Search results for: online teaching and learning

Authors: Elena Pozdnyakova

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“Government Relations” is a field of knowledge presently taught at the majority of universities around the globe. English as the default language can become the language of teaching since the issues discussed are both global and national in character. However for this field of knowledge key concepts and their word representations in English don’t often coincide with those in other languages. International master’s degree students abroad as well as students, taught the course in English at their national universities, are exposed to difficulties, connected with correct conceptualizing of terminology of GR in British and American academic traditions. The study was carried out during the GR English language course elaboration (pilot research: 2013 -2015) at Moscow State Institute of Foreign Relations (University), Russian Federation. Within this period, English language instructors designed and elaborated the three-semester course of GR. Methodologically the course design was based on elaboration model with the special focus on conceptual elaboration sequence and theoretical elaboration sequence. The course designers faced difficulties in concept selection and theoretical elaboration sequence. To improve the results and eliminate the problems with concept selection, a new, corpus-based approach was worked out. The computer-based tool WordSmith 6.0 was used with the aim to build a model of key concept selection. The corpus of GR English texts consisted of 1 million words (the study corpus). The approach was based on measuring effect size, i.e. the percent difference of the frequency of a word in the study corpus when compared to that in the reference corpus. The results obtained proved significant improvement in the process of concept selection. The corpus-based model also facilitated theoretical elaboration of teaching materials.

Keywords: corpus-based study, English as the default language, key concepts, measuring effect size, model of key concept selection

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Authors: Ehsan Atefi, Michael Grigware

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Respiratory failure secondary to surfactant deficiency resulting from respiratory distress syndrome is considered one major cause of morbidity in preterm infants. Surfactant replacement treatment (SRT) is considered an effective treatment for this disease. Here, we introduce an AI-mediated approach for estimating the distribution of surfactant in the lung airway of a newborn infant during SRT. Our approach implements machine learning to precisely estimate the splitting ratio of a liquid drop during bifurcation at different injection velocities and patient orientations. This technique can be used to calculate the surfactant residue remaining on the airway wall during the surfactant injection process. Our model works by minimizing the pressure drop difference between the two airway branches at each generation, subject to mass and momentum conservation. Our platform can be used to generate feedback for immediately adjusting the velocity of injection and patient orientation during SRT.

Keywords: respiratory failure, surfactant deficiency, surfactant replacement, machine learning

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Authors: Kittivate Boonyopakorn

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The purposes of this study were to find the English competence of the public health volunteers and to develop the use of their English. The samples for the study were 41 public health village volunteers at Ban Prasukchai, in Thailand. The findings showed that the sum of all scores for the pre-test was 452, while the score for the post-test was 1,080. Therefore, the results of the experiment confirm that the post-test scores (1,080) significantly are higher than the pre-test (452). The mean score (N=41) for the pre-test was 11.02 while the mean score (N=41) for the post-test was 18.10. The standard deviation for the pre-test was 2.734; however, for the post-test it was 1.934. In addition to the experts-evaluated research tools, the results of the evaluation for the structured interviews (IOC) were 1 in value. The evaluation of congruence for the content with learning objectives (IOC) were 0.66 to 1.00 in value. The evaluation of congruence for the pre and post-test with learning objectives (IOC) are 1 in value.

Keywords: finding the English competency, developing public health, village volunteers

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Authors: Shao-Di Feng, Von-Wun Soo

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To generate symbolic music with specific emotions is a challenging task due to symbolic music datasets that have emotion labels are scarce and incomplete. This research aims to generate more refined emotions based on the training datasets that are only labeled with four quadrants in Russel’s 2D emotion model. We focus on the theory of Music Fadernet and map arousal and valence to the low-level attributes, and build a symbolic music generation model by combining transformer and GM-VAE. We adopt an in-attention mechanism for the model and improve it by allowing modulation by conditional information. And we show the music generation model could control the generation of music according to the emotions specified by users in terms of high-level linguistic expression and by manipulating their corresponding low-level musical attributes. Finally, we evaluate the model performance using a pre-trained emotion classifier against a pop piano midi dataset called EMOPIA, and by subjective listening evaluation, we demonstrate that the model could generate music with more refined emotions correctly.

Keywords: music generation, music emotion controlling, deep learning, semi-supervised learning

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Authors: I Made Ginarsa, Agung Budi Muljono, I Made Ari Nrartha

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Transient voltage response appears in power system operation when an additional loading is forced to load bus of power systems. In this research, improvement of transient voltage response is done by using power system stabilizer-static var compensator (PSS-SVC) based on adaptive neuro-fuzzy inference system (ANFIS)-algorithm. The main function of the PSS is to add damping component to damp rotor oscillation through automatic voltage regulator (AVR) and excitation system. Learning process of the ANFIS is done by using off-line method where data learning that is used to train the ANFIS model are obtained by simulating the PSS-SVC conventional. The ANFIS model uses 7 Gaussian membership functions at two inputs and 49 rules at an output. Then, the ANFIS-PSS and ANFIS-SVC models are applied to power systems. Simulation result shows that the response of transient voltage is improved with settling time at the time of 4.25 s.

Keywords: improvement, transient voltage, PSS-SVC, ANFIS, settling time

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Authors: Tian Lan, Yixiang Wang, Wenxin Tai, Yilan Lyu, Zufeng Wu

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The monaural speech enhancement task in the time-frequencydomain has a myriad of approaches, with the stacked con-volutional neural network (CNN) demonstrating superiorability in feature extraction and selection. However, usingstacked single convolutions method limits feature represen-tation capability and generalization ability. In order to solvethe aforementioned problem, we propose an attention-basedadaptive convolutional network that integrates the multi-scale convolutional operations into a operation-specific blockvia input dependent attention to adapt to complex auditoryscenes. In addition, we introduce a two-stage progressivelearning method to enlarge the receptive field without a dra-matic increase in computation burden. We conduct a series ofexperiments based on the TIMIT corpus, and the experimen-tal results prove that our proposed model is better than thestate-of-art models on all metrics.

Keywords: speech enhancement, adaptive convolu-tion, progressive learning, time-frequency domain

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Authors: Rodrigo Arnaldo Scarpel

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Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

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Authors: R. R. Govind

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In the digital age, artificial intelligence (AI) is having a significant impact on the rapid changes that cities are experiencing. This study explores the profound impact of AI on urban morphology, especially with regard to promoting friendly design choices. It addresses a significant research gap by examining the real-world effects of integrating AI into urban design and management. The main objective is to outline a framework for integrating AI to transform urban settings. The study employs an urban design framework to effectively navigate complicated urban environments, emphasize the need for urban management, and provide efficient planning and design strategies. Taking Gangtok's informal settlements as a focal point, the study employs AI methodologies such as machine learning, predictive analytics, and generative AI to tackle issues of 'urban informality'. The insights garnered not only offer valuable perspectives but also unveil AI's transformative potential in addressing contemporary urban challenges.

Keywords: urban design, artificial intelligence, urban challenges, machine learning, urban informality

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Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

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Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

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Authors: Rie Adachi

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In Japan, it is required to improve students’ English communicative proficiency and the Education Ministry will start English education for the third grade and upper from year 2020 on. Considering the problems with the educational system, Content and Language Integrated Learning (CLIL) is more appropriate to be employed in elementary schools rather than just introducing English lessons. Effective CLIL takes place in the 4Cs Framework, and different strategies are used in various activities, such as arts and crafts, bodily expression, singing, playing roles, etc. After a CLIL workshop for local teachers focused on the 4Cs, the writer conducted a survey of the 36 participants using a questionnaire and found that they did not know the word CLIL, but seemed to have an interest after attending the workshop. The writer concluded that researchers and practitioners need to spread awareness of the 4Cs framework, to apply CLIL into Japanese educational context, to provide CLIL teacher training program and so on, in order to practice CLIL in Japanese elementary schools and nurture students with a global mindset.

Keywords: CLIL, 4Cs, homeroom teachers, intercultural understanding

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Authors: László Schmidt

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Technological and digital innovation is constantly and dynamically evolving, which poses an enormous challenge to both lawmaking and law enforcement. To legislation because artificial intelligence permeates many areas of people’s daily lives that the legislator must regulate. it can see how challenging it is to regulate e.g. self-driving cars/taxis/camions etc. Not to mention cryptocurrencies and Chat GPT, the use of which also requires legislative intervention. Artificial intelligence also poses an extraordinary challenge to law enforcement. In criminal cases, police and prosecutors can make great use of AI in investigations, e.g. in forensics, DNA samples, reconstruction, identification, etc. But it can also be of great help in the detection of crimes committed in cyberspace. In the case of cybercrime, on the one hand, it can be viewed as a new type of crime that can only be committed with the help of information systems, and that has a specific protected legal object, such as an information system or data. On the other hand, it also includes traditional crimes that are much easier to commit with the help of new tools. According to Hungarian Criminal Code section 375 (1), any person who, for unlawful financial gain, introduces data into an information system, or alters or deletes data processed therein, or renders data inaccessible, or otherwise interferes with the functioning of the information system, and thereby causes damage, is guilty of a felony punishable by imprisonment not exceeding three years. The Covid-19 coronavirus epidemic has had a significant impact on our lives and our daily lives. It was no different in the world of crime. With people staying at home for months, schools, restaurants, theatres, cinemas closed, and no travel, criminals have had to change their ways. Criminals were committing crimes online in even greater numbers than before. These crimes were very diverse, ranging from false fundraising, the collection and misuse of personal data, extortion to fraud on various online marketplaces. The most vulnerable age groups (minors and elderly) could be made more aware and prevented from becoming victims of this type of crime through targeted programmes. The aim of the study is to show the Hungarian judicial practice in relation to cybercrime and possible preventive solutions.

Keywords: cybercrime, COVID-19, Hungary, criminal law

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Authors: Joy Aworookoroh

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Writing is one of the active skills in language learning. Students of English as a foreign language are expected to write efficiently and proficiently in the language; however, there are usually challenges to optimal performance and competence in writing. Errors, on the other hand, in a foreign language learning situation are more positive than negative as they provide the basis for solving the limitations of the students. This paper investigates the situation in the Department of English, Taraba State University Jalingo. Students are administered a descriptive writing test across different levels of study. The target students are multilingual with an L1 of either Kuteb, Hausa or Junkun languages. The essays are accessed to identify the different kinds of errors in them alongside the classification of the order. Errors of correctness, clarity, engagement, and delivery were identified. However, the study identified that the degree of errors reduces alongside the experience and exposure of the students to an EFL classroom.

Keywords: errors, writings, descriptive essay, multilingual

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

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Authors: Chen-Chi An, Jun-Yi He, Cheng-Han Hsieh, Chen-Ching Ting

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This research has successfully developed an intelligent table tennis ball machine with human play simulate all situations of human play to take the service. It is well known; an excellent ball machine can help the table tennis coach to provide more efficient teaching, also give players the good technical training and entertainment. An excellent ball machine should be able to service all balls based on human play simulation due to the conventional competitions are today all taken place for people. In this work, two counter-rotating wheels are used to service the balls, where changing the absolute rotating speeds of the two wheels and the differences of rotating speeds between the two wheels can adjust the struck forces and the rotating speeds of the ball. The relationships between the absolute rotating speed of the two wheels and the struck forces of the ball as well as the differences rotating speeds between the two wheels and the rotating speeds of the ball are experimentally determined for technical development. The outlet speed, the ejected distance, and the rotating speed of the ball were measured by changing the absolute rotating speeds of the two wheels in terms of a series of differences in rotating speed between the two wheels for calibration of the ball machine; where the outlet speed and the ejected distance of the ball were further converted to the struck forces of the ball. In process, the balls serviced by the intelligent ball machine were based on the received calibration curves with help of the computer. Experiments technically used photosensitive devices to detect the outlet and rotating speed of the ball. Finally, this research developed some teaching programs for technical training using three ball machines and received more efficient training.

Keywords: table tennis, ball machine, human play simulation, counter-rotating wheels

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Authors: Ana Bilandzic, Marcus Foth, Greg Hearn

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In a narrow sense, innovation is the invention and commercialisation of a new product or service in the marketplace. The literature suggests places that support knowledge exchange and social interaction, e.g. coffee shops, to nurture innovative ideas. With the widespread success of Internet, interpersonal communication and interaction changed. Online platforms complement physical places for idea exchange and innovation – the rise of hybrid, ‘net localities.’ Further, since its introduction in 2003 by Chesbrough, the concept of open innovation received increased attention as a topic in academic research as well as an innovation strategy applied by companies. Open innovation allows companies to seek and release intellectual property and new ideas from outside of their own company. As a consequence, the innovation process is no longer only managed within the company, but it is pursued in a co-creation process with customers, suppliers, and other stakeholders. Quirky.com (Quirky), a company founded by Ben Kaufman in 2009, recognised the opportunity given by the Internet for knowledge exchange and open innovation. Quirky developed an online platform that makes innovation available to everyone. This paper reports on a study that analysed Quirky’s business process in an extended event-driven process chain (eEPC). The aim was to determine how the platform enabled crowdsourced innovation for physical products on the Internet. The analysis reveals that key elements of the business model are based on open innovation. Quirky is an example of how open innovation can support crowdsourced and crowdfunded product ideation, development and selling. The company opened up various stages in the innovation process to its members to contribute in the product development, e.g. product ideation, design, and market research. Throughout the process, members earn influence through participating in the product development. Based on the influence they receive, shares on the product’s turnover. The outcomes of the study’s analysis highlighted certain benefits of open innovation for product development. The paper concludes with recommendations for future research to look into opportunities of open innovation approaches to be adopted by tertiary institutions as a novel way to commercialise research intellectual property.

Keywords: business process, crowdsourced innovation, open innovation, Quirky

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Authors: Waleed Maqableh, Ahmad Al-Hamad, Manjit Sidhu

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Augmented reality (AR) technology has the capability to provide many benefits in the field of education as a modern technology which aided learning and improved the learning experience. This paper evaluates AR based application with multiple markers interaction technique (touch-to-print) which is designed for analyzing the kinematics of 4BL mechanism in mechanical engineering. The application is termed as AR-4BL-MAST and it allows the users to touch the symbols on a paper in natural way of interaction. The evaluation of this application was performed with mechanical engineering students and human–computer interaction (HCI) experts to test its effectiveness as a tangible user interface application where the statistical results show its ability as an interaction technique, and it gives the users more freedom in interaction with the virtual mechanical objects.

Keywords: augmented reality, multimedia, user interface, engineering, education technology

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Authors: Aniekan James Akpan, Inemesit Akpan Umoren, Uduak Iwok

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From prehistoric and primitive cultures, education was seen as a process of culture transmission by way of guiding children into becoming good members of their local communities. Even in modern cultures, education is seen as a systematic discipline aimed at cultivating genuine values to improve oneself and society. Without education, the chances of realizing the desired vision are marred as it is believed that nations that invest much in education are able to reap the desired benefits technologically, economically, socially, politically, and otherwise. In this sense, the moulding of character is considered the primary purpose of education, and until the audience of mass media through its various vehicles is seen as tools for improving the overall development of society. It is believed that a media-friendly person is likely to perform better than someone who is less friendly. This work, therefore, examines the role media play in educational development. As highlighted by the study, a summary of the functions of media shows that they widen horizon by acting as a liberating force, breaking distance, bonds, and transforming a traditional society into a modern one. With the use of technological development theory, agenda-setting theory as well as uses and gratification theory and multiple intelligence theory, the work identifies different ways in which mass media help in educational development and draws attention to the audience’s perception of media functions in terms of educational development. With a survey method and a population of 6,903,321 people, the work sampled 220 respondents using purposive technique drawn from rural communities in the South-South region of Nigeria. The work concludes that mass media are potent vehicles for teaching and learning and therefore recommends that government should provide basic infrastructures to the rural communities to aid full utilization of media potentials in educational development and equally urge media owners and practitioners to as a matter of urgency increase coverage time on issues bordering on education as it is done for political and other issues.

Keywords: educational, development, media usage, perception

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Authors: Antonios Paraskevas, Michael Madas

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For a university to maintain its international competitiveness in education, it is essential to recruit qualitative academic staff as it constitutes its most valuable asset. This selection demonstrates a significant role in achieving strategic objectives, particularly by emphasizing a firm commitment to the exceptional student experience and innovative teaching and learning practices of high quality. In this vein, the appropriate selection of academic staff establishes a very important factor of competitiveness, efficiency and reputation of an academic institute. Within this framework, our work demonstrates a comprehensive methodological concept that emphasizes the multi-criteria nature of the problem and how decision-makers could utilize our approach in order to proceed to the appropriate judgment. The conceptual framework introduced in this paper is built upon a hybrid neutrosophic method based on the Neutrosophic Analytical Hierarchy Process (N-AHP), which uses the theory of neutrosophy sets and is considered suitable in terms of a significant degree of ambiguity and indeterminacy observed in the decision-making process. To this end, our framework extends the N-AHP by incorporating the Neutrosophic Delphi Method (N-DM). By applying the N-DM, we can take into consideration the importance of each decision-maker and their preferences per evaluation criterion. To the best of our knowledge, the proposed model is the first which applies the Neutrosophic Delphi Method in the selection of academic staff. As a case study, it was decided to use our method for a real problem of academic personnel selection, having as the main goal to enhance the algorithm proposed in previous scholars’ work, and thus taking care of the inherent ineffectiveness which becomes apparent in traditional multi-criteria decision-making methods when dealing with situations alike. As a further result, we prove that our method demonstrates greater applicability and reliability when compared to other decision models.

Keywords: multi-criteria decision making methods, analytical hierarchy process, delphi method, personnel recruitment, neutrosophic set theory

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Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

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In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

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Authors: Adawati Suhaili, Kamisah Osman, Mohd Effendi, Ewan Mohd Matore

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The global education reform has prompted Malaysia to transform the education system in Malaysia through the Malaysian Education Blueprint (MEB) 2013-2025. This transformation is aimed to achieve the top one-third rank in international assessment. The low achievement of student scientific literacy in TIMMS (Trends in International Mathematics and Science Study ) and PISA (Programme for International Student Assessment) has caused concern to the Ministry Of Education (MOE) despite various reform efforts. Therefore, an alternative action by enhancing the role of the Head of Science Panels (HoSPs) as a key change agent in catalyzing the improvement of student performance should be considered. Highlights of previous studies have shown that subject leadership is able to enhance teacher teaching quality in order to increase student learning. To lead the Science department and guide Science teachers more effectively, HoSPs need to strengthen their leadership skills. However, the issue of weaknesses in the leadership competencies of HoSPs in Malaysia has caused them to lack confidence and ability in leading the Science Department. The main objective of this study is to explore the factors that contribute to the problems faced by HoSPs at Sarawak in their leadership roles. This study used a qualitative design framework and using a semi-structured interview method for data collection. There were six informants involved in the interview consisting of lecturers, Senior Administrative Assistant Teacher and HoSPs. The findings of the study had been identified four main factors that contribute to problems in the leadership competencies of HoSPs in Sarawak, namely leadership practices, leadership structure, academic subjects and school change. The results are significant to the MOE in strengthening the leadership competencies of HoSPs in a more focus for improving the achievement of scientific literacy of students in Malaysia. This study can help improve the Hosps' leadership competencies in Malaysia.

Keywords: issues, problems, Malaysia education blueprint, leadership competencies, head of science panels

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Authors: William Kerua

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The PNG University of Technology (Unitech) has mandatory access to teaching, research and extension education. Given such function, the Agriculture Department has established the ‘South Pacific Institute of Sustainable Agriculture and Rural Development (SPISARD)’ in 2004. SPISARD is established as a vehicle to improve farming systems practiced in selected villages by undertaking pluralistic extension method through ‘Educational Institutional Approach’. Unlike other models, SPISARD’s educational institutional approach stresses on improving the whole farming systems practiced in a holistic manner and has a two-fold focus. The first is to understand the farming communities and improve the productivity of the farming systems in a sustainable way to increase income, improve nutrition and food security as well as livelihood enhancement trainings. The second is to enrich the Department’s curriculum through teaching, research, extension and getting inputs from farming community. SPISARD has established number of model villages in various provinces in Papua New Guinea (PNG) and with many positive outcome and success stories. Adaption of ‘educational institutional approach’ thus binds research, extension and training into one package with the use of students and academic staff through model village establishment in delivering development and extension to communities. This centre (SPISARD) coordinates the activities of the model village programs and linkages. The key to the development of the farming systems is establishing and coordinating linkages, collaboration, and developing partnerships both within and external institutions, organizations and agencies. SPISARD has a six-point step strategy for the development of sustainable agriculture and rural development. These steps are (i) establish contact and identify model villages, (ii) development of model village resource centres for research and trainings, (iii) conduct baseline surveys to identify problems/needs of model villages, (iv) development of solution strategies, (v) implementation and (vi) evaluation of impact of solution programs. SPISARD envisages that the farming systems practiced being improved if the villages can be made the centre of SPISARD activities. Therefore, SPISARD has developed a model village approach to channel rural development. The model village when established become the conduit points where teaching, training, research, and technology transfer takes place. This approach is again different and unique to the existing ones, in that, the development process take place in the farmers’ environment with immediate ‘real time’ feedback mechanisms based on the farmers’ perspective and satisfaction. So far, we have developed 14 model villages and have conducted 75 trainings in 21 different areas/topics in 8 provinces to a total of 2,832 participants of both sex. The aim of these trainings is to directly participate with farmers in the pursuit to improving their farming systems to increase productivity, income and to secure food security and nutrition, thus to improve their livelihood.

Keywords: development, educational institutional approach, livelihood improvement, sustainable agriculture

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Authors: Julianne Viola

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Young people are at a critical stage in their lives, developing from young participants to adult participants in democratic society. At this time, civic engagement is crucial for young people’s sense of belonging and future participation in their communities. In adolescence, individuals form their own identities and associations with others and may accomplish this with the help of technology and social media. In the Digital Age, young people and adults use technology as a platform to discuss political issues, including human rights and social justice but do not always engage in civil discourse. There is an urgent need to investigate this complex interplay of social media, identity formation, and civil discourse as it relates to how teenagers become participants in democratic society and how they engage in civil discourse. This qualitative study draws on theories of identity formation in adolescence and is situated within the literature surrounding teen civic engagement and technology use. Through in-depth interviews with participants ages 14 through 17, this study investigates the ways in which teens conceptualize their civic identities and engagement, presence online, and civil discourse. The context in which the young people in this study have grown up has the potential to impact and inform these processes. Early results of this study illustrate what it means to be a young person in today’s world, and how perceptions of others’ opinions may influence young people’s engagement in their communities and online. Participants in this study often indicated concerns of their age as a constraint on participation in their communities and in society, and a self-imposed restriction around the people with whom they engage in conversation about political and social issues. While the participants shared common concerns and experiences, each participant’s unique perspectives and beliefs are viewed with equal importance. The results from this research will help students, teachers, and community groups learn about the reasons for engagement and disengagement among this age group, and how technology has influenced teens’ dialogue about political issues. With this knowledge, academics and school leaders can devise new ways to best teach citizenship skills and civil discourse to students in the Digital Age.

Keywords: civics, digital age, discourse, sociology of youth, youth studies

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Authors: Danzhao Cheng, Eugene Ch'ng

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Virtual Reality technologies can reconstruct cultural heritage objects and sites to a level of realism. Concentrating mostly on documenting authentic data and accurate representations of tangible contents, current virtual heritage is limited to accumulating visually presented objects. Such constructions, however, are fragmentary and may not convey the inherent significance of heritage in a meaningful way. In order to contextualise fragmentary historical contents where history can be told, a strategy is to create a guided narrative via role-playing. Such an approach can strengthen the logical connections of cultural elements and facilitate creative synthesis within the virtual world. This project successfully reconstructed the Ningbo Sanjiangkou VR site in Yuan Dynasty combining VR technology and role-play game approach. The results with 80 pairs of participants suggest that VR role-playing can be beneficial in a number of ways. Firstly, it creates thematic interactivity which encourages users to explore the virtual heritage in a more entertaining way with task-oriented goals. Secondly, the experience becomes highly engaging since users can interpret a historical context through the perspective of specific roles that exist in past societies. Thirdly, personalisation allows open-ended sequences of the expedition, reinforcing user’s acquisition of procedural knowledge relative to the cultural domain. To sum up, role-playing in VR poses great potential for experiential learning as it allows users to interpret a historical context in a more entertaining way.

Keywords: experiential learning, maritime silk road, role-playing, virtual heritage, virtual reality

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Authors: Nattamon Srithammee, Ratchanikorn Chonchaiya

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This study explores the impact of augmented reality (AR) technology on mathematics education, focusing on area and volume concepts at Phra Dabos School. We developed a mobile AR application to present mathematical concepts innovatively. Using a mixed-methods approach, we assessed 79 students' knowledge before and after using the application. Results showed significant improvement in students' understanding, with average test scores increasing from 3.70 to 9.04 (p < 0.001, Cohen's d = 2.05). Students reported increased engagement and satisfaction. Our findings suggest AR technology can be a valuable tool in mathematics education, particularly for enhancing understanding abstract concepts. This study contributes to research on technology-enhanced learning in STEM education and provides insights for educators and educational technology developers.

Keywords: augmented reality, mathematics education, area and volume, educational technology, STEM education

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Authors: Meral Melek Unver

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This paper aims to explore ways to help English as a Foreign Language (EFL) learners notice and revise voice in English and raise their awareness of when and how to use active and passive voice to convey meaning in their written and spoken work. Because passive voice is commonly preferred in certain genres such as academic essays and news reports, despite the current trends promoting active voice, it is essential for learners to be fully aware of the meaning, use and form of passive voice to better communicate. The participants in the study are 22 EFL learners taking a one-year intensive English course at a university, who will receive English medium education (EMI) in their departmental studies in the following academic year. Data from students’ written and oral work was collected over a four-week period and the misuse or inaccurate use of passive voice was identified. The analysis of the data proved that they failed to make sensible decisions about when and how to use passive voice partly because the differences between their mother tongue and English and because they were not aware of the fact that active and passive voice would not alternate all the time. To overcome this, a Test-Teach-Test shape lesson, as opposed to a Present-Practice-Produce shape lesson, was designed and implemented to raise their awareness of the decisions they needed to make in choosing the voice and help them notice the meaning and use of passive voice through concept checking questions. The results first suggested that awareness raising activities on the meaning and use of voice in English would be beneficial in having accurate and meaningful outcomes from students. Also, helping students notice and renotice passive voice through carefully designed activities would help them internalize the use and form of it. As a result of the study, a number of activities are suggested to revise and notice passive voice as well as a short questionnaire to help EFL teachers to self-reflect on their teaching.

Keywords: voice in English, test-teach-test, passive voice, English language teaching

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Authors: Sanjar Davronov

Abstract:

Linguistic research of food discourse helps to analyze gastronomic picture of the world which plays important role in cross-cultural communications. 20 hours lecture can’t provide broad knowledge about national picture of the world of native speakers whose language being studied by future translator students. This abstract analyses how to research food discourse in “Cross-cultural (or lingvo-cultural) communication” lectures for ESL students. During compare Uzbek and American national meals, we found some specific features of food names in both countries. For example: If names of food includes advertising character in USA restaurant menus like: New York strip Sirloin crowned with Fresh – squeezed orange and lemon with a hint of garlic; Uzbek meals names are too simple, short and force general afford in underlining action – preparation process like: “Dimlama” (dimla(verb-to stew)+ma(suffix of past perfect like- stew- stewed). “Qovurdoq” (qovur (verb- to fry)+ doq (suffix of adverb like “fried one”) but these are the most delicious and difficult in preparing national meals however it is heritage of national cuisine. There are also similarity between US and Uzbek food names which has geographical color - South African Lobster tail; Qashqadaryo tandiri (lamb prepared in “tandir” typical national oven with pine leafs in Qashkadarya region). Food for European people contains physical context more than spiritual but in Asian literature especially Uzbek food has some pragmatic stuff: salt and bread (associates with hospitality and humanity), don’t be faithlessness 40 for owners of house where you where a guest. We share some teaching techniques for food discourse analyzing lectures.

Keywords: cross-cultural communications, food discourse, ESL lectures, linguistic research

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Authors: Naci Büyükkaracığan, Yıldırım Akyol

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Information technology is called the technology which provides transmission of information elsewhere regardless of time, location, distance. Today, information technology is providing the occurrence of ground breaking changes in all areas of our daily lives. Information can be reached quickly to millions of people with help of information technology. In this Study, effects of information technology on students for educations and their learning preferences were demonstrated with using data obtained from questionnaires administered to students of 2015-2016 academic year at Selcuk University Kadınhanı Faik İçil Vocational School Construction Department. The data was obtained by questionnaire consisting of 30 questions that was prepared by the researchers. SPSS 21.00 package programme was used for statistical analysis of data. Chi-square tests, Mann-Whitney U test, Kruskal-Wallis and Kolmogorov-Smirnov tests were used in the data analysis for Descriptiving statistics. In a study conducted with the participation of 61 students, 93.4% of students' reputation of their own information communication device (computer, smart phone, etc.) That have been shown to be at the same rate and to the internet. These are just a computer of itself, then 45.90% of the students. The main reasons for the students' use of the Internet, social networking sites are 85.24%, 13.11% following the news of the site, as seen. All student assignments in information technology, have stated that they use in the preparation of the project. When students acquire scientific knowledge in the profession regarding their preferred sources evaluated were seen exactly when their preferred internet. Male students showed that daily use of information technology while compared to female students was statistically significantly less. Construction Package program where students are eager to learn about the reputation of 72.13% and 91.80% identified in the well which they agreed that an indispensable element in the professional advancement of information technology.

Keywords: information technologies, computer, construction, internet, learning systems

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Authors: Mekamı Hayet, Bounoua Nacer, Benabderrahmane Sidahmed, Taleb Ahmed

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Pose estimation is an important task in computer vision. Though the majority of the existing solutions provide good accuracy results, they are often overly complex and computationally expensive. In this perspective, we propose the use of dimensionality reduction techniques to address the problem of facial pose estimation. Firstly, a face image is converted into one-dimensional time series using Hilbert space filling curve, then the approach converts these time series data to a symbolic representation. Furthermore, a distance matrix is calculated between symbolic series of an input learning dataset of images, to generate classifiers of frontal vs. profile face pose. The proposed method is evaluated with three public datasets. Experimental results have shown that our approach is able to achieve a correct classification rate exceeding 97% with K-NN algorithm.

Keywords: machine learning, pattern recognition, facial pose classification, time series

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Authors: Irdawati Bay Nalls

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This study looked at Bilingual First Language Acquisition (BFLA) Spanish-English Mexican Americans across an elementary public school in the United States and the possibility of maintaining harmonious bilingualism. Adopting a phenomenological approach, with a focus on the status of bilingualism in education within a marginalized community, classroom observations, and small group and one-on-one interviews were conducted. This study explored the struggles of these bilinguals as they acculturated in America through their attempt to blend heritage and societal languages and cultural practices. Results revealed that bilinguals as young as 5 years old expressed their need to retain Spanish as a heritage language while learning English. 12 years old foresee that Spanish will not be taught to them in schools and highlighted the need to learn Spanish outside the school environments. Their voices revealed counter-narratives on identity and the need to maintain harmonious bilingualism as these students strived to give equal importance to the learning of English and Spanish as first languages despite the setbacks faced.

Keywords: BFLA, Mexican-American, bilingual, harmonious bilingualism

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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World-wide spread of coronavirus grows the concern about planning for the excess demand of hospital services in response to COVID-19 pandemic. The surge in the hospital services demand beyond the current capacity leads to shortage of ICU beds and ventilators in some parts of US. In this study, we forecast the required number of hospital beds and possible shortage of beds in US during COVID-19 pandemic to be used in the planning and hospitalization of new cases. In this paper, we used a data on COVID-19 deaths and patients’ hospitalization besides the data on hospital capacities and utilization in US from publicly available sources and national government websites. we used a novel ensemble modelling of deep learning networks, based on stacking different linear and non-linear layers to predict the shortage in hospital beds. The results showed that our proposed approach can predict the excess hospital beds demand very well and this can be helpful in developing strategies and plans to mitigate this gap.

Keywords: COVID-19, deep learning, ensembled models, hospital capacity planning

Procedia PDF Downloads 151