Search results for: students with learning disabilities

Authors: Stepan Grebeniuk, Ersi Hodo, Henri Ruotsalainen, Paul Tavolato

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Substation security plays an important role in the power delivery system. During the past years, there has been an increase in number of attacks on automation networks of the substations. In spite of that, there hasn’t been enough focus dedicated to the protection of such networks. Aiming to design a specialized anomaly detection system based on machine learning, in this paper we will discuss the IEC 60870-5-104 protocol that is used for communication between substation and control station and focus on the simulation of the substation traffic. Firstly, we will simulate the communication between substation slave and server. Secondly, we will compare the system's normal behavior and its behavior under the attack, in order to extract the right features which will be needed for building an anomaly detection system. Lastly, based on the features we will suggest the anomaly detection system for the asynchronous protocol IEC 60870-5-104.

Keywords: Anomaly detection, IEC-60870-5-104, Machine learning, Man-in-the-Middle attacks, Substation security

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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: Anastasia V. Lazareva

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Nowadays in the light of the evolving multifocal challenges in the sphere of environmental and social difficulties and despite the strong opposition of globalist and anti-globalist movements, we are facing the urgent need of the creation of a vast pool of educated environmentalists through the implementation of relevant university faculties and programs. Considering the threats humanity has tackled these years portrayed in every tiny detail in AGENDA – 2030 –namely, poverty, biodiversity loss, marine and terrestrial pollution, lack of sanitation, and equal rights for all, we must admit that professionals are required to address them all. With this purpose, we have conducted research based on the questionnaires of students, faculty members, and companies’ chief executives and human resources managers on what particular disciplines should be incorporated into the programs in universities and higher institutions to meet the millennium goals and tests. The research is based on the Linkert scale and covers various age groups of students. The topicality of this issue is predetermined by modern reality. The subject of the research is a questionnaire database filled in by 97 students, 17 faculty members of MGIMO University, and 14 companies’ representatives concerning their attitudes towards the implementation of environmental programs of studies in universities and the choice of disciplines required. The study has a limitation -it is based only on one university students' and faculty members’ questionnaires. The methods applied are a questionnaire, content analysis, sampling, and categorization. The findings of this survey imply that all three groups of respondents admit the necessity of implementing environmental programs for studies in higher education. Nevertheless, different groups favor various programs and disciplines to be incorporated into the curriculum.

Keywords: ecology, university studies, environmentalists, education, global challenges

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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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Authors: Zuhairah Abdul Hadi, Mohd Nazir bin Md. Zabit, Zuriadah Ismail

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Metacognitive has been empirically evidenced to be one important element influencing learning outcomes. Expert learners engage in metacognition by monitoring and controlling their thinking, and listing, considering and selecting the best strategies to achieve desired goals. Studies also found that good critical thinkers engage in more metacognition and people tend to activate more metacognition when solving complex problems. This study extends past studies by performing a qualitative analysis to understand metacognitive patterns among two high and two low performing groups by carefully examining video and audio records taken during Problem-based learning activities. High performing groups are groups with majority members scored well in Watson Glaser II Critical Thinking Appraisal (WGCTA II) and academic achievement tests. Low performing groups are groups with majority members fail to perform in the two tests. Audio records are transcribed and analyzed using schemas adopted from past studies. Metacognitive statements are analyzed using three stages model and patterns of metacognitive are described by contexts, components, and levels for each high and low performing groups.

Keywords: academic achievement, critical thinking, metacognitive, problem-based learning

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Authors: Roshanak Khodabakhsh Jolfaei, Javad Akbari Torkestani

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As a cooperative environment for problem-solving, it is necessary that grids develop efficient job scheduling patterns with regard to their goals, domains and structure. Since the Grid environments facilitate distributed calculations, job scheduling appears in the form of a critical problem for the management of Grid sources that influences severely on the efficiency for the whole Grid environment. Due to the existence of some specifications such as sources dynamicity and conditions of the network in Grid, some algorithm should be presented to be adjustable and scalable with increasing the network growth. For this purpose, in this paper a job scheduling algorithm has been presented on the basis of learning automata in computational Grid which the performance of its results were compared with FPSO algorithm (Fuzzy Particle Swarm Optimization algorithm) and GJS algorithm (Grid Job Scheduling algorithm). The obtained numerical results indicated the superiority of suggested algorithm in comparison with FPSO and GJS. In addition, the obtained results classified FPSO and GJS in the second and third position respectively after the mentioned algorithm.

Keywords: computational grid, job scheduling, learning automata, dynamic scheduling

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Authors: Karen Butler

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The LGBTQ youth of color face stigma related to both race and gender identity. Effectively dealing with racial/ethnic discrimination requires strong connections to family and one’s racial/ethnic group. However, LGBTQ youth of color seldom receive support from family, peer groups or church groups. Moreover, ethnic communities often perceive LGBTQ identities as a rejection of ethnic heritage. Thus, stigma places these young people at greater risk for substance use, violence, risky sexual behaviors, suicide, and homelessness. Offering a Queer Studies (QS) class is one way to facilitate a safer and more inclusive environment for LGBTQ students, faculty and staff. The discipline of Queer Studies encompasses theories and thinkers from numerous fields: cultural studies, gay and lesbian studies, race studies, women's studies, media, postmodernism, post-colonialism, psychoanalysis and more. We began our course development by researching existing programs and classes. Several course syllabi were examined and course materials such as readings, videos, and guest speakers were assessed for possible inclusion. We also employed informal survey methods with students and faculty in order to gauge interest in the course. We then developed a sample course syllabus and began the process of new course approval. Feedback thus far indicates that students of various sexual orientations and gender identities are interested in the course and understand the need to offer it; faculty in Psychology, Social Work, and Interdisciplinary Studies are interested in cross-listing the course; library staff is willing to assist with course material acquisition, and the administration is supportive. The purpose of this session is to 1) explore the various health and wellness issues facing LGBTQ students of color and 2) share our experience of developing a QS course in health education in order to address these needs. This process, from initial recognition of the need to a course offering, will be described and discussed in the hopes that participants will increase their awareness of the issues. A QS course would be an appropriate requirement for any number of majors as well as an elective for any major.

Keywords: black colleges, health education, LGBTQ, queer studies

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Authors: Behdoukht Mall Amiri, Zohreh Gheydar

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The thrust of the current study was to investigate the relationship among EFL learners' creativity (CR), emotional intelligence (EI), and self-efficacy (SE). To this end, a group of 120 male and female learners, between the ages of 19 and 35 studying BA in English Translation and MA in Teaching English at Islamic Azad University, Central Tehran were selected using convenient sampling and were given three questionnaires: Bar-On’s EQ-I questionnaire by Bar-On (1997), the General Self-Efficacy Scale questionnaire (SGSES) by Sherer et al. (1982), and a questionnaire of creativity (CR) by O'Neil, Abedi, and Spielberger (1992). Analysis of the results through Pearson Moment Correlation Coefficient showed that there was not a significant relationship between students’ CR and EI, and EI and SE. In addition, CR and SE were correlated significantly but negatively. Multiple regressions revealed that CR could significantly predict SE. Regarding the findings of the study, the obtained results may help EFL teachers, teacher trainers, materials developers, and educational policy makers to possess a broader perspective and heightened degree knowledge toward the TEFL practice and to take practical steps toward the attainments of the desired objectives of the profession.

Keywords: creativity, emotional intelligence, self-efficacy, learning

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Authors: Adam Johnbull

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The paper discusses ICT in Education as a quest for quality learning in the 21st century. Education is the key that unlock the door to development, without adequate education of the citizenry, the development of a nation becomes a sham. Information Communication Technologies (ICTs) has revolutionized the way people work today and are now transforming education systems. As a result, if schools train children in yesterday’s skills and technologies they may not be effective and fit in tomorrow’s world. This is a sufficient reason for ICT’s to win global recognition and attention and thus ensure desire quality in our school system. Thus, the purpose of the paper is to discuss amongst others, what is ICT. The roles of ICT’s in education, limitation and key challenges of integrating ICT to education in the enhancement of student learning and experiences in other to encourage policy makers, school administrators and teachers pay the required attention to integrate this technology in the education system. The paper concludes that regardless of all the limitation characterizing it. ICT benefit education system to provide quality education in the 21st century.

Keywords: ICTs, quest, information, global, sham, century

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Authors: Taylan Kabbani, Ekrem Duman

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The design of adaptive systems that take advantage of financial markets while reducing the risk can bring more stagnant wealth into the global market. However, most efforts made to generate successful deals in trading financial assets rely on Supervised Learning (SL), which suffered from various limitations. Deep Reinforcement Learning (DRL) offers to solve these drawbacks of SL approaches by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. In this paper, a continuous action space approach is adopted to give the trading agent the ability to gradually adjust the portfolio's positions with each time step (dynamically re-allocate investments), resulting in better agent-environment interaction and faster convergence of the learning process. In addition, the approach supports the managing of a portfolio with several assets instead of a single one. This work represents a novel DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem, or what is referred to as The Agent Environment as Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. More specifically, we design an environment that simulates the real-world trading process by augmenting the state representation with ten different technical indicators and sentiment analysis of news articles for each stock. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which can learn policies in high-dimensional and continuous action spaces like those typically found in the stock market environment. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of deep reinforcement learning in financial markets over other types of machine learning such as supervised learning and proves its credibility and advantages of strategic decision-making.

Keywords: the stock market, deep reinforcement learning, MDP, twin delayed deep deterministic policy gradient, sentiment analysis, technical indicators, autonomous agent

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Authors: Sujit K. Basak, Simon Collin

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The aim of this paper is to examine the technology access and use on the affecting social integration of local students at university. This aim is achieved by designing a structural equation modeling (SEM) in terms of integration with peers, integration with faculty, faculty support and on the other hand, examining the socio demographic impact on the technology access and use. The collected data were analyzed using the WarpPLS 5.0 software. This study was survey based and it was conducted at a public university in Canada. The results of the study indicated that technology has a strong impact on integration with faculty, faculty support, but technology does not have an impact on integration with peers. However, the social demographic has also an impact on the technology access and use.

Keywords: faculty, integration, peer, technology access and use

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Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden

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Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.

Keywords: Syndromic surveillance, Tweets, Machine Learning, data mining, Latent Dirichlet allocation (LDA), Influenza

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Authors: Giovanni Cicceri, Roberta Maisano, Nathalie Morey, Salvatore Distefano

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The main goal of this paper is to present a solution for a water purification system based on an Environmental Internet of Things (EIoT) platform to monitor and control water quality and machine learning (ML) models to support decision making and speed up the processes of purification of water. A real case study has been implemented by deploying an EIoT platform and a network of devices, called Gramb meters and belonging to the Gramb project, on wastewater purification systems located in Calabria, south of Italy. The data thus collected are used to control the wastewater quality, detect anomalies and predict the behaviour of the purification system. To this extent, three different statistical and machine learning models have been adopted and thus compared: Autoregressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) autoencoder, and Facebook Prophet (FP). The results demonstrated that the ML solution (LSTM) out-perform classical statistical approaches (ARIMA, FP), in terms of both accuracy, efficiency and effectiveness in monitoring and controlling the wastewater purification processes.

Keywords: environmental internet of things, EIoT, machine learning, anomaly detection, environment monitoring

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Authors: Aysegul Salikutluk, Zehra Altinay, Gokmen Dagli, Fahriye Altinay

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The aim of the study is to reveal the performance of special education schools as regards the service quality and management within the school culture. The project management and school climate are the fundamental elements for the quality in organisations. Having strategic plans, activities and funded projects improve service quality and satisfaction for the families who have children with disabilities. The research has qualitative nature, self-reports were used to examine the perceptions of teachers upon project management and school climate for service quality. The results show that special education schools' teachers are aware of essence of school climate and flow of communication for service quality and project management.

Keywords: disability, education, service quality, project management

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Authors: Amit Kumar, Amanpreet Chander, Ashish Sahani

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This paper presents our recent work on real-time human activity detection based on the media pipe pipeline and machine learning algorithms. The proposed system can detect human activities, including running, jumping, squatting, bending to the left or right, and standing still. This is a robust solution for developing a yoga, dance, metaverse, and fitness application that checks for the correction of the pose without having any additional monitor like a personal trainer. MediaPipe solution offers an open-source cross-platform which utilizes a two-step detector-tracker ML pipeline for live detection of key landmarks on our body which can be used for motion data collection. The prediction of real-time poses uses a variety of machine learning techniques and different types of analysis. Without primarily relying on powerful desktop environments for inference, our method achieves real-time performance on the majority of contemporary mobile phones, desktops/laptops, Python, or even the web. Experimental results show that our method outperforms the existing method in terms of accuracy and real-time capability, achieving an accuracy of 99.92% on testing datasets.

Keywords: human activity detection, media pipe, machine learning, metaverse applications

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the model-agnostic 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 an exponential growth of computation, this paper also proposes a key data extraction method that only extract 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: mutex task generation, data augmentation, meta-learning, text classification.

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Authors: Ali Nadi, Ali Edrissi

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Relief demand and transportation links availability is the essential information that is needed for every natural disaster operation. This information is not in hand once a disaster strikes. Relief demand and network condition has been evaluated based on prediction method in related works. Nevertheless, prediction seems to be over or under estimated due to uncertainties and may lead to a failure operation. Therefore, in this paper a stochastic programming model is proposed to evaluate real-time relief demand and network condition at the onset of a natural disaster. To address the time sensitivity of the emergency response, the proposed model uses reinforcement learning for optimization of the total relief assessment time. The proposed model is tested on a real size network problem. The simulation results indicate that the proposed model performs well in the case of collecting real-time information.

Keywords: disaster management, real-time demand, reinforcement learning, relief demand

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Ehsan Moradi, Parisa Paikhaste, Amir Alam Beigi, Seyedeh Somayeh Bathaei

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The need for entrepreneurial sustainability is as important as the entrepreneurship category itself. By transferring competencies in a sustainable entrepreneurship framework, entrepreneurship education can make a significant contribution to the effectiveness of businesses, especially for start-up entrepreneurs. This study analyzes the essential competencies of students in the development of sustainable entrepreneurship. It is an applied causal study in terms of nature and field in terms of data collection. The main purpose of this research project is to study and explain the dimensions of sustainability entrepreneurship competencies among agricultural students. The statistical population consists of 730 junior and senior undergraduate students of the Campus of Agriculture and Natural Resources, University of Tehran. The sample size was determined to be 120 using the Cochran's formula, and the convenience sampling method was used. Face validity, structure validity, and diagnostic methods were used to evaluate the validity of the research tool and Cronbach's alpha and composite reliability to evaluate its reliability. Structural equation modeling (SEM) was used by the confirmatory factor analysis (CFA) method to prepare a measurement model for data processing. The results showed that seven key dimensions play a role in shaping sustainable entrepreneurial development competencies: systems thinking competence (STC), embracing diversity and interdisciplinary (EDI), foresighted thinking (FTC), normative competence (NC), action competence (AC), interpersonal competence (IC), and strategic management competence (SMC). It was found that acquiring skills in SMC by creating the ability to plan to achieve sustainable entrepreneurship in students through the relevant mechanisms can improve entrepreneurship in students by adopting a sustainability attitude. While increasing students' analytical ability in the field of social and environmental needs and challenges and emphasizing curriculum updates, AC should pay more attention to the relationship between the curriculum and its content in the form of entrepreneurship culture promotion programs. In the field of EDI, it was found that the success of entrepreneurs in terms of sustainability and business sustainability of start-up entrepreneurs depends on their interdisciplinary thinking. It was also found that STC plays an important role in explaining the relationship between sustainability and entrepreneurship. Therefore, focusing on these competencies in agricultural education to train start-up entrepreneurs can lead to sustainable entrepreneurship in the agricultural higher education system.

Keywords: sustainable entrepreneurship, entrepreneurship education, competency, agricultural higher education

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Authors: Mohamed Chabi, Mahmoud I. Syam, Sarah Aw

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In Summer 2012, the Foundation Program Unit of Qatar University has started implementing new ways of teaching Math by introducing MML (MyMathLab) as an innovative interactive tool to support standard teaching. In this paper, we focused on the effect of proper use of the Study Plan component of MML on student’s performance. Authors investigated the results of students of pre-calculus course during Fall 2013 in Foundation Program at Qatar University. The results showed that there is a strong correlation between study plan results and final exam results, also a strong relation between homework results and final exam results. In addition, the attendance average affected on the student’s results in general. Multiple regression is determined between passing rate dependent variable and study plan, homework as independent variable.

Keywords: MyMathLab, study plan, assessment, homework, attendance, correlation, regression

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Authors: Oleg Vlasovets, Fabian Schaipp, Christian L. Mueller

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we conduct a network analysis and predict the sex of 1000 participants based on ”connectome” - pairwise Pearson’s correlation across 436 brain parcels. We solve the non-smooth convex optimization problem, known under the name of Graphical Lasso, where the solution includes a low-rank component. With this solution and machine learning model for a sex prediction, we explain the brain parcels-sex connectivity patterns.

Keywords: network analysis, neuroscience, machine learning, optimization

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Authors: Boon Yih Mah

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Web-based Cognitive Writing Instruction (WeCWI) is a hybrid e-framework that consolidates instructional design and language development towards the development of a web-based instruction (WBI). WeCWI divides instructional design into macro and micro perspectives. In macro perspective, a 21st century educator is encouraged to disseminate knowledge and share ideas with in-class and global learners. By leveraging the virtue of technology, WeCWI aims to transform the educator into an aggregator, curator, publisher, social networker and finally, a web-based instructor. Since the most notable contribution of integrating technology is being a tool of teaching as well as a stimulus for learning, WeCWI focuses on the use of contemporary web tools based on the multiple roles played by the 21st century educator. The micro perspective draws attention to the pedagogical approaches focussing on three main aspects: reading, discussion, and writing. With the effective use of pedagogical approaches, technology adds new dimensions and expands the bounds of learning capacity. Lastly, WeCWI also imparts the fundamental theoretical concepts for web-based instructors’ awareness such as interactionism, e-learning interactional-based model, computer-mediated communication (CMC), cognitive theories, and learning style model.

Keywords: web-based cognitive writing instruction, WeCWI, instructional design, e-framework, web-based instructor

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Authors: Genady Grabarnik, Serge Yaskolko

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Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders.

Keywords: introductory computer and data science education, generative AI, large language models, application of LLMS to computer and data science education

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Authors: M. Zezou

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This paper focuses on the design of a Massive Open Online Course (MOOC) about Content Language Integrated Learning (CLIL) and more specifically about how teachers can use CLIL as an educational approach incorporating technology in their teaching as well. All the four weeks of the MOOC will be presented and a step-by-step analysis of each lesson will be offered. Additionally, the paper includes detailed lesson plans about CLIL lessons with proposed CLIL activities and games in which technology plays a central part. The MOOC is structured based on certain criteria, in order to ensure success, as well as a positive experience that the learners need to have after completing this MOOC. It addresses to all language teachers who would like to implement CLIL into their teaching. In other words, it presents the methodology that needs to be followed so as to successfully carry out a CLIL lesson and achieve the learning objectives set at the beginning of the course. Firstly, in this paper, it is very important to give the definitions of MOOCs and LMOOCs, as well as to explore the difference between a structure-based MOOC (xMOOC) and a connectivist MOOC (cMOOC) and present the criteria of a successful MOOC. Moreover, the notion of CLIL will be explored, as it is necessary to fully understand this concept before moving on to the design of the MOOC. Onwards, the four weeks of the MOOC will be introduced as well as lesson plans will be presented: The type of the activities, the aims of each activity and the methodology that teachers have to follow. Emphasis will be placed on the role of technology in foreign language learning and on the ways in which we can involve technology in teaching a foreign language. Final remarks will be made and a summary of the main points will be offered at the end.

Keywords: CLIL, cMOOC, lesson plan, LMOOC, MOOC criteria, MOOC, technology, xMOOC

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Authors: Migena Mana, Ahmed Khalid Syed, Abdul Malik, Nikhil Cherian

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In recent years, advancements in deep learning enabled researchers to tackle the problem of self-driving cars. Car companies use huge datasets to train their deep learning models to make autonomous cars a reality. However, this approach has certain drawbacks in that the state space of possible actions for a car is so huge that there cannot be a dataset for every possible road scenario. To overcome this problem, the concept of reinforcement learning (RL) is being investigated in this research. Since the problem of autonomous driving can be modeled in a simulation, it lends itself naturally to the domain of reinforcement learning. The advantage of this approach is that we can model different and complex road scenarios in a simulation without having to deploy in the real world. The autonomous agent can learn to drive by finding the optimal policy. This learned model can then be easily deployed in a real-world setting. In this project, we focus on three RL algorithms: Q-learning, Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO). To model the environment, we have used TORCS (The Open Racing Car Simulator), which provides us with a strong foundation to test our model. The inputs to the algorithms are the sensor data provided by the simulator such as velocity, distance from side pavement, etc. The outcome of this research project is a comparative analysis of these algorithms. Based on the comparison, the PPO algorithm gives the best results. When using PPO algorithm, the reward is greater, and the acceleration, steering angle and braking are more stable compared to the other algorithms, which means that the agent learns to drive in a better and more efficient way in this case. Additionally, we have come up with a dataset taken from the training of the agent with DDPG and PPO algorithms. It contains all the steps of the agent during one full training in the form: (all input values, acceleration, steering angle, break, loss, reward). This study can serve as a base for further complex road scenarios. Furthermore, it can be enlarged in the field of computer vision, using the images to find the best policy.

Keywords: autonomous driving, DDPG (deep deterministic policy gradient), PPO (proximal policy optimization), reinforcement learning

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Authors: Rebecca-Lee Kuhnert, Ron Rapee

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Up to 15% of Australian youth will experience an emotional disorder, yet relatively few get the help they need. Schools provide an ideal environment through which we can identify young people who are struggling and provide them with appropriate help. Universal mental health screening is a method by which all young people in school can be quickly assessed for emotional disorders, after which identified youth can be linked to appropriate health services. Despite the obvious logic of this process, universal mental health screening has received little scientific evaluation and even less application in Australian schools. This study will develop methods for Australian education systems to help identify young people (aged 9-17 years old) who are struggling with existing and emerging emotional disorders. Prior to testing, a series of focus groups will be run to get feedback and input from young people, parents, teachers, and mental health professionals. They will be asked about their thoughts on school-based screening methods and and how to best help students at risk of emotional distress. Schools (n=91) across New South Wales, Australia will be randomised to do either immediate screening (in May 2021) or delayed screening (in February 2022). Students in immediate screening schools will complete a long online mental health screener consisting of standard emotional health questionnaires. Ultimately, this large set of items will be reduced to a small number of items to form the final brief screener. Students who score in the “at-risk” range on any measure of emotional health problems will be identified to schools and offered pathways to relevant help according to the most accepted and approved processes identified by the focus groups. Nine months later, the same process will occur among delayed screening schools. At this same time, students in the immediate screening schools will complete screening for a second time. This will allow a direct comparison of the emotional health and help-seeking between youth whose schools had engaged in the screening and pathways to care process (immediate) and those whose schools had not engaged in the process (delayed). It is hypothesised that there will be a significant increase in students who receive help from mental health support services after screening, compared with baseline. It is also predicted that all students will show significantly less emotional distress after screening and access to pathways of care. This study will be an important contribution to Australian youth mental health prevention and early intervention by determining whether school screening leads to a greater number of young people with emotional disorders getting the help that they need and improving their mental health outcomes.

Keywords: children and young people, early intervention, mental health, mental health screening, prevention, school-based mental health

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Authors: Hissa Sadiq, Abdullah Abu-Tineh, Fatma Al-Mutawah, Hamda Al-Sulaiti

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The purpose of this quantitative study was to measure the difference in levels of satisfaction of students, teachers, and parents in schools run by licensed school leaders comparing with schools run by unlicensed school leaders. Data was gathered from 108 school performance reports as published by Ministry of Education and Higher Education for the year 2015-2016. School leaders in 58 participating schools obtained the professional licenses while school leaders in 56 participating schools have no professional licenses. Percentages, standard deviations, and t-tests were used to analyze the data. Results showed that no statistical differences were found in students’ satisfaction between the two school types. However, there were statistical differences in parents and teachers’ satisfaction in the two school types attributed to obtaining the professional license. Teachers and parents of students in schools run by licensed school leaders satisfied more than schools run by unlicensed school leaders. Finally, many recommendations and implications were discussed and proposed. This paper was made possible by NPRP grant # (NPRP7-1224-5-178) from the Qatar national research fund (a member of Qatar Foundation) to Abdullah M. Abu-Tineh. The statements made herein are solely the responsibility of the author

Keywords: professional licenses, Qatari schools, licensure system, satisfaction

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Authors: Tai-Yi Yu, Tai-Kue Yu

Abstract:

Using global climate change as its main theme, this study establishes a model for understanding the environmental behavior of college students. It examines their beliefs about the environment, sustainability, and social impact. Theories about values, beliefs, norms, and planned behaviors helped establish the path relations among various latent variables, which include the students’ values regarding sustainability, environmental concern, social impact, perceived risk, environmental attitude, and behavioral intention. Personality traits were used as moderator variables in order to analyze their role in influencing environmental behaviors. The components-based partial least square (PLS) method was adopted, and the measurements and structural models were analyzed using the SmartPLS software. The proposed model complies with various test standards, including individual item reliability, composite reliability, average variance extracted, goodness-of-fit, and cross-validated redundancy. When college students are taught the concept of environmental sustainability, sustainability becomes an environmental attitude for them, and they are more likely to uphold an ethic of sustainability. The more an individual perceives the risks of global climate change, the stronger her emotional connection to the issue becomes. This positively affects the environmental attitude of college student, pushes them to participate more proactively in improvement activities, and encourages them to display their behavioral intention to improve global climate change. When considering the interaction effect among four latent variables (values regarding sustainability, social impact, environmental concern, and perceived risk), this study found that personality traits have a moderate effect on environmental attitude.

Keywords: partial least square, personality traits, social impact, environmental concern, perceived risk

Procedia PDF Downloads 432

Authors: Macarena-Paz Celume, Sylvie Tordjman

Abstract:

There is still no consensus regarding the differences between gifted and non-gifted students in relationship to their self-esteem and the impact that this might have on behavioral and emotional troubles. In fact, some studies present no difference between both groups or present gifted population having higher scores in self-esteem, while others indicate all the opposite, presenting lower self-esteem in gifted population, suggesting that self-esteem issues are probably due to the fact that gifted children who present low self-esteem might not consider their high Intellectual Quotient (IQ) as a positive characteristic, thus leading to behavioral or emotional troubles. According to the author's knowledge, there is poor evidence trying to understand self-esteem issues in troubled gifted and non-gifted students in France, also finding an important lack regarding the possible moderators that might influence self-esteem. This study aimed to validate the results of these samples, looking for age and sex moderators in order to present recent evidence for the study of self-esteem in troubled gifted students in France. This study analysed the data gathered in the past 12 years for troubled students attending to the National Centre for Assistance to High Potential of Children and Adolescents (CNAHP) in France comparing the results of gifted versus non-gifted population. Primary results showed no significant differences between the groups in global self-esteem (t=1,15 p < .25), consistent with correlation analysis that found no correlation between global self-esteem and total IQ for each of the groups (rgifted=.04, rnon-gifted=.-08). Nevertheless, an ANOVA analysis showed an important effect of giftedness over academic self-esteem even though no significant differences were found (t=1,8 p < .06). No significant differences between sex regarding global self-esteem in any of the groups were found. Nevertheless, non-gifted population showed a significant difference in physical self-esteem, being higher for boys than for girls (t=2.65 p < .01). Sex and age moderator analyses for self-esteem will be presented and discussed.

Keywords: children and adolescents, giftedness, self-esteem, troubled children and adolescents

Procedia PDF Downloads 139

Authors: Mehul Srivastava, Sharma Tushar Ravikant, Mridul Krishn Mishra

Abstract:

In structural design, the evaluation of safety and probability failure of structure is of significant importance, mainly when the variables are random. On real structures, structural reliability can be evaluated obtaining an implicit limit state function. The structural reliability limit state function is obtained depending upon the statistically independent variables. In the analysis of reliability, we considered the statistically independent random variables to be the load intensity applied and the depth or height of the beam member considered. There are many approaches for structural reliability problems. In this paper Extreme Learning Machine technique and First Order Second Moment Method is used to determine the reliability indices for the same set of variables. The reliability index obtained using ELM is compared with the reliability index obtained using FOSM. Higher the reliability index, more feasible is the method to determine the reliability.

Keywords: reliability, reliability index, statistically independent, extreme learning machine

Procedia PDF Downloads 688