Search results for: language learning model

Authors: Teerada Apibunyopas, Nithinant Thammakoranonta

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Many organizations bring e-Learning to use as a tool in their training and human development department. It is getting more popular because it is easy to access to get knowledge all the time and also it provides a rich content, which can develop the employees skill efficiently. This study focused on the factors that affect using e-Learning efficiently, so it will make job satisfaction increased. The questionnaires were sent to employees in large commercial banks, which use e-Learning located in Bangkok, the results from multiple linear regression analysis showed that employee’s characteristics, characteristics of e-Learning, learning and growth have influence on job satisfaction.

Keywords: e-Learning, job satisfaction, learning and growth, Bangkok

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Authors: Farouk Lawan Gambo, Hamada Mohammad

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Data compression shrinks files into fewer bits than their original presentation. It has more advantage on the internet because the smaller a file, the faster it can be transferred but learning most of the concepts in data compression are abstract in nature, therefore, making them difficult to digest by some students (engineers in particular). This paper studies the learning preference of engineering students who tend to have strong, active, sensing, visual and sequential learning preferences, the paper also studies the three shift of technology-aided that learning has experienced, which mobile learning has been considered to be the feature of learning that will integrate other form of the education process. Lastly, we propose a design and implementation of mobile learning application using software engineering methodology that will enhance the traditional teaching and learning of data compression techniques.

Keywords: data compression, learning preference, mobile learning, multimedia

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Authors: Hanan Abufares Elkhimry

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Looking for more effective teaching and learning approaches, teaching instructors have been telling trainee teachers to decrease their talking time, but the problem is how best to do this. Doing classroom research, specifically in the area of teacher talking time (TTT), is worthwhile, as it could improve the quality of teaching languages, as the learners are the ones who should be practicing and using the language. This work hopes to ascertain if teachers consider this need in a way that provides the students with the opportunities to increase their production of language. This is a question that is worthwhile answering. As many researchers have found, TTT should be decreased to 30% of classroom talking time and STT should be increased up to 70%. Other researchers agree with this, but add that it should be with awareness of the quality of teacher talking time. Therefore, this study intends to investigate the balance between quantity and quality of teacher talking time in the EFL classroom. For this piece of research and in order to capture the amount of talking in a four classrooms. The amount of talking time was measured. A Checklist was used to assess the quality of the talking time In conclusion, In order to improve the quality of TTT, the results showed that teachers may use more or less than 30% of the classroom talking time and still produce a successful classroom learning experience. As well as, the important factors that can affect TTT is the English level of the students. This was clear in the classroom observations, where the highest TTT recorded was with the lowest English level group.

Keywords: teacher talking time TTT, learning experience, classroom research, effective teaching

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Authors: Kevin Chan, Kevin K. H. Chung, Patcy P. S. Yeung, H. L. Ip, Bill T. C. Chung, Karen M. K. Chung

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Purpose: Recent evidence shows that reading self-efficacy and students perceived difficulty in reading are significantly associated with word reading and reading fluency. However, little is known about these relationships among students learning to read English as a second language, particularly in Chinese students. This study examined the contributions of reading self-efficacy, perception of difficulty in reading, and cognitive-linguistic skills to performance on English word reading and reading fluency in Chinese students. Method: A sample of 122 second-and third-grade students in Hong Kong, China, participated in this study. Students completed the measures of reading self-efficacy and perception of difficulty in reading. They were assessed on their English cognitive-linguistic and reading skills: rapid automatized naming, nonword reading, phonological awareness, word reading, and one-minute word reading. Results: Results of path analysis indicated that when students’ grades were controlled, reading self-efficacy was a significant correlate of word reading and reading fluency, whereas perception of difficulty in reading negatively predicted word reading. Conclusion: These findings underscore the importance of taking students’ reading self-efficacy and perception of difficulty in reading and their cognitive-linguistic skills into consideration when designing reading intervention and instructions for students learning English as a second language.

Keywords: self-efficacy, perception of difficulty in reading, english as a second language, word reading

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Authors: Anne Crescini

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The number of loanwords borrowed into the Japanese language is increasing rapidly in recent years, and many linguists argue that loanwords make up more than 10% of the Japanese lexicon. While these loanwords come from various Western languages, 80%-90% are borrowed from English. Also, there is a separate group of words and phrases categorized as ‘Japanese English’. These made-in-Japan linguistic creations may look and sound like English, but in fact are not used by native speakers and are often incomprehensible to them. Linguistically, the important thing to remember is that these terms are not English ones, but in fact, 100% Japanese words. A problem arises in language teaching, however, when Japanese English learners are unable to distinguish authentic loans from Japanese English terms. This confusion could greatly impede language acquisition and communication. The goal of this paper is to determine to what degree this potential misunderstanding may interfere with communication. Native English speakers living in the United States were interviewed and shown a list of romanized Japanese English terms, which are both commonly used and often mistaken for authentic loans. Then, the words were put into the context of a sentence in order to ascertain if context in any way aided comprehension. The results showed that while some terms are understood on their own, and others are understood better in context, a large number of the terms are entirely incomprehensible to native English speakers. If that is the case, and a Japanese learner mistakes a Japanese English term for an authentic loan, a communication breakdown may occur during interaction in English. With the ever-increasing presence of both groups of terms in the Japanese language, it is more important than ever that teaching professionals address this topic in the language classroom.

Keywords: Japanese, Japanese English, language acquisition, loanwords

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Authors: Hy Dang, Bo Mei

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The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: classification, machine learning, time representation, stock prediction

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Authors: Medjadj Tarek, Ghribi Hayet

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This study aims to develop a flood sensitivity assessment tool using machine learning (ML) techniques and geographic information system (GIS). The importance of this study is integrating the geographic information systems (GIS) and machine learning (ML) techniques for mapping flood risks, which help decision-makers to identify the most vulnerable areas and take the necessary precautions to face this type of natural disaster. To reach this goal, we will study the case of the city of M'sila, which is among the areas most vulnerable to floods. This study drew a map of flood-prone areas based on the methodology where we have made a comparison between 3 machine learning algorithms: the xGboost model, the Random Forest algorithm and the K Nearest Neighbour algorithm. Each of them gave an accuracy respectively of 97.92 - 95 - 93.75. In the process of mapping flood-prone areas, the first model was relied upon, which gave the greatest accuracy (xGboost).

Keywords: Geographic information systems (GIS), machine learning (ML), emergency mapping, flood disaster management

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Authors: Manik Sharma, Ganapathy Krishnamurthi

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The cost of some drugs and medical treatments has risen in recent years, that many patients are having to go without. A classification project could make researchers more efficient. One of the more surprising reasons behind the cost is how long it takes to bring new treatments to market. Despite improvements in technology and science, research and development continues to lag. In fact, finding new treatment takes, on average, more than 10 years and costs hundreds of millions of dollars. If successful, we could dramatically improve the industry's ability to model cellular images according to their relevant biology. In turn, greatly decreasing the cost of treatments and ensure these treatments get to patients faster. This work aims at solving a part of this problem by creating a cellular image classification model which can decipher the genetic perturbations in cell (occurring naturally or artificially). Another interesting question addressed is what makes the deep-learning model decide in a particular fashion, which can further help in demystifying the mechanism of action of certain perturbations and paves a way towards the explainability of the deep-learning model.

Keywords: cellular images, genetic perturbations, deep-learning, explainability

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Authors: Tsz Ching Jasmine Lam

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Linguistic landscape (LL) can serve as both top-down and bottom-up approaches to understanding language planning policy in various dimensions. It can reflect the language identities, motives and contestations perceived by stakeholders of different decision-making levels. Prior studies adopted the bottom-up approach to investigate the language practice and ideologies reflected by the design and linguistic features observed in the linguistic landscapes in ethnically and linguistically diverse areas, like Medan in Russia and Seoul in Korea. As Hong Kong is also a trilingual city with an inclusive combination of nationalities, this paper is intended to take it as a case study to explore the de facto language ideologies reflected by LL at the micro-level. We would look into the catering industry from a holistic perspective by reviewing the food menus of 66 restaurants located in diversified districts and serving different types of cuisines. This bottom-up LL research reveals that business owners and the public share the language ideologies of perceiving English as a prestigious language, multilingualism and traditional Chinese as a standard character.

Keywords: bottom-up, language ideologies, language planning policy, language policy, language identities, linguistic landscape

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: Nhac Thanh Huong

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Legal English, also known as Language of the Law (Mellinkoff, David. 2004), is an indispensable factor contributing to the development of legal field. At Hanoi Law University, legal English is a compulsory subject in the syllabus of legal English major; International Trade law and Fast-track law training program. The question that what obstacles students face with when dealing with legal English, however, has not been answered at that institution. Therefore, this present research, which makes use of survey questionnaires as the main method, aims to study the challenges of learning legal English from the students’ perspective, from which some useful solutions are drawn up to overcome these difficulties and improve the effectiveness of learning legal English. The results indicate notable difficulties arising from the level of general English skills, the characteristics of legal English and legal background knowledge. These findings lay a scientific foundation for suggesting some solutions for practical applications in teaching as well as learning legal English among both teachers and students.

Keywords: challenges, HLU, Legal English, students' perspective

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Authors: Vania Helena Tonussi Vidal, Ester Eliane Jeunon

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This article aims to analyze the change processes of management related bureaucracy and learning organization model. The theoretical framework was based on Beer and Nohria (2001) model, identified as E and O Theory. Based on this theory the empirical research was conducted in connection with six key dimensions: goal, leadership, focus, process, reward systems and consulting. We used a case study of an educational Institution located in Barbacena, Minas Gerais. This traditional center of technical knowledge for long time adopted the bureaucratic way of management. After many changes in a business model, as the creation of graduate and undergraduate courses they decided to make a deep change in management model that is our research focus. The data were collected through semi-structured interviews with director, managers and courses supervisors. The analysis were processed by the procedures of Collective Subject Discourse (CSD) method, develop by Lefèvre & Lefèvre (2000), Results showed the incremental growing of management model toward a learning organization. Many impacts could be seeing. As negative factors we have: people resistance; poor information about the planning and implementation process; old politics inside the new model and so on. Positive impacts are: new procedures in human resources, mainly related to manager skills and empowerment; structure downsizing, open discussions channel; integrated information system. The process is still under construction and now great stimulus is done to managers and employee commitment in the process.

Keywords: bureaucracy, organizational learning, organizational change, E and O theory

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Authors: J. Mano Ranjini

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

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

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Authors: Sunantha Teyarachakul

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We consider the worker-learning Newsvendor Model, under the case of lost-sales for unmet demand, with the research objective of proposing the cost-minimization order policy and lot size, scheduled to arrive at the beginning of the selling-period. In general, the New Vendor Model is used to find the optimal order quantity for the perishable items such as fashionable products or those with seasonal demand or short-life cycles. Technically, it is used when the product demand is stochastic and available for the single selling-season, and when there is only a one time opportunity for the vendor to purchase, with possibly of long ordering lead-times. Our work differs from the classical Newsvendor Model in that we incorporate the human factor (specifically worker learning) and its influence over the costs of processing units into the model. We describe this by using the well-known Wright’s Learning Curve. Most of the assumptions of the classical New Vendor Model are still maintained in our work, such as the constant per-unit cost of leftover and shortage, the zero initial inventory, as well as the continuous time. Our problem is challenging in the way that the best order quantity in the classical model, which is balancing the over-stocking and under-stocking costs, is no longer optimal. Specifically, when adding the cost-saving from worker learning to such expected total cost, the convexity of the cost function will likely not be maintained. This has called for a new way in determining the optimal order policy. In response to such challenges, we found a number of characteristics related to the expected cost function and its derivatives, which we then used in formulating the optimal ordering policy. Examples of such characteristics are; the optimal order quantity exists and is unique if the demand follows a Uniform Distribution; if the demand follows the Beta Distribution with some specific properties of its parameters, the second derivative of the expected cost function has at most two roots; and there exists the specific level of lot size that satisfies the first order condition. Our research results could be helpful for analysis of supply chain coordination and of the periodic review system for similar problems.

Keywords: inventory management, Newsvendor model, order policy, worker learning

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Authors: Tomoko Yaginuma, Rosa Cabecinhas

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Recently, the studies of the images about Japan and/or the Japanese people have been done in a Japanese language education context since the number of the students of Japanese as a Foreign Language (JFL) has been increasing worldwide, including in Portugal. It has been claimed that one of the reasons for this increase is the current popularity of Japanese pop-culture, namely anime (Japanese animations) and manga (Japanese visual novels), among young students. In the present study, the images about Japan and the Japanese held by JFL students in Portugal were examined by a questionnaire survey. The JFL students in higher education in Portugal (N=296) were asked to answer, among the other questions, their degree of agreement (using a Likert scale) with 24 pre-defined descriptions about the Japanese, which appear as relevant in a qualitative pilot study conducted before. The results show that the image of Japanese people by Portuguese JFL students is stressed around four dimensions: 1) diligence, 2) kindness, 3) conservativeness and 4) innovativeness. The students considered anime was the main source of information about the Japanese people and culture and anime was also strongly associated with the students’ interests in learning Japanese language.

Keywords: anime, cultural studies, images about Japan and Japanese people, Portugal

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Authors: Hirofumi Miyajima, Kazuya Kishida, Noritaka Shigei, Hiromi Miyajima

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Most of self-tuning fuzzy systems, which are automatically constructed from learning data, are based on the steepest descent method (SDM). However, this approach often requires a large convergence time and gets stuck into a shallow local minimum. One of its solutions is to use fuzzy rule modules with a small number of inputs such as DIRMs (Double-Input Rule Modules) and SIRMs (Single-Input Rule Modules). In this paper, we consider a (generalized) DIRMs model composed of double and single-input rule modules. Further, in order to reduce the redundant modules for the (generalized) DIRMs model, pruning and generative learning algorithms for the model are suggested. In order to show the effectiveness of them, numerical simulations for function approximation, Box-Jenkins and obstacle avoidance problems are performed.

Keywords: Box-Jenkins's problem, double-input rule module, fuzzy inference model, obstacle avoidance, single-input rule module

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Authors: Hondozi Jahja

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There is an obvious need to learn Arabic language and teach it to other speakers through the new curricula. The idea is to bridge the gap between theory and practice. To that end, we have sought to offer some means of help to master the Arabic language, in addition to our efforts to apply these means, enriching the culture of the student and develop his vocabulary. There is no doubt that taking care of the practical aspect of the grammar was our constant goal, and this particular aspect is what builds the student’s positive values and refine his taste and develop his language. In addressing these issues, we have adopted a school-based approach based primarily on the active and positive participation of the student. The theoretical linguistic issues - in our opinion - are not a primary goal, but the goal is to be used them by students through speaking and applying them. Among the objectives of this research is to establish the basic language skills of the students using new means that help the student to acquire these skills and apply them in various subjects of interest in his progress and development. Unfortunately, some of our students consider the grammar as ‘difficult’, ‘complex’ and ‘heavy’ in itself. This is one of the obstacles that stand in the way of their desired results. As a consequence, they end up talking – mumbling - about the difficulties they face in applying those rules. Therefore, some of our students finish their university studies and are unable to express what they feel using language correctly. For this purpose, we have sought in this research to follow a new integrated approach, which is to study the grammar of the language through modern means of the consolidation of the principle of functional language, and that the rule implies to control tongues and linguistic expressions properly. This research is a result of a practical experience as a teacher of Arabic language for non-native speakers at the ‘Hassan Pristina’ University, located in Pristina, the capital of Kosovo and at the Qatar Training Center since its establishment in 2012.

Keywords: arabic, applied methods, acquire, learning

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Authors: Ngoc-Nguyen Nguyen, Hsiu-Ling Chen, Thanh-Truc Lai Huynh

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In foreign language learning, vocabulary acquisition has consistently posed challenges to learners, especially for those at lower levels. Conventional approaches often fail to promote vocabulary learning and ensure engaging experiences alike. The emergence of mobile learning, particularly the integration of chatbot systems, has offered alternative ways to facilitate this practice. Chatbots have proven effective in educational contexts by offering interactive learning experiences in a constructivist manner. These tools have caught attention in the field of mobile-assisted language learning (MALL) in recent years. This research is conducted in an English for Specific Purposes (ESP) course at the A2 level of the CEFR, designed for non-English majors. Participants are first-year Vietnamese students aged 18 to 20 at a university. This quasi-experimental study follows a pretest-posttest control group design over five weeks, with two classes randomly assigned as the experimental and control groups. The experimental group engages in chatbot-assisted spaced practice with SRL components, while the control group uses the same spaced practice without SRL. The two classes are taught by the same lecturer. Data are collected through pre- and post-tests, cognitive load surveys, and semi-structured interviews. The combination of self-regulated learning (SRL) and distributed practice, grounded in the spacing effect, forms the basis of the present study. SRL elements, which concern goal setting and strategy planning, are integrated into the system. The spaced practice method, similar to those used in widely recognized learning platforms like Duolingo and Anki flashcards, spreads out learning over multiple sessions. This study’s design features quizzes progressively increasing in difficulty. These quizzes are aimed at targeting both the Recognition-Recall and Comprehension-Use dimensions for a comprehensive acquisition of vocabulary. The mobile-based chatbot system is built using Golang, an open-source programming language developed by Google. It follows a structured flow that guides learners through a series of 4 quizzes in each week of teacher-led learning. The quizzes start with less cognitively demanding tasks, such as multiple-choice questions, before moving on to more complex exercises. The integration of SRL elements allows students to self-evaluate the difficulty level of vocabulary items, predict scores achieved, and choose appropriate strategy. This research is part one of a two-part project. The initial findings will determine the development of an upgraded chatbot system in part two, where adaptive features in response to the integration of SRL components will be introduced. The research objectives are to assess the effectiveness of the chatbot-assisted approach, based on the combination of spaced practice and SRL, in improving vocabulary acquisition and managing cognitive load, as well as to understand students' perceptions of this learning tool. The insights from this study will contribute to the growing body of research on mobile-assisted language learning and offer practical implications for integrating chatbot systems with spaced practice into educational settings to enhance vocabulary learning.

Keywords: mobile learning, mobile-assisted language learning, MALL, chatbots, vocabulary learning, spaced practice, spacing effect, self-regulated learning, SRL, self-regulation, EFL, cognitive load

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Authors: Ava Dodd, Jonathan Adams

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The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools, and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represents another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.

Keywords: computer vision, machine learning, synthetic data, YOLOv4

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Authors: Mohd Rashid Bin Hj. Md Idris, Lajiman Bin Janoory, Abdullah Bin Yusof, Mahzir Bin Ibrahim

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The Malay language curriculum at primary school level in Malaysia is instrumental in ensuring the status of the language as the official and national language, the language of instruction as well as the language that unites the various ethnics in Malaysia. A research addressing issues related to the curriculum standard is, therefore, essential to provide value added quality to the existing National Education Philosophy in ongoing efforts to produce an individual who is balanced in intellectual, spiritual, emotional and physical developments. The objective of this study is to examine the Philosophy of Language Theory, to review the content of the Malay language subject in relation to the Standard Curriculum for Primary Schools (KSSR), and to identify aspects of Theory of Philosophy in the Standard Curriculum for Primary Schools. The Malay language Primary School Curriculum is designed to enable students to be competent speakers and communicators of the language in order to gain knowledge, skills, information, values, and ideas and to enhance skills in social relations. Therefore, this study is designed to help educators to achieve all the stated goals. At the same time students at primary school level are expected to be able to apply the principle of language perfection as stated in the Philosophy of Language Theory to enable them to understand, appreciate and to take pride in being a Malaysian who speaks the language well.

Keywords: language, philosophy, theory, curriculum, standard, national education philosophy

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Authors: Eyup Bayram Guzel

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There have been growing awareness that technology offers unique and promising advantages by offering up-to-data educational materials in promoting teaching and learning materials, new strategies for building enhanced communication environment for people with disabilities and specifically for this study concentrated on the students with deafness and hearing impairments. Creating e-learning environment where teachers and students work in collaboration to develop better educational outcomes is the foremost reason of conducting this research. This study examined the perspectives of special education teachers’ regarding an application of e-learning software called Multimedia Builder on the students with deafness and hearing impairments. Initial and follow up interviews were conducted with 15 special education teachers around the scope of qualitative case study. Grounded approach has been used to analyse and interpret the data. The research results revealed that application of Multimedia Builder software were influential on reading, sign language, vocabulary improvements, computer and ICT usage developments and on audio-visual learning achievements for the advantages of students with deafness and hearing impairments. The implications of the study encouraged the ways of using e-learning tools and strategies to promote unique and comprehensive learning experiences for the targeted students and their teachers.

Keywords: e-learning, special education, deafness and hearing impairment, computer-ICT usage.

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Authors: Vahid Bairami Rad, Shapoor Norazar, Moslem Talebi Asl

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The fast growing accessibility and capability of emerging technologies have fashioned enormous possibilities of designing, developing and implementing innovative teaching methods in the classroom. Using teaching methods and technology together have a fantastic results, because the global technological scenario has paved the way to new pedagogies in teaching-learning process. At the other side methods by focusing on students and the ways of learning in them, that can demonstrate logical ways of improving student achievement in English as a foreign language in Iran. The sample of study was 90 students of 10th grade of high school located in Ardebil. A pretest-posttest equivalent group designed to compare the achievement of groups. Students divided to 3 group, Control base, computer base, method and technology base. Pretest and post test contain 30 items each from English textbook were developed and administrated, then obtained data were analyzed. The results showed that there was an important difference. The 3rd group performance was better than other groups. On the basis of this result it was obviously counseled that teaching-learning capabilities.

Keywords: method, technology based environment, computer based environment, english as a foreign language, student achievement

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Authors: Lizhi Ma, Chengcheng Hu, Fuxian Wong

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Partial reduce (P-Reduce) has set a state-of-the-art performance on distributed machine learning in the heterogeneous environment over the All-Reduce architecture. The dynamic P-Reduce based on the exponential moving average (EMA) approach predicts all the intermediate model parameters, which raises unreliability. It is noticed that the approximation trick leads the wrong way to obtaining model parameters in all the nodes. In this paper, SAP-Reduce is proposed, which is a variant of the All-Reduce distributed training model with staleness-aware dynamic P-Reduce. SAP-Reduce directly utilizes the EMA-like algorithm to generate the normalized weights. To demonstrate the effectiveness of the algorithm, the experiments are set based on a number of deep learning models, comparing the single-step training acceleration ratio and convergence time. It is found that SAP-Reduce simplifying dynamic P-Reduce outperforms the intermediate approximation one. The empirical results show SAP-Reduce is 1.3× −2.1× faster than existing baselines.

Keywords: collective communication, decentralized distributed training, machine learning, P-Reduce

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Zelalem Fantahun

Abstract:

Part-of-speech tagging is the process of assigning a part-of-speech or other lexical class marker to each word into naturally occurring text. Part-of-speech tagging is the most fundamental and basic task almost in all natural language processing. In natural language processing, the problem of providing large amount of manually annotated data is a knowledge acquisition bottleneck. Since, Amharic is one of under-resourced language, the availability of tagged corpus is the bottleneck problem for natural language processing especially for POS tagging. A promising direction to tackle this problem is to provide a system that does not require manually tagged data. In unsupervised learning, the learner is not provided with classifications. Unsupervised algorithms seek out similarity between pieces of data in order to determine whether they can be characterized as forming a group. This paper explicates the development of unsupervised part-of-speech tagger using K-Means clustering for Amharic language since large amount of data is produced in day-to-day activities. In the development of the tagger, the following procedures are followed. First, the unlabeled data (raw text) is divided into 10 folds and tokenization phase takes place; at this level, the raw text is chunked at sentence level and then into words. The second phase is feature extraction which includes word frequency, syntactic and morphological features of a word. The third phase is clustering. Among different clustering algorithms, K-means is selected and implemented in this study that brings group of similar words together. The fourth phase is mapping, which deals with looking at each cluster carefully and the most common tag is assigned to a group. This study finds out two features that are capable of distinguishing one part-of-speech from others these are morphological feature and positional information and show that it is possible to use unsupervised learning for Amharic POS tagging. In order to increase performance of the unsupervised part-of-speech tagger, there is a need to incorporate other features that are not included in this study, such as semantic related information. Finally, based on experimental result, the performance of the system achieves a maximum of 81% accuracy.

Keywords: POS tagging, Amharic, unsupervised learning, k-means

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Authors: Jeyaseelan Gnanaseelan, Subajana Jeyaseelan

Abstract:

The paper avails, in a systemic form, some of the results of the investigation into nature, functions, problems, and implications in the use of English as the medium of Instruction (EMI) in the Business Management courses at Vavuniya Campus of the University of Jaffna, located in the conflict-affected northern part of Sri Lanka. It is a case study of the responses of the students and the teachers from Tamil and Sinhala language communities of the Faculty of Business Studies. This paper analyzes the perceptions on the use of the medium, the EMI background, resources available and accessible, language abilities of the teachers and learners, learning style and pedagogy, the EMI methodology, the socio-economic and socio-political contexts typical of a non-native English learning context. The analysis is quantitative and qualitative. It finds out the functional perspective of the EMI in Sri Lanka and suggests practical strategies of contextualization and acculturation in the EMI organization and positions. The paper assesses the learner and teacher capacity in the use of English. The ethnic conflict and linguistic politics in Sri Lanka have contributed multiple factors to the current use of English as the medium. It has conflicted with its domestic realities and the globalization trends of the world at large which determines efficiency and effectiveness.

Keywords: medium of instruction, English, business management, teaching and learning

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Authors: Dohun Kim

Abstract:

Technological innovations have stimulated flipped learning in many disciplines, including language teaching. It is a specific type of blended learning, which combines onsite (i.e. face-to-face) with online experiences to produce effective, efficient and flexible learning. Flipped learning literally ‘flips’ conventional teaching and learning activities upside down: it leverages technologies to deliver a lecture and direct instruction—other asynchronous activities as well—outside the classroom to reserve onsite time for interaction and activities in the upper cognitive realms: applying, analysing, evaluating and creating. Unlike the conventional flipped approaches, which focused on video lecture, followed by face-to-face or on-site session, new innovative methods incorporate various means and structures to serve the needs of different academic disciplines and classrooms. In the light of such innovations, this study adopted ‘student-engaged’ approaches to interpreter training and contrasts them with traditional classrooms. To this end, students were also encouraged to engage in asynchronous activities online, and innovative technologies, such as Telepresence, were employed. Based on the class implementation, a thorough examination was conducted to examine how we can structure and implement flipped classrooms for language and interpreting training while actively engaging learners. This study adopted a quantitative research method, while complementing it with a qualitative one. The key findings suggest that the significance of the instructor’s role does not dwindle, but his/her role changes to a moderator and a facilitator. Second, we can apply flipped learning to both theory- and practice-oriented modules. Third, students’ integration into the community of inquiry is of significant importance to foster active and higher-order learning. Fourth, cognitive presence and competence can be enhanced through strengthened and integrated teaching and social presences. Well-orchestrated teaching presence stimulates students to find out the problems and voices the convergences and divergences, while fluid social presence facilitates the exchanges of knowledge and the adjustment of solutions, which eventually contributes to consolidating cognitive presence—a key ingredient that enables the application and testing of the solutions and reflection thereon.

Keywords: blended learning, Community of Inquiry, flipped learning, interpreter training, student-centred learning

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Authors: Joseph Lillington, Tom Gout, Mike Harrison, Ian Farnan

Abstract:

The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data.

Keywords: machine learning, predictive modelling, pattern recognition, radioactive waste glass

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Authors: Janet C. Read

Abstract:

UClan set collaboration with Lingokids to assess the Lingokids learning app's impact on learning outcomes in classrooms in the UK for children with ages ranging from 3 to 5 years. Data gathered during the controlled study with 69 children includes attitudinal data, engagement, and learning scores. Data shows that children enjoyment while learning was higher among those children using the game-based app compared to those children using other traditional methods. It’s worth pointing out that engagement when using the learning app was significantly higher than other traditional methods among older children. According to existing literature, there is a direct correlation between engagement, motivation, and learning. Therefore, this study provides relevant data points to conclude that Lingokids learning app serves its purpose of encouraging learning through playful and interactive content. That being said, we believe that learning outcomes should be assessed with a wider range of methods in further studies. Likewise, it would be beneficial to assess the level of usability and playability of the app in order to evaluate the learning app from other angles.

Keywords: learning app, learning outcomes, rapid test activity, Smileyometer, early childhood education, innovative pedagogy

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