Search results for: second language vocabulary learning

Authors: Laurent Thiry, Michel Hassenforder

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This paper presents a general approach to implement efficient queries’ interpreters in a functional programming language. Indeed, most of the standard tools actually available use an imperative and/or object-oriented language for the implementation (e.g. Java for Jena-Fuseki) but other paradigms are possible with, maybe, better performances. To proceed, the paper first explains how to model data structures and queries in a functional point of view. Then, it proposes a general methodology to get performances (i.e. number of computation steps to answer a query) then it explains how to integrate some optimization techniques (short-cut fusion and, more important, data transformations). It then compares the functional server proposed to a standard tool (Fuseki) demonstrating that the first one can be twice to ten times faster to answer queries.

Keywords: data transformation, functional programming, information server, optimization

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Authors: Aregamalage Sujeewa Vijayanthi Polgampala, Fang Huang

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This study explores the genesis and development of concept mapping as a useful tool for science education and its effectiveness as technique for teaching and learning and evaluation for secondary science in schools and the role played by National College of Education science teachers. Concept maps, when carefully employed and executed serves as an integral part of teaching method and measure of effectiveness of teaching and tool for evaluation. Research has shown that science concept maps can have positive influence on student learning and motivation. The success of concept maps played in an instruction class depends on the type of theme selected, the development of learning outcomes, and the flexibility of instruction in providing library unit that is equipped with multimedia equipment where learners can interact. The study was restricted to 6 male and 9 female respondents' teachers in third-year internship pre service science teachers in Gampaha district Sri Lanka. Data were collected through 15 item questionnaire provided to learners and in depth interviews and class observations of 18 science classes. The two generated hypotheses for the study were rejected, while the results revealed that significant difference exists between factors influencing teachers' choice of concept maps, its usefulness and problems hindering the effectiveness of concept maps for teaching and learning process of secondary science in schools. It was examined that concept maps can be used as an effective measure to evaluate students understanding of concepts and misconceptions. Even the teacher trainees could not identify, key concept is on top, and subordinate concepts fall below. It is recommended that pre service science teacher trainees should be provided a thorough training using it as an evaluation instrument.

Keywords: concept maps, evaluation, learning science, misconceptions

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Authors: Bo-Bin Xiaoa, Chia-Yi Liua

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In the task of searching for anomalous radiation sources, personnel holding radiation detectors to search for radiation sources may be exposed to unnecessary radiation risk, and automated search using machines becomes a required project. The research uses various sophisticated algorithms, which are double Q learning, dueling network, and NoisyNet, of deep reinforcement learning to search for radiation sources. The simulation environment, which is a 10*10 grid and one shielding wall setting in it, improves the development of the AI model by training 1 million episodes. In each episode of training, the radiation source position, the radiation source intensity, agent position, shielding wall position, and shielding wall length are all set randomly. The three algorithms are applied to run AI model training in four environments where the training shielding wall is a full-shielding wall, a lead wall, a concrete wall, and a lead wall or a concrete wall appearing randomly. The 12 best performance AI models are selected by observing the reward value during the training period and are evaluated by comparing these AI models with the gradient search algorithm. The results show that the performance of the AI model, no matter which one algorithm, is far better than the gradient search algorithm. In addition, the simulation environment becomes more complex, the AI model which applied Double DQN combined Dueling and NosiyNet algorithm performs better.

Keywords: double Q learning, dueling network, NoisyNet, source searching

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Authors: Nitin Singh, Meng Ling, Talha Ahmed, Tianxia Zhao, Reinier van de Pol

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We propose the use of reinforcement learning (RL) as a viable alternative for optimizing supply chain management, particularly in scenarios with stochasticity in product demands. RL’s adaptability to changing conditions and its demonstrated success in diverse fields of sequential decision-making makes it a promising candidate for addressing supply chain problems. We investigate the impact of demand fluctuations in a multi-product supply chain system and develop RL agents with learned generalizable policies. We provide experimentation details for training RL agents and statistical analysis of the results. We study the generalization ability of RL agents for different demand uncertainty scenarios and observe superior performance compared to the agents trained with fixed demand curves. The proposed methodology has the potential to lead to cost reduction and increased profit for companies dealing with frequent inventory movement between supply and demand nodes.

Keywords: inventory management, reinforcement learning, supply chain optimization, uncertainty

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Authors: Qiao-Yu Warren Cai

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Due to the rise of big data, building corpora and using them to analyze ChineseL2 learners’ language output has become a trend. Various empirical research has been conducted using Chinese corpora built by different academic institutes. However, most of the research analyzed the data in the Chinese corpora usingcorpus-based qualitative content analysis with descriptive statistics. Descriptive statistics can be used to make summations about the subjects or samples that research has actually measured to describe the numerical data, but the collected data cannot be generalized to the population. Comte, a Frenchpositivist, has argued since the 19th century that human beings’ knowledge, whether the discipline is humanistic and social science or natural science, should be verified in a scientific way to construct a universal theory to explain the truth and human beings behaviors. Inferential statistics, able to make judgments of the probability of a difference observed between groups being dependable or caused by chance (Free Geography Notes, 2015)and to infer from the subjects or examples what the population might think or behave, is just the right method to support Comte’s argument in the field of TCSOL. Also, inferential statistics is a core of quantitative research, but little research has been conducted by combing corpora with inferential statistics. Little research analyzes the differences in Chinese L2 learners’ language corpus output errors by using theOne-way ANOVA so that the findings of previous research are limited to inferring the population's Chinese errors according to the given samples’ Chinese corpora. To fill this knowledge gap in the professional development of Taiwanese TCSOL, the present study aims to utilize the One-way ANOVA to analyze corpus output errors of Chinese L2 learners from America, Myanmar, and Singapore. The results show that no significant difference exists in ‘shì (是) sentence’ and word order errors, but compared with Americans and Singaporeans, it is significantly easier for Myanmar to have ‘sentence blends.’ Based on the above results, the present study provides an instructional approach and contributes to further exploration of how Chinese L2 learners can have (and use) learning strategies to lower errors.

Keywords: Chinese corpus, error analysis, one-way analysis of variance, Chinese L2 learners, Americans, myanmar, Singaporeans

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Authors: Ci Lin, Tet Yeap, Iluju Kiringa

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This paper introduces the subspace rotation algorithm (SRA) to train the Restricted Hopfield Network (RHN) as an auto-associative memory. Subspace rotation algorithm is a gradient-free subspace tracking approach based on the singular value decomposition (SVD). In comparison with Backpropagation Through Time (BPTT) on training RHN, it is observed that SRA could always converge to the optimal solution and BPTT could not achieve the same performance when the model becomes complex, and the number of patterns is large. The AUTS case study showed that the RHN model trained by SRA could achieve a better structure of attraction basin with larger radius(in general) than the Hopfield Network(HNN) model trained by Hebbian learning rule. Through learning 10000 patterns from MNIST dataset with RHN models with different number of hidden nodes, it is observed that an several components could be adjusted to achieve a balance between recovery accuracy and noise resistance.

Keywords: hopfield neural network, restricted hopfield network, subspace rotation algorithm, hebbian learning rule

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Authors: Mohammad Al-Dawoody

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This study aims at exploring if priming is affected by gender in Modern Standard Arabic and if it is restricted solely to subjects with no specific language impairment (SLI). The sample in this study consists of 74 subjects, between the ages of 11;1 and 11;10, distributed into (a) 2 SLI experimental groups of 38 subjects divided into two gender groups of 18 females and 20 males and (b) 2 non-SLI control groups of 36 subjects divided into two gender groups of 17 females and 19 males. Employing a mixed research design, the researcher conducted this study within the framework of the relevance theory (RT) whose main assumption is that human beings are endowed with a biological ability to magnify the relevance of the incoming stimuli. Each of the four groups was given two different priming stimuli: audio-visual priming (T1) and syntactic priming (T2). The results showed that the priming effect was sheer distinct among SLI participants especially when retrieving typical responses (TR) in T1 and T2 with slight superiority of males over females. The results also revealed that non-SLI females showed stronger original response (OR) priming in T1 than males and that non-SLI males in T2 excelled in OR priming than females. Furthermore, the results suggested that the audio-visual priming has a stronger effect on SLI females than non-SLI females and that syntactic priming seems to have the same effect on the two groups (non-SLI and SLI females). The conclusion is that the priming effect varies according to gender and is not confined merely to non-SLI subjects.

Keywords: specific language impairment, relevance theory, audio-visual priming, syntactic priming, modern standard Arabic

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Authors: AnjalI A. Prasad

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Nowadays, social media played an important role in spreading misinformation or fake news. This study analyzes the fake news related to the COVID-19 pandemic spread in social media. This paper aims at evaluating and comparing different approaches that are used to mitigate this issue, including popular deep learning approaches, such as CNN, RNN, LSTM, and BERT algorithm for classification. To evaluate models’ performance, we used accuracy, precision, recall, and F1-score as the evaluation metrics. And finally, compare which algorithm shows better result among the four algorithms.

Keywords: BERT, CNN, LSTM, RNN

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Authors: Lassaad Smirani

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In this paper we propose a Neurosymbolic Learning System (NLS) as a channel estimator for Long Term Evolution Advanced (LTE-A) uplink. The proposed system main idea based on Neural Network has modules capable of performing bidirectional information transfer between symbolic module and connectionist module. We demonstrate various strengths of the NLS especially the ability to integrate theoretical knowledge (rules) and experiential knowledge (examples), and to make an initial knowledge base (rules) converted into a connectionist network. Also to use empirical knowledge witch by learning will have the ability to revise the theoretical knowledge and acquire new one and explain it, and finally the ability to improve the performance of symbolic or connectionist systems. Compared with conventional SC-FDMA channel estimation systems, The performance of NLS in terms of complexity and quality is confirmed by theoretical analysis and simulation and shows that this system can make the channel estimation accuracy improved and bit error rate decreased.

Keywords: channel estimation, SC-FDMA, neural network, hybrid system, BER, LTE-A

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Authors: Hoi Nga Ng, Kam Weng Boey, Chi Wai Kwan

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Supporters of Inclusive education proclaim that all students, regardless of disabilities or special educational needs (SEN), have the right to study in the normal school setting. It is asserted that students with SEN would benefit in academic performance and psychosocial adjustment via participation in common learning activities within the ordinary school system. When more and more students of SEN completed their early schooling, institute of higher education become the setting where students of SEN continue their learning. This study aimed to investigate the school well-being, social relationship, and academic self-concept of students of SEN in higher education. The Perception of Inclusion Questionnaire (PIQ) was used as the measuring instruments. PIQ was validated and incorporated in a questionnaire designed for online survey. Participation was voluntary and anonymous. A total of 90 students with SEN and 457 students without SEN responded to the online survey. Results showed no significant differences in school well-being and social relationship between students with and without SEN, but students with SEN, particularly those with learning and development impairment and those with mental illness and emotional problems, were significantly poorer in academic self-concept. Implications of the findings were discussed.

Keywords: ccademic self-concept, school well-being, social relationship, special educational needs

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Authors: Eun Kyong Park

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This study explores the relationships between immigrant parents’ beliefs about bilingualism, family literacy practices, and their children’s identity development in Sydney, Australia. This project examines how these parents’ ideological beliefs and knowledge are related to their provision of family literacy practices and management of the environment for their bilingual children based on family language policy (FLP). This is a follow-up study of the author’s prior thesis that presented Korean immigrant mothers’ beliefs and decision-making in support of their children’s bilingualism. It includes fathers’ perspectives within the participating families as a whole by foregrounding their perceptions of bilingual and identity development. It adopts a qualitative approach with twelve immigrant mothers and fathers living in a Korean-Australian community whose child attends one of the communities Korean language programs. This time, it includes introspective and self-evocative auto-ethnographic data. The initial data set collected from the first part of this study demonstrated the mothers provided rich, diverse, and specific family literacy activities for their children. These mothers selected specific practices to facilitate their child’s bilingual development at home. The second part of data has been collected over a three month period: 1) a focus group interview with mothers; 2) a brief self-report of fathers; 3) the researcher’s reflective diary. To analyze these multiple data, thematic analysis and coding were used to reveal the parents’ ideologies surrounding bilingualism and bilingual identities. It will highlight the complexity of language and literacy practices in the family domain interrelated with sociocultural factors. This project makes an original contribution to the field of bilingualism and FLP and a methodological contribution by introducing auto-ethnographic input of this community’s lived practices. This project will empower Korean-Australian immigrant families and other multilingual communities to reflect their beliefs and practices for their emerging bilingual children. It will also enable educators and policymakers to access authentic information about how bilingualism is practiced within these immigrant families in multiple ways and to help build the culturally appropriate partnership between home and school community.

Keywords: bilingualism, beliefs, identity, family language policy, Korean immigrant parents in Australia

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Authors: Malik Ghulam Behlol, Alison Fox, Faiza Masood, Sabiha Arshad

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This paper explores the barriers to the application of learning-supportive assessment at teaching practicum while investigating the role of university teachers (UT), cooperative teachers (CT), prospective teachers ( PT) and heads of the practicum schools (HPS) in the selected universities of Pakistan and the UK. It is a qualitative case study and data were collected through the lesson observation of UT in the pre-service teacher education setting and PT in practicum schools. Interviews with UT, HPS, and Focus Group Discussions with PT were conducted too. The study has concluded that as compared to the UK counterpart, PT in Pakistan faces significant barriers in applying learning-supportive assessment in the school practicum settings because of large class sizes, lack of institutionalised collaboration between universities and schools, poor modelling of the lesson, ineffective feedback practices, lower order thinking assignments, and limited opportunities to use technology in school settings.

Keywords: assessment, pre-service teacher education, theory-practice gap, teacher education

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Authors: Fernanda T. Orsati, Julie Causton

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Discursive practices enacted by educators in kindergarten create a blueprint for how the educational trajectories of students with disabilities are constructed. This two-year ethnographic case study critically examine educators’ relationships with students considered to present challenging behaviors in one kindergarten classroom located in a predominantly White middle-class school district in the Northeast of the United States. Focusing on the language and practices used by one special education teacher and three teaching assistants, this paper analyzes how teacher responses to students’ behaviors constructs and positions students over one year of kindergarten education. Using a critical discourse analysis, it shows that educators understand students’ behaviors as a deficit and needing consequences. This study highlights how educators’ responses reflect students' individual characteristics including family background, socioeconomics and ability status. This paper offers in-depth analysis of two students’ stories, which evidenced that the language used by educators amplifies the social positioning of students within the classroom and creates a foundation for who they are constructed to be. Through exploring routine language and practices, this paper demonstrates that educators outlined a blueprint of kindergartners, which positioned students as learners in ways that became the ground for either a limited or a promising educational pathway for them.

Keywords: behavior, early education, special education, critical discourse analysis

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Authors: Tina Stavredes

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Increasingly borderless learning opportunities including online learning are expanding. Singapore University of Social Science (SUSS) conducted research in February of 2017 to determine the level of consumer interest in undertaking a completely online distance learning degree program across three countries in the Asian Pacific region. The target audience was potential bachelor degree and post-degree students from Malaysia, Indonesia, and Vietnam. The results gathered were used to assess the market size and ascertain the business potential of online degree programs in Malaysia, Indonesia and Vietnam. Secondly, the results were used to determine the most receptive markets to prioritise entry and identify the most receptive student segments. In order to achieve the key outcomes, the key points of understanding were as follows: -Motivations for higher education & factors that influence the choice of institution, -Interest in online learning, -Interest in online learning from a Singapore university relative to other foreign institutions, -Key drivers and barriers of interest in online learning. An online survey was conducted from from 7th Feb 2017 to 27th Feb 2017 amongst n=600 respondents aged 21yo-45yo, who have a basic command of English, A-level qualifications and above, and who have an intent to further their education in the next 12 months. Key findings from the study regarding enrolling in an online program include the need for a marriage between intrinsic and extrinsic motivation factors and the flexibility and support offered in an online program. Overall, there was a high interest for online learning. Survey participants stated they are intrinsically motivated to learn because of their interest in the program of study and the need for extrinsic rewards including opportunities for employment or salary increment in their current job. Seven out of ten survey participants reported they are motivated to further their education and expand their knowledge to become more employable. Eight in ten claims that the feasibility of furthering their education depends on cost and maintaining a work-life balance. The top 2 programs of interest are business and information and communication technology. They describe their choice of university as a marriage of both motivational and feasibility factors including cost, choice, quality of support facilities, and the reputation of the institution. Survey participants reported flexibility as important and stated that appropriate support assures and grows their intent to enrol in an online program. Respondents also reported the importance of being able to work while studying as the main perceived advantage of online learning. Factors related to the choice of an online university emphasized the quality of support services. Despite concerns, overall there was a high interest for online learning. One in two expressed strong intent to enrol in an online programme of study. However, unfamiliarity with online learning is a concern including the concern with the lack of face-to-face interactions. Overall, the findings demonstrated an interest in online learning. A main driver was the ability to earn a recognised degree while still being able to be with the family and the ability to achieve a ‘better’ early career growth.

Keywords: distance education, student motivations, online learning, online student needs

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Authors: Leigh Ann Wilson, Melanie Borrego

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The purpose of institutional learning outcomes (ILOs) is to assess what students across the university know and what they do not. The issue is gathering this information in a systematic and usable way. This presentation will explain how one institution has engineered this process for both student success and maximum faculty curriculum and course design input. At Brandman University, there are three levels of learning outcomes: course, program, and institutional. Institutional Learning Outcomes (ILOs) are mapped to specific courses. Faculty course developers write the signature assignments (SAs) in alignment with the Institutional Learning Outcomes for each course. These SAs use a specific rubric that is applied consistently by every section and every instructor. Each year, the 12-member General Education Team (GET), as a part of their work, conducts the calibration and assessment of the university-wide SAs and the related rubrics for one or two of the five ILOs. GET members, who are senior faculty and administrators who represent each of the university's schools, lead the calibration meetings. Specifically, calibration is a process designed to ensure the accuracy and reliability of evaluating signature assignments by working with peer faculty to interpret rubrics and compare scoring. These calibration meetings include the full time and adjunct faculty members who teach the course to ensure consensus on the application of the rubric. Each calibration session is chaired by a GET representative as well as the course custodian/contact where the ILO signature assignment resides. The overall calibration process GET follows includes multiple steps, such as: contacting and inviting relevant faculty members to participate; organizing and hosting calibration sessions; and reviewing and discussing at least 10 samples of student work from class sections during the previous academic year, for each applicable signature assignment. Conversely, the commitment for calibration teams consist of attending two virtual meetings lasting up to three hours in duration. The first meeting focuses on interpreting the rubric, and the second meeting involves comparing scores for sample work and sharing feedback about the rubric and assignment. Next, participants are expected to follow all directions provided and participate actively, and respond to scheduling requests and other emails within 72 hours. The virtual meetings are recorded for future institutional use. Adjunct faculty are paid a small stipend after participating in both calibration meetings. Full time faculty can use this work on their annual faculty report for "internal service" credit.

Keywords: assessment, assurance of learning, course design, institutional learning outcomes, rubrics, signature assignments

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: Pirada Techaratpong

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This qualitative research has 2 objectives: to study marketing management of the cultural learning center in Suansunandha Rajabhat University and to suggest guidelines to improve its marketing management. This research is based on a case study of the Arts and Culture Office in Suansunandha Rajabhat University, Bangkok. This research found the Art and Culture Office has no formal marketing management. However, the marketing management is partly covered in the overall business plan, strategic plan, and action plan. The process can be divided into 5 stages. The marketing concept has long been introduced to its policy but not apparently put into action due to inflexible system. Some gaps are found in the process. The research suggests the Art and Culture Office implement the concept of marketing orientation, meeting the needs and wants of its target customers and adapt to the changing situation. Minor guidelines for improvement are provided.

Keywords: cultural learning center, marketing, management, museum

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Authors: Xian Xie, Qinghua Fang

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This study used quantitative and qualitative methods to explore the characteristics of flipped classroom’s influence on classroom environment of college English reading, writing, and translating, and to summarize and reflect on the teaching characteristics of college English Reading, writing, and translating. The results of the study indicated that after the flipped classroom applied to reading, writing, and translating, students’ performance was improved to a certain extent, the classroom environment was improved to some extent, students of the flipped classroom are generally satisfied with the classroom environment; students showed a certain degree of individual differences to the degree of cooperation, participation, self-responsibility, task-orientation, and the teacher leadership and innovation. The study indicated that the implementation of flipped classroom teaching mode can optimize College English reading, writing, and translating classroom environment and realize target－learner as the center in foreign language teaching and learning, but bring a greater challenge to teachers.

Keywords: classroom environment, college English reading, writing and translating, individual differences, flipped classroom

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Authors: Pan Long, Bi Dongjie, Li Xifeng, Xie Yongle

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The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals.

Keywords: complex-valued signal processing, synthetic aperture radar, 2-D radar imaging, compressive sensing, sparse Bayesian learning

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Authors: M. Roberts Masillamani

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There is a phenomenal difference, as regard to the teaching methodology adopted at the urban and the rural area colleges. However, bright and talented student may be from rural back ground even. But there is huge dearth of the digitization in the rural areas and lesser developed countries. Today’s students need new skills to compete and successful in the future. Education should be combination of practical, intellectual, and social skills. What does this mean for rural classrooms and how can it be achieved. Rural colleges are not able to hire the best resources, since the best teacher’s aim is to move towards the city. If city is provided everywhere, then there will be no rural area. This is possible by forming university clusters (UC). The University cluster is a group of renowned and accredited universities coming together to bridge this dearth. The UC will deliver the live lectures and allow the students’ from remote areas to actively participate in the classroom. This paper tries to present a plan of action of providing a better live classroom teaching and learning system from the city to the rural and the lesser developed countries. This paper titled “University Clusters using ICT for teaching and learning” provides a true concept of opening live digital classroom windows for rural colleges, where resources are not available, thus reducing the digital divide. This is different from pod casting a lecture or distance learning and eLearning. The live lecture can be streamed through digital equipment to another classroom. The rural students can collaborate with their peers and critiques, be assessed, collect information, acquire different techniques in assessment and learning process. This system will benefit rural students and teachers and develop socio economic status. This will also will increase the degree of confidence of the Rural students and teachers. Thus bringing about the concept of ‘Train the Trainee’ in reality. An educational university cloud for each cluster will be built remote infrastructure facilities (RIF) for the above program. The users may be informed, about the available lecture schedules, through the RIF service. RIF with an educational cloud can be set by the universities under one cluster. This paper talks a little more about University clusters and the methodology to be adopted as well as some extended features like, tutorial classes, library grids, remote laboratory login, research and development.

Keywords: lesser developed countries, digital divide, digital learning, education, e-learning, ICT, library grids, live classroom windows, RIF, rural, university clusters and urban

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Authors: H. J. Joshi, Satyajeet Patil, Parth Dandavate, Mihir Kulkarni, Harshita Agrawal

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As the world looks towards a sustainable future, electric vehicles have become increasingly popular. Millions worldwide are looking to switch to Electric cars over the previously favored combustion engine-powered cars. This demand has seen an increase in Electric Vehicle Charging Stations. The big challenge is that the randomness of electrical energy makes it tough for these charging stations to provide an adequate amount of energy over a specific amount of time. Thus, it has become increasingly crucial to model these patterns and forecast the energy needs of power stations. This paper aims to analyze how different machine learning models perform on Electric Vehicle charging time-series data. The data set consists of authentic Electric Vehicle Data from the Netherlands. It has an overview of ten thousand transactions from public stations operated by EVnetNL.

Keywords: forecasting, smart grid, electric vehicle load forecasting, machine learning, time series forecasting

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Authors: Silka Abyadati, Dadi Rusdiana, Enjang Akhmad Juanda

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This study aims to investigate the students’ mastery concepts enhancement between students who are studying by using Integrated Real-Life Video and Animation (IRVA) and students who are studying without using IRVA. The development of IRVA is conducted by five stages: Analyze, Design, Development, Implementation and Evaluation (ADDIE) based on constructivist for Rotational Dynamics material in Physics learning. A constructivist model-based learning used is Interpretation Construction (ICON), which has the following phases: 1) Observation, 2) Construction interpretation, 3) Contextualization prior knowledge, 4) Conflict cognitive, 5) Learning cognitive, 6) Collaboration, 7) Multiple interpretation, 8) Multiple manifestation. The IRVA is developed for the stages of observation, cognitive conflict and cognitive learning. The sample of this study consisted of 32 students experimental group and a control group of 32 students in class XI of the school year 2015/2016 in one of Senior High Schools Bandung. The study was conducted by giving the pretest and posttest in the form of 20 items of multiple choice questions to determine the enhancement of mastery concept of Rotational Dynamics. Hypothesis testing is done by using T-test on the value of N-gain average of mastery concepts. The results showed that there is a significant difference in an enhancement of students’ mastery concepts between students who are studying by using IRVA and students who are studying without IRVA. Students in the experimental group increased by 0.468 while students in the control group increased by 0.207.

Keywords: ADDIE, constructivist learning, Integrated Real-Life Video and Animation, mastery concepts, rotational dynamics

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Authors: Suheyla Demi̇rkol Orak

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The requirement of inclusive education is one of the utmost important results of both regular and irregular immigration. The matter in the case of Syrian immigrants is even worse than the other immigrants cases in world history since a massive immigration wave has affected all world countries' socio-economic profiles. When Syrians immigrated from Syria all over the world, they aimed to survive and left behind the war, but surviving is not optional occasion without handling language-related problems. Humans exist and preserve their existence with their language. That is a matter of concern for the integration of Syrians into the hosting countries. Many countries are proceeding with various programs to integrate Syrians into the majority groups by either assimilation or adaptation policies. Turkey has got the lion's share of the Syrian immigration apple, and in the same vein with this situation, its language education system should be analyzed severely in order to come up with a perfect match program for the integration of Syrians. It aimed to generate an inclusive education model for catalyzing the integration process of immigrant Syrian students into the majority socio-economic group via overcoming the language barrier. The identity of the immigrants is prioritized. The study follows a narrative literature review, which aims to review and critique relevant literature and offers a new conceptualization derived from the previous literature. The study derives a critical localized bilingual education model. As the outcome of the narrative literature review, a bilingual education model which prioritized the identity of the target community was designed. In the present study, main bilingual education programs and most of the countries' bilingual education policies were reviewed critically and suggestions were listed for the Syrian immigrants dominantly in Turkey and suggested to be benefitted by the other countries through localizing the practices.

Keywords: bi/multilingual education, sheltered education, immigrants, glocalization, submersion program, immersion program

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Authors: Paola Utreras, Yazmina Olmos, Loreto Sanhueza

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This work shows how the learning of physics is enriched with scientific inquiry practices, achieving learning that results in the use of higher-level cognitive skills. The activities, which were carried out with students of the 3rd semester of the courses of the Faculty of Sciences of the Engineering of the Austral University of Chile, focused on the understanding of the nature of the thermodynamic variables and how they relate to each other. This, through the analysis of atmospheric data obtained in the meteorological station Miraflores, located on the campus. The proposed activities consisted of the elaboration of time series, linear analysis of variables, as well as the analysis of frequencies and periods. From their results, the students reached conclusions associated with the nature of the thermodynamic variables studied and the relationships between them, to finally make public their results in a report using scientific writing standards. It is observed that introducing topics that are close to them, interesting and which affect their daily lives allows a better understanding of the subjects, which is reflected in higher levels of approval and motivation for the subject.

Keywords: basic sciences, inquiry-based learning, scientific inquiry, thermodynamics

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Authors: Deyu Yuan

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Discourse analysis as an interdisciplinary approach has more than 60-years-history since it was first named by Zellig Harris in 'Discourse Analysis' on Language in 1952. Ferdinand de Saussure differentiated the 'parole' from the 'langue' that established the principle of focusing on language but not speech. So the rising of discourse analysis can be seen as a discursive turn for the entire language research that closely related to the theory of Speech act. Critical discourse analysis becomes the mainstream of contemporary language research through drawing upon M. A. K. Halliday's socio-semiotic theory and Foucault, Barthes, Bourdieu's views on the sign, discourse, and ideology. So in contrast to general semiotics, social semiotics mainly focuses on parole and the application of semiotic theories to some applicable fields. The article attempts to discuss this applicable sociosemiotics and show the features of it that differ from the Saussurian and Peircian semiotics in four aspects: 1) the sign system is about meaning-generation resource in the social context; 2) the sign system conforms to social and cultural changes with the form of metaphor and connotation; 3) sociosemiotics concerns about five applicable principles including the personal authority principle, non-personal authority principle, consistency principle, model demonstration principle, the expertise principle to deepen specific communication; 4) the study of symbolic functions is targeted to the characteristics of ideational, interpersonal and interactional function in social communication process. Then the paper describes six features which characterize this sociosemiotics as applicable semiotics: social, systematic, usable interdisciplinary, dynamic, and multi-modal characteristics. Thirdly, the paper explores the multi-modal choices of sociosemiotics in the respects of genre, discourse, and style. Finally, the paper discusses the relationship between theory and practice in social semiotics and proposes a relatively comprehensive theoretical framework for social semiotics as applicable semiotics.

Keywords: discourse analysis, sociosemiotics, pragmatics, ideology

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Authors: Chassel T. Paras, Tryxzy Q. Dela Cruz, Ma. Carmela Lousie V. Goingco, Pauline L. Tolentino, Carmela S. Dizon

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In implementing inclusive education, parental involvement is measured to be an irreplaceable contributing factor. Parental involvement is described as an indispensable aspect of the teaching-learning process and has a remarkable effect on the student's academic performance. However, there are still differences in the viewpoints, expectations, and needs of both parents and teachers that are not yet fully conveyed in their relationship; hence, the perceptions of SNED teachers are essential in their collaboration with parents. This qualitative study explored how SNED teachers perceive the roles of parental involvement in the teaching-learning process of students with special needs. To answer this question, one-on-one face-to-face semi-structured interviews with three SNED teachers in a selected public school in Angeles City, Philippines, that offer special needs education services were conducted. The gathered data are then analyzed using Interpretative Phenomenological Analysis (IPA). The results revealed four superordinate themes, which include: (1) roles of parental involvement, (2) parental involvement opportunities, (3) barriers to parental involvement, and (4) parent-teacher collaboration practices. These results indicate that SNED teachers are aware of the roles and importance of parental involvement; however, despite parent-teacher collaboration, there are still barriers that impede parental involvement. Also, SNED teachers acknowledge the big roles of parents as they serve as main figures in the teaching-learning process of their children with special needs. Lastly, these results can be used as input in developing a school-facilitated parenting involvement framework that encompasses the contribution of SNED teachers in planning, developing, and evaluating parental involvement programs, which future researchers can also use in their studies

Keywords: parental involvement, special needs education, teaching-learning process, teachers’ perceptions, special needs education teachers, interpretative phenomenological analysis

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Authors: Yun-Ju Chiu, Feng-Yi Chen

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Kinematics is the beginning to learn mechanics in physics course. The concept of acceleration plays an important role in learning kinematics. Teachers usually instruct the conception through the formulas and graphs of kinematics and the well-known law F = ma. However, over the past few decades, a lot of researchers reveal numerous students’ difficulties in learning acceleration. One of these difficulties is that students frequently confuse acceleration with velocity and force. Why is the concept of acceleration so difficult to learn? The aim of this study is to understand the conceptual evolution of acceleration through the historical textual analysis. Text analysis and one-to-one interviews with high school students and teachers are used in this study. This study finds the history of science constructed from textbooks is usually quite different from the real evolution of history. For example, most teachers and students believe that the best-known law F = ma was written down by Newton. The expression of the second law is not F = ma in Newton’s best-known book Principia in 1687. Even after more than one hundred years, a famous Cambridge textbook titled An Elementary Treatise on Mechanics by Whewell of Trinity College did not express this law as F = ma. At that time of Whewell, the early mid-nineteenth century Britain, the concept of acceleration was not only ambiguous but also confused with the concept of force. The process of learning the concept of acceleration is analogous to its conceptual development in history. The study from the perspective of historical textual analysis will promote the understanding of the concept learning difficulties, the development of professional physics teaching, and the improvement of the context of physics textbooks.

Keywords: acceleration, textbooks, mechanics, misconception, history of science

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Authors: Almutasim Billa A. Alanazi, Hal S. Tharp

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Safe performance and efficient energy consumption are essential factors for designing a control system. This paper presents a reinforcement learning (RL) model that can be applied to control applications to improve safety and reduce energy consumption. As hardware constraints and environmental disturbances are imprecise and unpredictable, conventional control methods may not always be effective in optimizing control designs. However, RL has demonstrated its value in several artificial intelligence (AI) applications, especially in the field of control systems. The proposed model intelligently monitors a system's success by observing the rewards from the environment, with positive rewards counting as a success when the controlled reference is within the desired operating zone. Thus, the model can determine whether the system is safe to continue operating based on the designer/user specifications, which can be adjusted as needed. Additionally, the controller keeps track of energy consumption to improve energy efficiency by enabling the idle mode when the controlled reference is within the desired operating zone, thus reducing the system energy consumption during the controlling operation. Water temperature control for a hydroponic system is taken as a case study for the RL model, adjusting the variance of disturbances to show the model’s robustness and efficiency. On average, the model showed safety improvement by up to 15% and energy efficiency improvements by 35%- 40% compared to a traditional RL model.

Keywords: control system, hydroponics, machine learning, reinforcement learning

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Authors: Ammar Alali, Mahmoud Abughaban

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Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

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Authors: A. Nawaz

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The present study uses Halliday’s transitivity model to analyze and interpret ideological stance in PM Nawaz Sharif’s political discourse. His famous speech at the 71st UN assembly was analyzed qualitatively using clausal analysis approach to investigate the communicative functions of the linguistic choices made in the address. The study discovers that among the six process types under the transitivity model, material, relational and mental processes appear most frequently in the speech, making up almost 86% of the whole. Verbal processes rank 4th, whereas existential and behavioral are the least occurring processes covering only 2 and 1 percent respectively. The dominant use of material processes suggests that Nawaz Sharif and his government are the main actors working on several concrete projects to produce a sense of developmental progression and continuity. Using relational and mental processes the PM, along with establishing proximity with masses and especially Kashmiri, gives guarantees and promises. The linguistic analysis concludes Kashmir dispute as being the central theme of the address, since it covers more than half of the discourse. The address calls for a strong action instead of formal assurances and wishful thoughts. The study establishes that language structures can yield certain connotations and ideologies which are not overt for readers. This is in affirmation to the supposition that language form performs a communicative function and is not merely fortuitous.

Keywords: Hallidian perspective on language, implicit meanings, Nawaz Sharif, political ideologies, political speeches, transitivity, UN Assembly

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