firewall and IDS implementation. The network traffics are classified as normal or attacks in the existing testbed environment based on six machine learning classification methods applied in the system. It is required to be tested to get datasets and applied for DoS and PortScan. The dataset is based on CICIDS2017 and some features have been added. This system tested 26 features from the applied dataset. The system is to reduce false positive rates and to improve accuracy in the implemented testbed design. The system also proves good performance by selecting important features and comparing existing a dataset by machine learning classifiers.

Keywords: false negative rate, intrusion detection system, machine learning methods, performance

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Authors: Cory A. Logston

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It is imperative researchers continue to explore every transformative strategy to increase empathy and awareness of racial bias. Racism is a social and political concept that uses stereotypical ideology to highlight racial inequities. Everyone has biases they may not be aware of toward disparate out-groups. There is some form of racism in every profession; doctors, lawyers, and teachers are not immune. There have been numerous successful and unsuccessful strategies to motivate and transform an individual’s unconscious biased attitudes. One method designed to induce a transformative experience and identify implicit bias is virtual reality (VR). VR is a technology designed to transport the user to a three-dimensional environment. In a virtual reality simulation, the viewer is immersed in a realistic interactive video taking on the perspective of a Black man. The viewer as the character experiences discrimination in various life circumstances growing up as a child into adulthood. For instance, the prejudice felt in school, as an adolescent encountering the police and false accusations in the workplace. Current research suggests that an immersive VR simulation can enhance self-awareness and become a transformative learning experience. This study uses virtual reality immersion and transformative learning theory to create empathy and identify any unintentional racial bias. Participants, White teachers, will experience a VR immersion to create awareness and identify implicit biases regarding Black students. The desired outcome provides a springboard to reconceptualize their own implicit bias. Virtual reality is gaining traction in the research world and promises to be an effective tool in the transformative learning process.

Keywords: empathy, implicit bias, transformative learning, virtual reality

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Kim Bower

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Evaluating and researching motivation in language learning is a complex and multi-faceted activity. Various models for investigating learner motivation have been proposed in the literature, but no one model supplies a complex and coherent model for investigating a range of motivational characteristics. Here, such a methodological framework, which includes exemplification of sources of evidence and potential methods of investigation, is proposed. The process model for the investigation of motivation within language learning settings proposed is based on a complex dynamic systems perspective that takes account of cognition and affects. It focuses on three overarching aspects of motivation: the learning environment, learner engagement and learner identities. Within these categories subsets are defined: the learning environment incorporates teacher, course and group specific aspects of motivation; learner engagement addresses the principal characteristics of learners' perceived value of activities, their attitudes towards language learning, their perceptions of their learning and engagement in learning tasks; and within learner identities, principal characteristics of self-concept and mastery of the language are explored. Exemplifications of potential sources of evidence in the model reflect the multiple influences within and between learner and environmental factors and the possible changes in both that may emerge over time. The model was initially developed as a framework for investigating different models of Content and Language Integrated Learning (CLIL) in contrasting contexts in secondary schools in England. The study, from which examples are drawn to exemplify the model, aimed to address the following three research questions: (1) in what ways does CLIL impact on learner motivation? (2) what are the main elements of CLIL that enhance motivation? and (3) to what extent might these be transferable to other contexts? This new model has been tried and tested in three locations in England and reported as case studies. Following an initial visit to each institution to discuss the qualitative research, instruments were developed according to the proposed model. A questionnaire was drawn up and completed by one group prior to a 3-day data collection visit to each institution, during which interviews were held with academic leaders, the head of the department, the CLIL teacher(s), and two learner focus groups of six-eight learners. Interviews were recorded and transcribed verbatim. 2-4 naturalistic observations of lessons were undertaken in each setting, as appropriate to the context, to provide colour and thereby a richer picture. Findings were subjected to an interpretive analysis by the themes derived from the process model and are reported elsewhere. The model proved to be an effective and coherent framework for planning the research, instrument design, data collection and interpretive analysis of data in these three contrasting settings, in which different models of language learning were in place. It is hoped that the proposed model, reported here together with exemplification and commentary, will enable teachers and researchers in a wide range of language learning contexts to investigate learner motivation in a systematic and in-depth manner.

Keywords: investigate, language-learning, learner motivation model, dynamic systems perspective

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Authors: Beng Zhen Yeow

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In most work that involves children, the voice of the children is often not heard. This is ironic since a lot of discussions might involve their welfare and safety. It might seem natural that the professionals should hear from them about what they wish for instead of deciding what is best for them. However, this, unfortunately, might be more the exception than the norm in most case and hence in many instances, children are merely 'subjects' in conversations about safety instead of active participants in the construction or creation of safety in the family. There might be many reasons why it does not happen in our work. Firstly, professionals have learnt how to 'socialise' into their professional roles and hence in the process become 'un-childlike'. Secondly, there is also a lack of professional training with regards to how to talk with children. Finally, there might be also a lack of concrete tools and techniques that are developed to facilitate the process. In this paper, the case study method is used to show how the idea of safety could be concretised and discussed with children and their family members, and hence making them active participants and co-creators of their own safety. Specific skills and techniques are highlighted through the case study. In this case, there was improvement in outcomes like no repeated offence or abuse. In addition, children were also able to advocate for their own safety after six months of intervention and how the family members were able to explicitly say what they can do to improve safety. The professionals in the safety network reported significant improvements. On top of that, the abused child who was removed due to child protection concerns, had verbalized observations of change in mother’s parenting abilities, and has requested for home leave to begin due to ownership of safety planning and having confidence to co-create safety for her siblings and herself together with the professionals in the safety network. Children becoming active participants in the co-creation of safety not only serve the purpose in allowing them to own a 'voice' but at the same time, give them greater confidence to protect themselves at home and in other contexts outside of home.

Keywords: partnering for safety, collaborative social work, family and systemic psychotherapy, child protection

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Authors: Alonge Isaac Olusola

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Taking a cue from countries such as China and Japan, there is no doubt that the teaching and learning of Mother Tongue ( MT) or Language of Immediate Environment (LIE) is a potential source of development in every country. The engine of economic, scientific, technological and political advancement would be more functional when the language of instruction for teaching and learning in schools is in the child’s mother tongue. The purpose of this paper therefore, is to delve into the genesis of the official recognition given to the teaching and learning of Nigerian languages at national level with special focus on Yoruba language. Yoruba language and other Nigerian languages were placed on a national pedestal by a Nigerian Educational Minister, Late Professor Babatunde Fafunwa, who served under the government of General Ibrahim Babangida (1985 – 1993). Through his laudable effort, the teaching and learning of Nigerian languages in schools all over the nation was incorporated officially in the national policy of education. Among all the Nigerian languages, Hausa, Igbo and Yoruba were given foremost priorities because of the large population of their speakers. Since the Fafunwa era, Yoruba language has become a national subject taught in primary, secondary and tertiary institutions in Nigeria. However, like every new policy, its implementation has suffered several forms of criticisms and impediments from governments, policy makers, curriculum developers, school administrators, teachers and learners. This paper has been able to arrive at certain findings through oral interviews, questionnaires and evaluation of pupils/students enrolment and performances in Yoruba language with special focus on the South-west and North central regions of Nigeria. From the research carried out, some factors have been found to be responsible for the successful implementation or otherwise of Yoruba language instruction policy in some schools, colleges and higher institutions in Nigeria. In conclusion, the paper made recommendations on how the National Policy of Education would be implemented to enhance the teaching and learning of Yoruba language in all Nigerian schools.

Keywords: language of immediate environment, mother tongue, national policy of education, yoruba language

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Authors: Farzaneh Badinlou, Reza Kormi-Nouri, Monika Knopf, Kamal Kharazi

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Research into long-term memory has demonstrated that the richness of the knowledge base cues in memory tasks improves retrieval process, which in turn influences learning and memory performance. The present research investigated the idea that adding cues connected to knowledge can affect memory performance in the context of action memory in children. In action memory studies, participants are instructed to learn a series of verb–object phrases as verbal learning and experience-based learning (learning by doing and learning by observation). It is well established that executing action phrases is a more memorable way to learn than verbally repeating the phrases, a finding called enactment effect. In the present study, a total of 410 students from four grade groups—2nd, 4th, 6th, and 8th—participated in this study. During the study, participants listened to verbal action phrases (VTs), performed the phrases (SPTs: subject-performed tasks), and observed the experimenter perform the phrases (EPTs: experimenter-performed tasks). During the test phase, cued recall test was administered. Semantic relational cues (i.e., well-integrated vs. poorly integrated items) were manipulated in the present study. In that, the participants were presented two lists of action phrases with high semantic integration between verb and noun, e.g., “write with the pen” and with low semantic integration between verb and noun, e.g., “pick up the glass”. Results revealed that experience-based learning had a better results than verbal learning for both well-integrated and poorly integrated items, though manipulations of semantic relational cues can moderate the enactment effect. In addition, children of different grade groups outperformed for well- than poorly integrated items, in flavour of older children. The results were discussed in relation to the effect of knowledge-based information in facilitating retrieval process in children.

Keywords: action memory, enactment effect, knowledge-based cues, school-aged children, semantic relational cues

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Authors: Wen Chen

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To investigate the interleaving effect of subject matter (mathematics vs. history) and perceptual modality (visual vs. auditory materials) on student’s attention and learning outcomes, the present study collected self-reported data on subjective cognitive load (SCL) and attention level, EEG data, and learning outcomes from micro-lectures. Eighty-one 7th grade students were randomly assigned to four learning conditions: blocked (by subject matter) micro-lectures with auditory textual information (B-A condition), blocked (by subject matter) micro-lectures with visual textual information (B-V condition), interleaved (by subject matter) micro-lectures with auditory textual information (I-A condition), and interleaved micro-lectures by both perceptual modality and subject matter (I-all condition). The results showed that although interleaved conditions may show advantages in certain indices, the I-all condition showed the best overall outcomes (best performance, low SCL, and high attention). This study suggests that interleaving by both subject matter and perceptual modality should be preferred in scheduling and planning classes.

Keywords: cognitive load, interleaving effect, micro-lectures, sustained attention

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Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

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This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

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Authors: Fatih Frank Alparslan

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The aviation industry is at a crucial turning point due to modern technologies, environmental concerns, and changing ways of transporting people and goods globally. The paper examines these challenges and opportunities comprehensively. It emphasizes the role of innovative management and advanced technology in shaping the future of air travel. This study begins with an overview of the current state of the aviation industry, identifying key areas where innovation and technology could be highly beneficial. It explores the latest advancements in airplane design, propulsion, and materials. These technological advancements are shown to enhance aircraft performance and environmental sustainability. The paper also discusses the use of artificial intelligence and machine learning in improving air traffic control, enhancing safety, and making flight operations more efficient. The management of these technologies is critically important. Therefore, the research delves into necessary changes in organization, culture, and operations to support innovation. It proposes a management approach that aligns with these modern technologies, underlining the importance of forward-thinking leaders who collaborate across disciplines and embrace innovative ideas. The paper addresses challenges in adopting these innovations, such as regulatory barriers, the need for industry-wide standards, and the impact of technological changes on jobs and society. It recommends that governments, aviation businesses, and educational institutions collaborate to address these challenges effectively, paving the way for a more innovative and eco-friendly aviation industry. In conclusion, the paper argues that the future of aviation relies on integrating new management practices with innovative technologies. It urges a collective effort to push beyond current capabilities, envisioning an aviation industry that is safer, more efficient, and environmentally responsible. By adopting a broad approach, this research contributes to the ongoing discussion about resolving the complex issues facing today's aviation sector, offering insights and guidance to prepare for future advancements.

Keywords: aviation innovation, technology integration, environmental sustainability, management strategies, multidisciplinary approach

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Authors: Susana Silva, Dora Martins

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The recent political, economic, social technological and employment changes significantly affect the tourism organizations and consequently the changing nature of the employment experience of the tourism workforce. Such scene leads several researchers and labor analysts to reflect about what kinds of jobs, knowledge and competences are need to ensure the success to teach, to learning and to work on this sector. In recent years the competency-based approach in high education level has become of significant interest. On the one hand, this approach could leads to the forming of the key students’ competences which contribute their better preparation to the professional future and on the other hand could answer better to practical demands from tourism job market. The goals of this paper are (1) to understand the expectations of university tourism students in relation to the present and future tourism competences demands, (2) to identify the importance put on the soft skills, (3) to know the importance of high qualification to their future professional activity and (4) to explore the students perception about present and future tourist sector specificities. To this proposal, a questionnaire was designed and distributed to every students who participate on classes of Hospitality Management under degree and master from one public Portuguese university. All participants were invited, during December 2014 and September 2015, to answer the questionnaire at the moment and on presence of one researcher of this study. Fulfilled the questionnaire 202 students (72, 35,6% male and 130, 64.4% female), the mean age was 21,64 (SD=5,27), 91% (n=86) were undergraduate and 18 (9%) were master students. 80% (n=162) of our participants refers as a possibility to look for a job outside the country.42% (n=85) prefers to work in a medium-sized tourism units (with 50-249 employees). According to our participants the most valued skills in tourism are the domain of foreign languages (87.6%, n=177), the ability to work as a team (85%), the personal persistence (83%, n=168), the knowledge of the product/services provided (73.8%, n=149), and assertiveness (66.3%, n=134). 65% (n=131) refers the availability to look for a job in a home distance of 1000 kilometers and 59% (n=119) do not consider the possibility to work in another area than tourism. From the results of this study we are in the position of confirming the need for universities to maintain a better link with the professional tourism companies and to rethink some competences into their learning course model. Based on our results students, universities and companies could understand more deeply the motivations, expectations and competences need to build the future career who study and work on the tourism sector.

Keywords: human capital, employability, students’ competencies perceptions, soft skills, tourism

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Authors: Vadivel Ayyasamy

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The present approach deals with the identification of Emotions and classification of Emotional patterns at Phrase-level with respect to Positive and Negative Orientation. The proposed approach considers emotion triggered terms, its co-occurrence terms and also associated sentences for recognizing emotions. The proposed approach uses Part of Speech Tagging and Emotion Actifiers for classification. Here sentence patterns are broken into phrases and Neuro-Fuzzy model is used to classify which results in 16 patterns of emotional phrases. Suitable intensities are assigned for capturing the degree of emotion contents that exist in semantics of patterns. These emotional phrases are assigned weights which supports in deciding the Positive and Negative Orientation of emotions. The approach uses web documents for experimental purpose and the proposed classification approach performs well and achieves good F-Scores.

Keywords: emotions, sentences, phrases, classification, patterns, fuzzy, positive orientation, negative orientation

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Authors: Julian Wise

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Newcrest Mining is one of the world’s top five gold and rare earth mining organizations by production, reserves and market capitalization in the world. This paper elaborates on the data acquisition processes employed by Newcrest in collaboration with Fortune 500 listed organization, Insight Enterprises, to standardize machine learning solutions which process data from over a hundred thousand distributed Internet of Things (IoT) devices located at mine sites globally. Through the utilization of software architecture cloud technologies and edge computing, the technological developments enable for standardized processes of machine learning applications to influence the strategic optimization of mineral processing. Target objectives of the machine learning optimizations include time savings on mineral processing, production efficiencies, risk identification, and increased production throughput. The data acquired and utilized for predictive modelling is processed through edge computing by resources collectively stored within a data lake. Being involved in the digital transformation has necessitated the standardization software architecture to manage the machine learning models submitted by vendors, to ensure effective automation and continuous improvements to the mineral process models. Operating at scale, the system processes hundreds of gigabytes of data per day from distributed mine sites across the globe, for the purposes of increased improved worker safety, and production efficiency through big data applications.

Keywords: mineral technology, big data, machine learning operations, data lake

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