Search results for: learning pattern

Authors: Shivani Jha, Klaus Baier, Rafiq Azzam, Ramakar Jha

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In India, generally people migrate from rural area to the urban area for better infra-structural facilities, high standard of living, good job opportunities and advanced transport/communication availability. In fact, unplanned urban development due to migration of people causes seriou damage to the land use, water pollution and available water resources. In the present work, an attempt has been made to use satellite data of different years for urban change detection of Chennai metropolitan city along with pattern analysis to generate future scenario of urban development using buffer zoning in GIS environment. In the analysis, SRTM (30m) elevation data and IRS-1C satellite data for the years 1990, 2000, and 2014, are used. The flow accumulation, aspect, flow direction and slope maps developed using SRTM 30 m data are very useful for finding suitable urban locations for industrial setup and urban settlements. Normalized difference vegetation index (NDVI) and Principal Component Analysis (PCA) have been used in ERDAS imagine software for change detection in land use of Chennai metropolitan city. It has been observed that the urban area has increased exponentially in Chennai metropolitan city with significant decrease in agriculture and barren lands. However, the water bodies located in the study regions are protected and being used as freshwater for drinking purposes. Using buffer zone analysis in GIS environment, it has been observed that the development has taken place in south west direction significantly and will do so in future.

Keywords: urban change, satellite data, the Chennai metropolis, change detection

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Authors: Cenk Sezen, Turgay Partal

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North Sea Caspian Pattern Index (NCP) refers to an atmospheric teleconnection between the North Sea and North Caspian at the 500 hPa geopotential height level. The aim of this study is to search for effects of NCP on annual and seasonal mean temperature and also annual and seasonal precipitation totals in the Aegean region of Turkey. The study contains the data that consist of 46 years obtained from nine meteorological stations. To determine the relationship between NCP and the climatic parameters, firstly the Pearson correlation coefficient method was utilized. According to the results of the analysis, most of the stations in the region have a high negative correlation NCPI in all seasons, especially in the winter season in terms of annual and seasonal mean temperature (statistically at significant at the 90% level). Besides, high negative correlation values between NCPI and precipitation totals are observed during the winter season at the most of stations. Furthermore, the NCPI values were divided into two group as NCPI(-) and NCPI(+), and then mean temperature and precipitation total values, which are grouped according to the NCP(-) and NCP(+) phases, were determined as annual and seasonal. During the NCPI(-), higher mean temperature values are observed in all of seasons, particularly in the winter season compared to the mean temperature values under effect of NCP(+). Similarly, during the NCPI(-) in winter season precipitation total values have higher than the precipitation total values under the effect of NCP(+); however, in other seasons there no substantial changes were observed between the precipitation total values. As a result of this study, significant proof is obtained with regards to the influences of NCP on the temperature and precipitation regime in the Aegean region of Turkey.

Keywords: Aegean region, NCPI, precipitation, temperature

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Authors: Ting Gao, Mingyue He

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Reconfigurable intelligent surfaces (RISs) are expected to be an important part of next-generation wireless communication networks due to their potential to reduce the hardware cost and energy consumption of millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) technology. However, owing to the lack of signal processing abilities of the RIS, the perfect channel state information (CSI) in RIS-assisted communication systems is difficult to acquire. In this paper, the uplink channel estimation for mmWave systems with a hybrid active/passive RIS architecture is studied. Specifically, a deep learning-based estimation scheme is proposed to estimate the channel between the RIS and the user. In particular, the sparse structure of the mmWave channel is exploited to formulate the channel estimation as a sparse reconstruction problem. To this end, the proposed approach is derived to obtain the distribution of non-zero entries in a sparse channel. After that, the channel is reconstructed by utilizing the least-squares (LS) algorithm and compressed sensing (CS) theory. The simulation results demonstrate that the proposed channel estimation scheme is superior to existing solutions even in low signal-to-noise ratio (SNR) environments.

Keywords: channel estimation, reconfigurable intelligent surface, wireless communication, deep learning

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Authors: M. K. Wijayarathna, S. M. Buddika Harshanath

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Monitoring student engagement has become a crucial part of the educational process and a reliable indicator of the capacity to retain information. As online learning classrooms are now more common these days, students' attention levels have become increasingly important, making it more difficult to check each student's concentration level in an online classroom setting. To profile student attention to various gradients of engagement, a study is a plan to conduct using machine learning models. Using a convolutional neural network, the findings and confidence score of the high accuracy model are obtained. In this research, convolutional neural networks are using to help discover essential emotions that are critical in defining various levels of participation. Students' attention levels were shown to be influenced by emotions such as calm, enjoyment, surprise, and fear. An improved virtual learning system was created as a result of these data, which allowed teachers to focus their support and advise on those students who needed it. Student participation has formed as a crucial component of the learning technique and a consistent predictor of a student's capacity to retain material in the classroom. Convolutional neural networks have a plan to implement the platform. As a preliminary step, a video of the pupil would be taken. In the end, researchers used a convolutional neural network utilizing the Keras toolkit to take pictures of the recordings. Two convolutional neural network methods are planned to use to determine the pupils' attention level. Finally, those predicted student attention level results plan to display on the graphical user interface of the System.

Keywords: HTML5, JavaScript, Python flask framework, AI, graphical user

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Authors: Thangavelu Muthukumar, Thotapalli Parvathaleswara Sastry

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Collagen is the most abundantly available connective tissue protein, which is being used as a biomaterial for various biomedical applications. Presently, fish wastes are disposed improperly which is causing serious environmental pollution resulting in offensive odour. Fish scales are promising source of Type I collagen. Medicinal plants have been used since time immemorial for treatment of various ailments of skin and dermatological disorders especially cuts, wounds, and burns. Developing biomaterials from the natural sources which are having wound healing properties within the search of a common man is the need of hour, particularly in developing and third world countries. With these objectives in view we have developed a wound dressing material containing fish scale collagen (FSC) incorporated with Macrotyloma uniflorum plant extract (PE). The wound dressing composite was characterized for its physiochemical properties using conventional methods. SEM image revealed that the composite has fibrous and porous surface which helps in transportation of oxygen as well as absorbing wound fluids. The biomaterial has shown 95% biocompatibility with required mechanical strength and has exhibited antimicrobial properties. This biomaterial has been used as a wound dressing material in experimental wounds of rats. The healing pattern was evaluated by macroscopic observations, panimetric studies, biochemical, histopathological observations. The results showed faster healing pattern in the wounds treated with CSPE compared to the other composites used in this study and untreated control. These experiments clearly suggest that CSPE can be used as wound/burn dressing materials.

Keywords: collagen, wound dressing, Macrotyloma uniflorum, burn dressing

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Authors: Sriraman Kothuri, Komal Teja Mattupalli

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In this paper we proposed a novel algorithm for recognizing hulls in a hand written digits. This is an extension to the work on “Digit Recognition Using Freeman Chain code”. In order to find out the hulls in a user given digit it is necessary to follow three steps. Those are pre-processing, Boundary Extraction and at last apply the Hull Detection system in a way to attain the better results. The detection of Hull Regions is mainly intended to increase the machine learning capability in detection of characters or digits. This can also extend this in order to get the hull regions and their intensities in Black Holes in Space Exploration.

Keywords: chain code, machine learning, hull regions, hull recognition system, SASK algorithm

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Authors: Li-Xin Shi, Meng-Fei Hu, Song-Chuan Zhao

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Dense particle suspensions composed of mixtures of particles and fluid are omnipresent in natural phenomena and in industrial processes. Dense particle suspension under shear may lose its uniform state to large local density and stress fluctuations which challenge the mean-field description of the suspension system. However, it still remains largely debated and far from fully understood of the internal mechanism. Here, a dynamics of a non-Brownian suspension is explored under horizontal swirling excitations, where high-density patches appear when the excitation frequency is increased beyond a threshold. These density patches are self-assembled into a hexagonal pattern across the system with further increases in frequency. This phenomenon is underlined by the spontaneous growth of density waves (instabilities) along the flow direction, and the motion of these density waves preserves the circular path and the frequency of the oscillation. To investigate the origin of the phenomena, the constitutive relationship calibrated by independent rheological measurements is implemented into a simplified two-phase flow model. And the critical instability frequency in theory calculation matches the experimental measurements quantitatively without free parameters. By further analyzing the model, the instability is found to be closely related to the discontinuous shear thickening transition of the suspension. In addition, the long-standing density waves degenerate into random fluctuations when replacing the free surface with rigid confinement. It indicates that the shear-thickened state is intrinsically heterogeneous, and the boundary conditions are crucial for the development of local disturbance.

Keywords: dense suspension, instability, self-organization, density wave

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Authors: Albin Wallace

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Part of the provision within educational institutions is the design, commissioning and implementation of ICT facilities to improve teaching and learning. Inevitably, these facilities focus largely on Internet Protocol (IP) based provisions including access to the World Wide Web, email, interactive software and hardware tools. Educators should be committed to the use of ICT to improve learning and teaching as well as to issues relating to the Internet and educational disadvantage, especially with respect to access and exclusion concerns. In this paper I examine some recent research into the issue of inequality and use of the Internet during which I discuss the causes and consequences of exclusion in the context of social inequality, digital literacy and digital inequality, also touching on issues of global inequality.

Keywords: inequality, internet, education, design

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Authors: Manish Subedi, Bijay Bartaula, Ashok R. Pant, Purbesh Adhikari, Sanjib K. Sharma

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Background: The pattern of glomerular disease varies worldwide. In absence of kidney disease/Kidney biopsy registry in Nepal, the exact etiology of different forms of glomerular disease is primarily unknown in our country. Method: We retrospectively analyzed 175 cases of renal biopsies performed from dated September 2014 to August 2016 at B. P. Koirala Institute of Health Sciences, Dharan, Nepal. Results: The commonest indication for renal biopsy was nephrotic syndrome (34.9%), followed by Systemic lupus erythematosus with suspected renal involvement (22.3%). Majority of patients were in the 30-60 year bracket (57.2%), with the mean age of the patients being 35.37 years. The average number of glomeruli per core was 13, with inadequate sampling in 5.1%. IgA nephropathy (17%) was found to be the most common primary glomerular disease, followed by membranous nephropathy (14.6%) and FSGS (14.6%). The commonest secondary glomerular disease was lupus nephritis. Complications associated with renal biopsy were pain at biopsy site in 18% of cases, hematuria in 6% and perinephric hematoma in 4% cases. Conclusion: The commonest primary and secondary glomerular disease was IgA nephropathy and lupus nephritis respectively. The high prevalence of Systemic lupus erythematosus with lupus nephritis among Nepalese in comparison with other developing countries warrants further evaluation. As an initial attempt towards documentation of glomerular diseases in the national context, this study should serve as a stepping stone towards the eventual establishment of a full-fledged national registry of glomerular diseases in Nepal.

Keywords: glomerular, Nepal, renal biopsy, systemic lupus erythematoses

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Authors: Daban Mohammed Haji

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This paper explores the application of constructive alignment in digital education at the University of Sulaimani, focusing specifically on the Language and Culture Center, Translation Department, and English Department. Constructive alignment, an outcome-based pedagogical framework developed by John Biggs, ensures that learning activities and assessments are directly aligned with the intended learning outcomes (ILOs). The study's findings reveal a significant gap in awareness and understanding of this pedagogical concept among lecturers. Many instructors are unfamiliar with constructive alignment, and those who have some knowledge of it face considerable challenges. These challenges include aligning learning activities and assessments with the ILOs and fostering higher-order cognitive skills as outlined in the SOLO taxonomy and revised Bloom’s taxonomy. To address this issue, the existing pedagogy center at the University of Sulaimani could play a pivotal role. This center has the potential to foster faculty development and promote the adoption of constructive alignment in online teaching. By leveraging the center's expertise and resources, a tailored program can be designed to enhance faculty understanding and application of this pedagogical framework.

Keywords: constructive alignment, student-centerdness, pedagogy, bologna process

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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: Boadi Agyekum

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In this study, a social location perspective is used to explore the challenges of creating opportunities that will foster lifelong education, inclusion, and equity for residents of rural communities in Ghana. The differentiated experiences of rural adults are under-researched and often unacknowledged in lifelong education literature and distance education policy. There is a need to examine carefully the structural inequalities that create disadvantages for residents of rural communities and women in pursuing distance education in designated cities in Ghana. The paper uses in-depth interviews to explore participants’ experiences of learning at a distance and to scrutinise the narratives of lifelong education. The paper reflects on the implications of the framework employed for educators and social justice in lifelong education. It further recommends the need to provide IT laboratories and fully online programs that would require stable and regular internet and access to ICT equipment for potential learning in rural communities. The social location approach presented a number of axes of diversity as comparatively more important than others; these included gender, age, education, work commitment, geography, and degree of social connectedness. This can inform lifelong education policy and programs to sustain quality education.

Keywords: equity, distance education, lifelong learning, social location, intersectionality, rural communities

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Authors: Pranav Ajmera, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare

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The cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR, that is, a value greater than 0.55, is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR from chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. We propose a deep learning-based model for automatic CTR calculation that can assist the radiologist with the diagnosis of cardiomegaly and optimize the radiology flow. The study population included 1012 posteroanterior (PA) CXRs from a single institution. The Attention U-Net deep learning (DL) architecture was used for the automatic calculation of CTR. A CTR of 0.55 was used as a cut-off to categorize the condition as cardiomegaly present or absent. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence (AI) assistance. The Attention U-Net model was highly specific in calculating the CTR. The model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. During the analysis, we observed that 51 out of 1012 samples were misclassified by the model when compared to annotations made by the expert radiologist. We further observed that the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Our segmentation-based AI model demonstrated high specificity and sensitivity for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows.

Keywords: cardiomegaly, deep learning, chest radiograph, artificial intelligence, cardiothoracic ratio

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Authors: Qinke Sun, Liang Zhou

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The ‘Dragon-Elephant Debate’ between China and India is an important manifestation of global multipolarity in the 21st century. The two rising powers have carried out economic reforms one after another in the interval of more than ten years, becoming the fastest growing developing country and emerging economy in the world. At the same time, the development differences between China and India have gradually attracted wide attention of scholars. Based on the continuous annual night light data (DMSP-OLS) from 1992 to 2012, this paper systematically compares and analyses the regional development differences between China and India by Gini coefficient, coefficient of variation, comprehensive night light index (CNLI) and hot spot analysis. The results show that: (1) China's overall expansion from 1992 to 2012 is 1.84 times that of India, in which China's change is 2.6 times and India's change is 2 times. The percentage of lights in unlighted areas in China dropped from 92% to 82%, while that in India from 71% to 50%. (2) China's new growth-oriented cities appear in Hohhot, Inner Mongolia, Ordos, and Urumqi in the west, and the declining cities are concentrated in Liaoning Province and Jilin Province in the northeast; India's new growth-oriented cities are concentrated in Chhattisgarh in the north, while the declining areas are distributed in Uttar Pradesh. (3) China's differences on different scales are lower than India's, and regional inequality of development is gradually narrowing. Gini coefficients at the regional and provincial levels have decreased from 0.29, 0.44 to 0.24 and 0.38, respectively, while regional inequality in India has slowly improved and regional differences are gradually widening, with Gini coefficients rising from 0.28 to 0.32. The provincial Gini coefficient decreased slightly from 0.64 to 0.63. (4) The spatial pattern of China's regional development is mainly east-west difference, which shows the difference between coastal and inland areas; while the spatial pattern of India's regional development is mainly north-south difference, but because the southern states are sea-dependent, it also reflects the coastal inland difference to a certain extent. (5) Beijing and Shanghai present a multi-core outward expansion model, with an average annual CNLI higher than 0.01, while New Delhi and Mumbai present the main core enhancement expansion model, with an average annual CNLI lower than 0.01, of which the average annual CNLI in Shanghai is about five times that in Mumbai.

Keywords: spatial pattern, spatial difference, DMSP-OLS, China, India

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Authors: Alexandros Pournaras, Anastasia Papadopoulou, Serafim Kontos, Anastasios Karakostas

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The air quality of modern cities is an important concern, as poor air quality contributes to human health and environmental issues. Reliable air quality forecasting has, thus, gained scientific and governmental attention as an essential tool that enables authorities to take proactive measures for public safety. In this study, the potential of Machine Learning (ML) models to forecast PM2.5 at local scale is investigated in the city of Thessaloniki, the second largest city in Greece, which has been struggling with the persistent issue of air pollution. ML models, with proven ability to address timeseries forecasting, are employed to predict the PM2.5 concentrations and the respective Air Quality Index 5-days ahead by learning from daily historical air quality and meteorological data from 2014 to 2016 and gathered from two stations with different land use characteristics in the urban fabric of Thessaloniki. The performance of the ML models on PM2.5 concentrations is evaluated with common statistical methods, such as R squared (r²) and Root Mean Squared Error (RMSE), utilizing a portion of the stations’ measurements as test set. A multi-categorical evaluation is utilized for the assessment of their performance on respective AQIs. Several conclusions were made from the experiments conducted. Experimenting on MLs’ configuration revealed a moderate effect of various parameters and training schemas on the model’s predictions. Their performance of all these models were found to produce satisfactory results on PM2.5 concentrations. In addition, their application on untrained stations showed that these models can perform well, indicating a generalized behavior. Moreover, their performance on AQI was even better, showing that the MLs can be used as predictors for AQI, which is the direct information provided to the general public.

Keywords: Air Quality, AQ Forecasting, AQI, Machine Learning, PM2.5

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Authors: Yiwen Jin, Jing Xiao, Pinfang Su

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The idea of adapting authentic materials in language teaching is from the communicative method in the 1970s. Different from the language in language textbooks, authentic materials is not deliberately written, it is from the native speaker’s real life and contains real information, which can meet social needs. It could improve learners ' interest, create authentic context and improve learners ' communicative competence. Authentic materials play an important role in CFL（Chinese as a foreign language） classroom. Different types of authentic materials can be used in different ways during learning and teaching. Because of the COVID-19 pandemic，a lot of Chinese learners are learning Chinese without the real language environment. Although there are some well-written textbooks, there is a certain distance between textbook language materials and daily life. Learners cannot automatically fill this gap. That is why it is necessary to apply authentic materials as a supplement to the language textbook to create the real context. Chinese teachers around the world are working together, trying to integrate the resources and apply authentic materials through different approach. They apply authentic materials in the form of new textbooks, manuals, apps and short videos they collect and create to help Chinese learning and teaching. A review of previous research on authentic materials and the Chinese teachers’ attempt to adapt it in the classroom are offered in this manuscript.

Keywords: authentic materials, Chinese as a second language, developmental use of digital resources, materials development for language teaching

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Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Xiaodong Li, Peng Gao, Chao-Jung Huang, Shiying Hao, Xuefeng B. Ling, Yongxia Han, Yaqi Zhang, Le Zheng, Chengyin Ye, Modi Liu, Minjie Xia, Changlin Fu, Bo Jin, Karl G. Sylvester, Eric Widen

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Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.

Keywords: cancer prediction, deep learning, electronic health records, pancreatic adenocarcinoma

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Authors: Mayowa A. Sofowora, Seraphin D. Eyono Obono

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The use of information and communication technologies such as computers, mobile phones and the internet is becoming prevalent in today’s world; and it is facilitating access to a vast amount of data, services, and applications for the improvement of people’s lives. However, this prevalence of ICTs is hampered by the problem of low income levels in developing countries to the point where people cannot timeously replace or repair their ICT devices when damaged or lost; and this problem serves as a motivation for this study whose aim is to examine the perceptions of teachers on the reliability of cellphones when used for teaching and learning purposes. The research objectives unfolding this aim are of two types: objectives on the selection and design of theories and models, and objectives on the empirical testing of these theories and models. The first type of objectives is achieved using content analysis in an extensive literature survey, and the second type of objectives is achieved through a survey of high school teachers from the ILembe and Umgungudlovu districts in the KwaZuluNatal province of South Africa. Data collected from this questionnaire based survey is analysed in SPSS using descriptive statistics and Pearson correlations after checking the reliability and validity of the questionnaire. The main hypothesis driving this study is that there is a relationship between the demographics and the attribution identity of teachers on one hand, and their perceptions on the reliability of cellphones on the other hand, as suggested by existing literature; except that attribution identities are considered in this study under three angles: intention, knowledge and ability, and action. The results of this study confirm that the perceptions of teachers on the reliability of cellphones for teaching and learning are affected by the school location of these teachers, and by their perceptions on learners’ cellphones usage intentions and actual use.

Keywords: attribution, cellphones, e-learning, reliability

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Authors: Kavita Goel, Donald Winchester

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In a world of information overload and work complexities, academics often struggle to create an online instructional environment enabling efficient and effective student learning. Research has established that students’ learning styles are different, some learn faster when taught using audio and visual methods. Attributes like prior knowledge and mental effort affect their learning. ‘Cognitive load theory’, opines learners have limited processing capacity. Cognitive load depends on the learner’s prior knowledge, the complexity of content and tasks, and instructional environment. Hence, the proper allocation of cognitive resources is critical for students’ learning. Consequently, a lecturer needs to understand the limits and strengths of the human learning processes, various learning styles of students, and accommodate these requirements while designing online assessments. As acknowledged in the cognitive load theory literature, visual and auditory explanations of worked examples potentially lead to a reduction of cognitive load (effort) and increased facilitation of learning when compared to conventional sequential text problem solving. This will help learner to utilize both subcomponents of their working memory. Instructional design changes were introduced at the case site for the delivery of the postgraduate business subjects. To make effective use of auditory and visual modalities, video recorded lectures, and key concept webinars were delivered to students. Videos were prepared to free up student limited working memory from irrelevant mental effort as all elements in a visual screening can be viewed simultaneously, processed quickly, and facilitates greater psychological processing efficiency. Most case study students in the postgraduate programs are adults, working full-time at higher management levels, and studying part-time. Their learning style and needs are different from other tertiary students. The purpose of the audio and visual interventions was to lower the students cognitive load and provide an online environment supportive to their efficient learning. These changes were expected to impact the student’s learning experience, their academic performance and retention favourably. This paper posits that these changes to instruction design facilitates students to integrate new knowledge into their long-term memory. A mixed methods case study methodology was used in this investigation. Primary data were collected from interviews and survey(s) of students and academics. Secondary data were collected from the organisation’s databases and reports. Some evidence was found that the academic performance of students does improve when new instructional design changes are introduced although not statistically significant. However, the overall grade distribution of student’s academic performance has changed and skewed higher which shows deeper understanding of the content. It was identified from feedback received from students that recorded webinars served as better learning aids than material with text alone, especially with more complex content. The recorded webinars on the subject content and assessments provides flexibility to students to access this material any time from repositories, many times, and this enhances students learning style. Visual and audio information enters student’s working memory more effectively. Also as each assessment included the application of the concepts, conceptual knowledge interacted with the pre-existing schema in the long-term memory and lowered student’s cognitive load.

Keywords: cognitive load theory, learning style, instructional environment, working memory

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Authors: Kornelius Ronald Demu, Dewi Retno Sari Saputro, Purnami Widyaningsih

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Human Development Index (HDI) is a standard measurement for a country's human development. Several factors may have influenced it, such as life expectancy, gross domestic product (GDP) based on the province's annual expenditure, the number of poor people, and the percentage of an illiterate people. The scatter plot between HDI and the influenced factors show that the plot does not follow a specific pattern or form. Therefore, the HDI's data in Indonesia can be applied with a nonparametric regression model. The estimation of the regression curve in the nonparametric regression model is flexible because it follows the shape of the data pattern. One of the nonparametric regression's method is a truncated spline. Truncated spline regression is one of the nonparametric approach, which is a modification of the segmented polynomial functions. The estimator of a truncated spline regression model was affected by the selection of the optimal knots point. Knot points is a focus point of spline truncated functions. The optimal knots point was determined by the minimum value of generalized cross validation (GCV). In this article were applied the data of Human Development Index with a truncated spline nonparametric regression model. The results of this research were obtained the best-truncated spline regression model to the HDI's data in Indonesia with the combination of optimal knots point 5-5-5-4. Life expectancy and the percentage of an illiterate people were the significant factors depend to the HDI in Indonesia. The coefficient of determination is 94.54%. This means the regression model is good enough to applied on the data of HDI in Indonesia.

Keywords: generalized cross validation (GCV), Human Development Index (HDI), knots point, nonparametric regression, truncated spline

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Authors: Jeyaseelan Gnanaseelan, Subajana Jeyaseelan

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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: Yan Lyu

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Japanese degree adverbs can be explained in different ways, which is hard for Japanese learners to comprehend. For instance, when ‘tyotto’ is used as a degree word, it can be interpreted literally or not. In the sentence ‘Ano mise, tyotto oishi yo. zehi iku to ii yo.’, ‘tyotto’ can be interpreted as a high degree contextually. Despite pragmatic factors, phonetics factors can also affect the interpretation of such ‘tyotto’. Concentrating on the pattern of ‘tyotto +adjective’, the paper aims to investigate the correlation between the interpretation of ‘tyotto’ and the phonetic factors in some specific contexts based on a listening experiment via PRAAT. It is also investigated that how the phonetic factors affect the interpretation of high degree adverbs, including ‘soutou’ , ‘totemo’ , ‘kanari’ and ‘sugoku’. In the experiment, Japanese speakers listened to sentences which were composed of degree adverbs and adjectives in different intonations and judged which degree the sentences expressed. Two conclusions can be drawn from the experiment results. Firstly, for adverbs expressing a high degree, in the pattern of ‘degree adverb + adjective’, either degree adverb or adjective is pronounced in a higher pitch, or both are highly pronounced, a higher degree can be expressed. Besides, with the insertion of geminate consonant and the extension of the vowel, the longer the duration of the degree adverb becomes, the higher degree can be expressed. Secondly, for ‘tyotto’, which expresses a low degree, the interpretation will be influenced by both phonetic and contextual factors. Phonetically, there are three factors causing ‘tyotto’ to be interpreted as a common degree or a high degree. The three factors are the high pitch of the modified adjective, the extended silence period of the geminate consonant and the change in the intonations of ‘tyotto’. In some contexts just like the comparison sentences, no matter how ‘tyotto + adjective’ is pronounced, ‘tyotto’ tends to be interpreted as a low degree literally.

Keywords: contextual interpretation, Japanese degree adverbs, phonetic interpretation, PRAAT

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Authors: Chutikarn Sriviboon, Witthaya Mekhum

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The purposes of this research were to analyze and evaluate successful factors in OTOP production process for the developing of learning center on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The research has been designed as a qualitative study to gather information from 30 OTOP producers in Bangkontee District, Samudsongkram Province. They were all interviewed on 3 main parts. Part 1 was about the production process including 1) production 2) product development 3) the community strength 4) marketing possibility and 5) product quality. Part 2 evaluated appropriate successful factors including 1) the analysis of the successful factors 2) evaluate the strategy based on Sufficiency Economic Philosophy and 3) the model of learning center on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The results showed that the production did not affect the environment with potential in continuing standard quality production. They used the raw materials in the country. On the aspect of product and community strength in the past 1 year, it was found that there was no appropriate packaging showing product identity according to global market standard. They needed the training on packaging especially for food and drink products. On the aspect of product quality and product specification, it was found that the products were certified by the local OTOP standard. There should be a responsible organization to help the uncertified producers pass the standard. However, there was a problem on food contamination which was hazardous to the consumers. The producers should cooperate with the government sector or educational institutes involving with food processing to reach FDA standard. The results from small group discussion showed that the community expected high education and better standard living. Some problems reported by the community included informal debt and drugs in the community. There were 8 steps in developing the model of learning center on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality.

Keywords: production process, OTOP, sufficiency economic philosophy, learning center

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Authors: Iftikhar U. Sikder

Abstract:

This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.

Keywords: concept approximation, granular computing, reducts, rough set theory, rule induction

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Authors: Selim M. Khan, Dustin D. Pearson, Tryggve Rönnqvist, Markus E. Nielsen, Joshua M. Taron, Aaron A. Goodarzi

Abstract:

Accumulation of radioactive radon gas in indoor air poses a serious risk to human health by increasing the lifetime risk of lung cancer and is classified by IARC as a category one carcinogen. Radon exposure risks are a function of geologic, geographic, design, and human behavioural variables and can change over time. Using time series and deep machine learning modelling, we analyzed long-term radon test outcomes as a function of building metrics from 25,489 Canadian and 38,596 Swedish residential properties constructed between 1945 to 2020. While Canadian and Swedish properties built between 1970 and 1980 are comparable (96–103 Bq/m³), innate radon risks subsequently diverge, rising in Canada and falling in Sweden such that 21st Century Canadian houses show 467% greater average radon (131 Bq/m³) relative to Swedish equivalents (28 Bq/m³). These trends are consistent across housing types and regions within each country. The introduction of energy efficiency measures within Canadian and Swedish building codes coincided with opposing radon level trajectories in each nation. Deep machine learning modelling predicts that, without intervention, average Canadian residential radon levels will increase to 176 Bq/m³ by 2050, emphasizing the importance and urgency of future building code intervention to achieve systemic radon reduction in Canada.

Keywords: radon health risk, time-series, deep machine learning, lung cancer, Canada, Sweden

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Authors: Hsin-Yi Huang, Ming-Sheng Liu, Jiun-Yan Shiau

Abstract:

Planning the order picking lists of warehouses to achieve when the costs associated with logistics on the operational performance is a significant challenge. In e-commerce era, this task is especially important productive processes are high. Nowadays, many order planning techniques employ supervised machine learning algorithms. However, the definition of which features should be processed by such algorithms is not a simple task, being crucial to the proposed technique’s success. Against this background, we consider whether unsupervised algorithms can enhance the planning of order-picking lists. A Zone2 picking approach, which is based on using clustering algorithms twice, is developed. A simplified example is given to demonstrate the merit of our approach.

Keywords: order picking, warehouse, clustering, unsupervised learning

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Authors: Daniel M. Wolff, Patricia Gray, Rafael de la Parra Venegas

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An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment.

Keywords: anthrophony, hydrophone, k-means, machine learning

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Authors: Cynthia Adlerstein-Grimberg, Andrea Bralic-Echeverría

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An extensive and controversial research debate discusses pedagogical documentation (PD) within early childhood education (ECE) as integral to ECE teachers' professional development. The literature converges in acknowledging that ICT integration in PD can be fundamental for children's and teachers' collaborative learning by making their processes visible and open to reflection. Controversial issues about PD emerge around ICT integration and the use of multimedia applications and platforms, displacing the physical experience involved in this pedagogical practice. Authors argue that online platforms make PD become a passive device to demonstrate accountability and performance. Furthermore, ICT integration would make educators inform children and families of pedagogical processes, positioning them more as consumers instead of involving them in collective thinking and pedagogical decision-making. This article analyses how pedagogical documentation mediated by a multimedia application (MAFApp) allows for the positive strengthening of an ECE pedagogical online community that thinks collectively about learning environments. In doing so, the paper shows how ICT integration supports ECE teachers' collective online thinking, enabling them to move from the controversial version of online PD, where they only act as informers of children's learning and assume a voyeuristic perspective, towards a collective online thinking that builds professional development and supports pedagogical decision-making about learning environments. This article answers How ECE teachers' pedagogical documentation evolves with ICT integration using the MAFApp multimedia application in a national ECE online community. From a posthumanist stance, this paper draws on an 18-month collaborative ethnographic immersion in Chile's unique public ECE online PD community. It develops a unique case study of an online ECE pedagogical community mediated by a multimedia application called MAFApp. This ECE online community includes 32 Chilean public kindergartens, 45 ECE teachers, and 72 assistants, who produced 534 pedagogical documentation. Fieldwork included 35 in-depth interviews, 13 discussion groups, and the constant comparison method for the PD coding. Findings show ICT integration in PD builds collective online thinking that evolves through four moments of growing complexity: 1) teachernalism of built environments, 2) onlookerism of children's anecdotes in learning environments; 3) storytelling of children's place-making, and 4) empowering pedagogies for co-creating learning environments. ICT integration through the MAFApp multimedia application enabled ECE teachers to build collective online thinking, making pedagogies of place visible and engaging children in co-constructing learning environments. This online PD is a continuous professional learning space for ECE teachers, empowering pedagogies of place. In conclusion, ICT integration into PD progressively empowers pedagogies of place in Chilean public ECE. Strengthening collective online thinking using the MAFApp multimedia application sharply contrasts with some recent PD research findings. ICT integration to PD enabled strong collective online thinking. Doing so makes PD operate as a place of professional development, pedagogical reflective encounters, and experimentation while inhabiting their own learning environments with children.

Keywords: early childhood education, ICT integration, multimedia application, online collective thinking, pedagogical documentation, professional development

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Authors: Aathira H.

Abstract:

Digital games can act as catalysts for educational transformation in the current scenario. Children and adolescence acquire this digital knowledge quickly and hence digital games can act as one of the most effective media for technology-mediated learning. Mobile gaming industries have seen the rise of a new trending genre of games, i.e., “Merge games” which is currently thriving in the market. This paper analysis on how gamifying historic and cultural narratives with merge mechanics can be an effective way to educate school children. Through the study of how merge mechanics in games have currently emerged as a trend., this paper argues how it can be integrated with a strong narrative which can convey history in an engaging way for education.

Keywords: game-based learning, merge mechanics, historical narratives, gaming innovations

Procedia PDF Downloads 87