Search results for: transfer learning

Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: Mohamed Ayoub

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The structures and energetics of various isomeric hydrogen bonded dimers, such as (FH…OC, FH…CO), (FH…CNH, FH…NCH), (FH…N2O, FH…ON2), and (FH…NHCO, FH…OCNH) have been investigated using DFT B3LYP with aug-cc-pVTZ basis set and by natural bond orbital (NBO) analysis. For each isomeric pair we calculated: H-bond energy (ΔEB…H), charge-transfer (QCT), where B is atom bearing lone-pairs in CO, CNH, NCH, N2O, and NHCO, H-bond distances (RB…H), the elongation of HF bond (ΔRHF) and the red-shift of HF stretching frequency (ΔVHF). We conclude that the principle difference in the relative stability between each isomeric pair is attributed to distinctive interaction of carbon and oxygen lone pairs of CO, carbon and nitrogen lone-pairs of CNH and NCH, and nitrogen and oxygen lone pairs of N2O and NHCO into the unfilled antibond on HF (σ*HF).

Keywords: charge transfer, computational chemistry, isomeric hydrogen bond, natural bond orbital

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Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

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Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

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Authors: Huey-Yeu Yan, Ching-Ying Lee, Hung-Ru Lin

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Background: Physical assessment is a vital clinical nursing competence. The gap between conventional teaching method and the way e-generation students’ preferred could be bridged owing to the support of Internet technology, i.e. interacting with online media to manage learning works. Nursing instructors in the wake of new learning pattern of the e-generation students are challenged to actively adjust and make teaching contents and methods more versatile. Objective: The objective of this research is to explore the effects on teaching and learning with live webcast-assisted on a specific topic, Physical Assessment technique, on a designated group of young nursing majors. It’s hoped that, with a way of nursing instructing, more versatile learning resources may be provided to facilitate self-directed learning. Design: This research adopts a cross-sectional descriptive survey. The instructor demonstrated physical assessment techniques and operation procedures via live webcast broadcasted online to all students. It increased both the off-time interaction between teacher and students concerning teaching materials. Methods: A convenient sampling was used to recruit a total of 52 nursing-majors at a certain university. The nursing majors took two-hour classes of Physical Assessment per week for 18 weeks (36 hrs. in total). The instruction covered four units with live webcasting and then conducted an online anonymous survey of learning outcomes by questionnaire. The research instrument was the online questionnaire, covering three major domains—online media used, learning outcome evaluation and evaluation result. The data analysis was conducted via IBM SPSS Statistics Version 2.0. The descriptive statistics was undertaken to describe the analysis of basic data and learning outcomes. Statistical methods such as descriptive statistics, t-test, ANOVA, and Pearson’s correlation were employed in verification. Results: Results indicated the following five major findings. (1) learning motivation, about four fifth of the participants agreed the online instruction resources are very helpful in improving learning motivation and raising the learning interest. (2) learning needs, about four fifth of participants agreed it was helpful to plan self-directed practice after the instruction, and meet their needs of repetitive learning and/or practice at their leisure time. (3) learning effectiveness, about two third agreed it was helpful to reduce pre-exam anxiety, and improve their test scores. (4) course objects, about three fourth agreed that it was helpful to achieve the goal of ‘executing the complete Physical Assessment procedures with proper skills’. (5) finally, learning reflection, about all of participants agreed this experience of online instructing, learning, and practicing is beneficial to them, they recommend instructor to share with other nursing majors, and they will recommend it to fellow students too. Conclusions: Live webcasting is a low-cost, convenient, efficient and interactive resource to facilitate nursing majors’ motivation of learning, need of self-directed learning and practice, outcome of learning. When live webcasting is integrated into nursing teaching, it provides an opportunity of self-directed learning to promote learning effectiveness, as such to fulfill the teaching objective.

Keywords: innovative teaching, learning effectiveness, live webcasting, physical assessment technique

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Authors: Bahia Braktia, Ana Marcela Montenegro Sanchez

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Social media, such as Facebook, Twitter, and YouTube, has been used for teaching and learning for quite some time. These platforms have been proven to be a good tool to improve various language skills, students’ performance of the English language, motivation as well as trigger the authentic language interaction. However, little is known about the potential effects of social media usage on the learning performance of Arabic language learners. The present study explores the potential role that the social media technologies play in learning Arabic as a foreign language at a university in Southeast of United States. In order to investigate this issue, an online survey was administered to examine the perceptions and attitudes of American students learning Arabic. The research questions were: How does social media, specifically Facebook and Twitter, impact the students' Arabic language skills, and what is their attitude toward it? The preliminary findings of the study showed that students had a positive attitude toward the use of social media to enhance their Arabic language skills, and that they used a range of social media features to expose themselves to the Arabic language and communicate in Arabic with native Arabic speaking friends. More detailed findings will be shared in the light data analysis with the audience during the presentation.

Keywords: foreign language learning, social media, students’ perceptions, survey

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Authors: Michelle Jennett, Jana Dengler, Maytal Perlman

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Background: Cervical spinal cord injury (SCI) is a devastating event that results in upper limb paralysis, loss of independence, and disability. People living with cervical SCI have identified improvement of upper limb function as a top priority. Nerve and tendon transfer surgery has successfully restored upper limb function in cervical SCI but is not universally used or available to all eligible individuals. This exploratory mixed-methods study used an implementation science approach to better understand these factors that influence access to upper limb reconstruction in the Canadian context and design an intervention to increase access to care. Methods: Data from the Canadian Institute for Health Information’s Discharge Abstracts Database (CIHI-DAD) and the National Ambulatory Care Reporting System (NACRS) were used to determine the annual rate of nerve transfer and tendon transfer surgeries performed in cervical SCI in Canada over the last 15 years. Semi-structured interviews informed by the consolidated framework for implementation research (CFIR) were used to explore Ontario healthcare provider knowledge and practices around upper limb reconstruction. An inductive, iterative constant comparative process involving descriptive and interpretive analyses was used to identify themes that emerged from the data. Results: Healthcare providers (n = 10 upper extremity surgeons, n = 10 SCI physiatrists, n = 12 physical and occupational therapists working with individuals with SCI) were interviewed about their knowledge and perceptions of upper limb reconstruction and their current practices and discussions around upper limb reconstruction. Data analysis is currently underway and will be presented. Regional variation in rates of upper limb reconstruction and trends over time are also currently being analyzed. Conclusions: Utilization of nerve and tendon transfer surgery to improve upper limb reconstruction in Canada remains low. There are a complex array of interrelated individual-, provider- and system-level barriers that prevent individuals with cervical SCI from accessing upper limb reconstruction. In order to offer equitable access to care, a multi-modal approach addressing current barriers is required.

Keywords: cervical spinal cord injury, nerve and tendon transfer surgery, spinal cord injury, upper extremity reconstruction

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Authors: Robert John Godby

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To be responsive now and in the future, workplaces must address the demands of multicultural teams as they become more common elements of the global labor force. This is especially the case for aged care due to the aging population, industry growth and migrant recruitment. This research identifies influences on and improvements for learning in these environments. Its unique contribution is to illuminate how culturally diverse workplaces can work and learn together more effectively. A mixed-methods approach was used to gather data about this topic in two phases. Firstly, the research methods included a survey of 102 aged care workers around Australia from two multi-site aged care organisations. The questionnaire elicited both quantitative and qualitative data about worker characteristics and perspectives on working and learning in aged care. Secondly, a case study of one aged care worksite was formulated drawing on worksite information and interviews with workers. A review of the literature suggests that learning in multicultural work environments is influenced by three main factors: 1) the individual workers themselves, 2) their interaction with each other and 3) the environment in which they work. There are various accounts of these three factors, how they are manifested and how they lead to a change in workers’ disposition, knowledge, or expertise when confronted with new circumstances. The study has found that a key individual factor influencing learning is cultural background. Their unique view of the world was shown to affect their approach to both their work and co-working. Interactional factors suggest that the high requirement for collaboration in aged care positively supports learning in this context; however, it can be hindered by cultural bias and spoken accent. The study also found that environmental factors, such as disruptions caused by the pandemic, were another key influence. For example, the need to wear face masks hindered the communication needed for workplace learning. This was especially challenging due to the diverse language backgrounds and abilities within the teams. Potential improvements for learning in multicultural aged care work environments were identified. These include more frequent and structured inter-peer learning (e.g. buddying), communication training (e.g. English language usage for both native and non-native speaking workers) and support for cross-cultural habitude (e.g. recognizing and adapting to cultural differences). Workplace learning in cross-cultural aged care environments is an area that is not extensively dealt with in the literature. This study addresses this gap and holds the potential to contribute practical insights to aged care and other diverse industries.

Keywords: cross-cultural learning, learning in aged care, migrant learning, workplace learning

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Authors: C.W. Kan

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Softening applied on textile products based on several reasons. First, the synthetic detergent removes natural oils and waxes, thus lose the softness. Second, compensate the harsh handle of resin finishing. Also, imitate natural fibres and improve the comfort of fabric are the reasons to apply softening. There are different types of softeners for softening finishing of textiles, nonionic softener, anionic softener, cationic softener and silicone softener. The aim of this study is to illustrate the proper application of different softeners and their final softening effect in textiles. The results could also provide guidance note to the students in learning this topic. Acknowledgment: Authors would like to thank the financial support from the Hong Kong Polytechnic University for this work.

Keywords: learning materials, softening, textiles, effect

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Authors: Imen Elleuch, Wael Ouarda, Gargouri Bilel

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We proposes in this paper a Deep Learning based approach to classify text in order to enrich an Arabic ontology based on the objects classes of Gaston Gross. Those object classes are defined by taking into account the syntactic and semantic features of the treated language. Thus, our proposed approach is a hybrid one. In fact, it is based on the one hand on the object classes that represents a knowledge based-approach on classification of text and in the other hand it uses the deep learning approach that use the word embedding-based-approach to classify text. We have applied our proposed approach on a corpus constructed from an Arabic dictionary. The obtained semantic classification of text will enrich the Arabic objects classes ontology. In fact, new classes can be added to the ontology or an expansion of the features that characterizes each object class can be updated. The obtained results are compared to a similar work that treats the same object with a classical linguistic approach for the semantic classification of text. This comparison highlight our hybrid proposed approach that can be ameliorated by broaden the dataset used in the deep learning process.

Keywords: deep-learning approach, object-classes, semantic classification, Arabic

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Authors: Maiya Smith, Tyler Thorne

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Background: The Pacific faces multiple healthcare crises, including high rates of noncommunicable diseases, infectious disease outbreaks, and susceptibility to natural disasters. These issues are expected to worsen in the coming decades, increasing the burden on an already understaffed healthcare system. Telehealth is not new to the Pacific, but improvements in technology and accessibility have increased its utility and have already proven to reduce costs and increase access to care in remote areas. Telehealth includes distance learning; a form of education that can help alleviate many healthcare issues by providing continuing education to healthcare professionals and upskilling staff, while decreasing costs. This study examined distance learning programs at the Ministry of Health in the Pacific nation of Palau and identified key elements to their successful distance learning programs. Methods: Staff at the Belau National Hospital in Koror, Palau as well as private practitioners were interviewed to assess distance learning programs utilized. This included physicians, IT personnel, public health members, and department managers of allied health. In total, 36 people were interviewed. Standardized questions and surveys were conducted in person throughout the month of July 2019. Results: Two examples of successful distance learning programs were identified. Looking at the factors that made these programs successful, as well as consulting with staff who undertook other distance learning programs, four factors for success were determined: having a cohort, having a facilitator, dedicated study time off from work, and motivation. Discussion: In countries as geographically isolated as the Pacific, with poor access to specialists and resources, telehealth has the potential to radically change how healthcare is delivered. Palau shares similar resources and issues as other countries in the Pacific and the lessons learned from their successful programs can be adapted to help other Pacific nations develop their own distance learning programs.

Keywords: distance learning, Pacific, Palau, telehealth

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Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

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In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

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Authors: Slava Kalyuga

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There are two opposing major points of view on the optimal degree of initial instructional guidance that is usually discussed in the literature by the advocates of the corresponding learning approaches. Using unguided or minimally guided problem-solving tasks prior to explicit instruction has been suggested by productive failure and several other instructional theories, whereas an alternative approach - using fully guided worked examples followed by problem solving - has been demonstrated as the most effective strategy within the framework of cognitive load theory. An integrated approach discussed in this paper could combine the above frameworks within a broader theoretical perspective which would allow bringing together their best features and advantages in the design of learning tasks for STEM education. This paper represents a systematic review of the available empirical studies comparing the above alternative sequences of instructional methods to explore effects of several possible moderating factors. The paper concludes that different approaches and instructional sequences should coexist within complex learning environments. Selecting optimal sequences depends on such factors as specific goals of learner activities, types of knowledge to learn, levels of element interactivity (task complexity), and levels of learner prior knowledge. This paper offers an outline of a theoretical framework for the design of complex learning tasks in STEM education that would integrate explicit instruction and inquiry (exploratory, discovery) learning approaches in ways that depend on a set of defined specific factors.

Keywords: cognitive load, explicit instruction, exploratory learning, worked examples

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Authors: Jonathan Frank, Michelle Salmona

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We conducted quantitative and qualitative analyses of nurses’ perceived ease of use of electronic medical records and telemedicine in the Cook Islands. We examined antecedents of perceived ease of use through the lens of social construction of learning, and cultural diffusion. Our findings confirmed expected linkages between PEOU, attitudes and intentions. Interviews with nurses suggested infrastructural barriers to engaged learning. We discussed managerial implications of our findings, and areas of interest for future research.

Keywords: health information technology, ICT4D, TAM, developing countries

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Authors: Fiseha M. Guangul, Annissa Muhammed, Aja O. Chikere

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Group task is one method to create the conducive environment for the active teaching-learning process. Performing group task with active involvement of students will benefit the students in many ways. However, in most cases all students do not participate actively in the group task, and hence the intended benefits are not acquired. This paper presents the improvements of students’ participation in the group task and learning from the group task by introducing different techniques to enhance students’ participation. For the purpose of this research Carpentry and Joinery II (WT-392) course from Wood Technology Department at Adama Science and Technology University was selected, and five groups were formed. Ten group tasks were prepared and the first five group tasks were distributed to the five groups in the first day without introducing the techniques that are used to enhance participation of students in the group task. On another day, the other five group tasks were distributed to the same groups and various techniques were introduced to enhance students’ participation in the group task. The improvements of students’ learning from the group task after the implementation of the techniques. After implementing the techniques the evaluation showed that significant improvements were obtained in the students’ participation and learning from the group task.

Keywords: group task, students participation, active learning, the evaluation method

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Authors: Beulah Rani Inbanathan

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In the present era, it is vivid from the numerous outcomes that data privacy is being compromised in various ways. Machine learning is one technology that uses the centralized server, and then data is given as input which is being analyzed by the algorithms present on this mentioned server, and hence outputs are predicted. However, each time the data must be sent by the user as the algorithm will analyze the input data in order to predict the output, which is prone to threats. The solution to overcome this issue is federated learning, where the models alone get updated while the data resides on the local machine and does not get exchanged with the other local models. Nevertheless, even on these local models, there are chances of data poisoning, and it is crystal clear from various experiments done by many people. This paper delves into many ways where data poisoning occurs and the many methods through which it is prevalent that data poisoning still exists. It includes the poisoning attacks on IoT devices, Edge devices, Autoregressive model, and also, on Industrial IoT systems and also, few points on how these could be evadible in order to protect our data which is personal, or sensitive, or harmful when exposed.

Keywords: data poisoning, federated learning, Internet of Things, edge computing

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Authors: Paul Koffitse Agobia

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In 2020 the Ministry of Education in Ghana and the National Council for Curriculum and Assessment (NaCCA) rolled out a new curriculum, Common Core Programme (CCP) for Basic 7 to 10, that lays emphasis on character building and values which are important to the Ghanaian society by providing education that will produce character–minded learners, with problem solving skills, who can play active roles in dealing with the increasing challenges facing Ghana and the global society. Therefore, learning and teaching approaches that prioritise the use of digital learning resources and active learning are recommended. The new challenge facing Ghanaian teachers is the ability to use new technologies together with the appropriate content pedagogical knowledge to help learners develop, aside the communication skills in French, the essential 21st century skills as recommended in the new curriculum. This article focusses on the pedagogical approaches that are recommended by NaCCA. The study seeks to examine French language teachers’ understanding of the recommended pedagogical approaches and how they use digital learning resources in class to foster the development of these essential skills and values. 54 respondents, comprised 30 teachers and 24 head teachers, were selected in 6 Junior High schools in rural districts (both private and public) and 6 from Junior High schools in an urban setting. The schools were selected in three regions: Volta, Central and Western regions. A class observation checklist and an interview guide were used to collect data for the study. The study reveals that some teachers adopt teaching techniques that do not promote active learning. They demonstrate little understanding of the core competences and values, therefore, fail to integrate them in their lessons. However, some other teachers, despite their lack of understanding of learning and teaching philosophies, adopted techniques that can help learners develop some of the core competences and values. In most schools, digital learning resources are not utilized, though teachers have smartphones or laptops.

Keywords: active learning, core competences, digital learning resources, pedagogical approach, values.

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Authors: Golnush Masghati-Amoli, Paul Chin

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Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

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Authors: Yi-Cheng Tian, Miin-Shen Yang

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The mixture likelihood approach to clustering is a popular clustering method where the expectation and maximization (EM) algorithm is the most used mixture likelihood method. In the literature, the EM algorithm had been used for mixture regression models. However, these EM mixture regression algorithms are sensitive to initial values with a priori number of clusters. In this paper, to resolve these drawbacks, we construct a learning-based schema for the EM mixture regression algorithm such that it is free of initializations and can automatically obtain an approximately optimal number of clusters. Some numerical examples and comparisons demonstrate the superiority and usefulness of the proposed learning-based EM mixture regression algorithm.

Keywords: clustering, EM algorithm, Gaussian mixture model, mixture regression model

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Authors: Colleen McMillan, Alice Schmidt-Hanbidge, Beth Archer-Kuhn, Heather Boynton, Judith Hughes

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It is well known that social work students enter the profession with higher scores of adverse childhood experiences (ACE). Add to that the fact that COVID-19 has forced higher education institutions to shift to online teaching and learning, where students, faculty and field educators in social work education have reported increased stressors as well as posing challenges in developing relationships with students and being able to identify mental health challenges including those related to trauma. This multi-institutional project included three Canadian post-secondary institutions at five sites (the University of Waterloo, the University of Calgary and the University of Manitoba) and partners; Desire To Learn (D2L), The Centre for Teaching Excellence at the University of Waterloo and the Taylor Institute for Teaching and Learning. A sequential mixed method research design was used. Survey data was collected from students, faculty and field education staff from the 3 universities using the Qualtrics Insight Platform, followed by virtual focus group data with students to provide greater clarity to the quantitative data. Survey data was analyzed using SPSS software, while focus group data was transcribed verbatim and organized with N-Vivo 12. Thematic analysis used line-by-line coding and constant comparative methods within and across focus groups. The following three objectives of the study were achieved: 1) Establish a Canadian baseline on trauma informed pedagogy and student experiences of trauma informed teaching in the online higher education environment during a pandemic; 2) Identify and document educator and student experiences of online learning regarding the ability to process trauma experiences; and, 3) Transfer the findings into a trauma informed pedagogical model for Social Work as a first step toward developing a universal trauma informed teaching model. The trauma informed pedagogy model would be presented in relation to the study findings.

Keywords: trauma informed pedagogy, higher education, social work, mental health

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Authors: Samar Alharbi, Yota Dimitriadi

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The literature on educational technology in Saudi Arabia reveals female learners’ unwillingness to study fully online courses in higher education despite the fact that Saudi universities have offered a variety of online degree programmes to undergraduate students in many regions of the country. The root causes keeping female students from successfully learning in online environments are limited social interaction, lack of motivation and difficulty with the use of e-learning platforms. E-immediacy remains an important method of online teaching to enhance students’ interaction and support their online learning. This study explored Saudi female students’ perceptions, as well as the experiences of lecturers’ immediacy behaviours in online environments, who participate in fully online courses using Blackboard at a Saudi university. Data were collected through interviews with focus groups. The three focus groups included five to seven students each. The female participants were asked about lecturers’ e-immediacy behaviours and which e-immediacy behaviours were important for an effective learning environment. A thematic analysis of the data revealed three main themes: the encouragement of student interaction, the incorporation of social media and addressing the needs of students. These findings provide lecturers with insights into instructional designs and strategies that can be adopted in using e-immediacy in effective ways, thus improving female learners’ interactions as well as their online learning experiences.

Keywords: e-learning, female students, higher education, immediacy

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Authors: Aida Nurul Safitri, Rosita Sari

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Physics is science, which in its learning there are some product such as theory, fact, concept, law and formula. So that to understand physics lesson students not only need a theory or concept but also mathematical calculation to solve physics problem through formula or equation. This is can be taken from mathematics lesson which obtained by students. This research is to know the influence of mathematics learning outcomes towards physics ability in Senior High School through authentic assessment system. Based on the researches have been discussed, is obtained that mathematic lesson have an important role in physics learning but it according to one aspect only, namely cognitive aspect. In Indonesia, curriculum of 2013 reinforces displacement in the assessment, from assessment through test (measuring the competence of knowledge based on the result) toward authentic assessment (measuring the competence of attitudes, skills, and knowledge based on the process and results). In other researches are mentioned that authentic assessment system give positive responses for students to improve their motivation and increase the physics learning in the school.

Keywords: authentic assessment, curriculum of 2013, mathematic, physics

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Authors: Y. T. Gee, I. N. Umar

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Students’ achievement and motivation in learning English in Malaysia is a worrying trend as it is lagging behind several other countries in Asia. Thus, necessary actions have to be taken by the parties concerned to overcome this problem. The purpose of this research was to study the effects of drill and practice courseware on students’ achievement and motivation in learning English language. A multimedia courseware was developed for this purpose. The independent variable was the drill and practice courseware while the dependent variables were the students’ achievement and motivation. Their achievement was measured using pre-test and post-test scores, while motivation was measured using a questionnaire adapted from Keller’s (1979) Instructional Materials Motivation Scale. A total of 60 students from three vernacular primary schools in a northern state in Malaysia were randomly selected in this study. The findings indicate: (1) a significant difference between the students’ pre-test and post-test scores after using the courseware, (2) no significant difference in the achievement score between male and female students after using the courseware, (3) a significant difference in motivation score between the female and the male students, and (4) while the female students scored significantly higher than the male students in the aspects of relevance, confidence and satisfaction, no significant difference in terms of attention was observed between them. Overall, the findings clearly indicate that although the female students are significantly more motivated than their male students, they are equally good in terms of achievement after learning from the courseware. Through this study, the drill and practice courseware is proven to influence the students’ learning and motivation.

Keywords: courseware, drill and practice, English learning, motivation

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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Gliomas are most challenging and aggressive type of tumors which appear in different sizes, locations, and scattered boundaries. CNN is most efficient deep learning approach with outstanding capability of solving image analysis problems. A fully automatic deep learning based 2D-CNN model for brain tumor segmentation is presented in this paper. We used small convolution filters (3 x 3) to make architecture deeper. We increased convolutional layers for efficient learning of complex features from large dataset. We achieved better results by pushing convolutional layers up to 16 layers for HGG model. We achieved reliable and accurate results through fine-tuning among dataset and hyper-parameters. Pre-processing of this model includes generation of brain pipeline, intensity normalization, bias correction and data augmentation. We used the BRATS-2015, and Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.81 for complete, 0.79 for core, 0.80 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, HGG

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Authors: Silvia Hvozdíková, Eva Stranovská

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The purpose of the article is to discuss the results of the research conducted during the period of two semesters paying attention to selected factors of foreign language reading comprehension through the means of Linguistic Intervention Program. The Linguistic Intervention Program was designed for the purpose of the current research. It refers to such method of foreign language teaching which emphasized active social learning, creative drama strategies, self-directed learning. The research sample consisted of 360 respondents, foreign language learners ranging from 13 – 17 years of age. Specifically designed questionnaire and a standardized foreign language reading comprehension tests were applied to serve the purpose. The outcomes of the research recorded significant results towards significant relationship between selected elements of the Linguistic Intervention Program and the academic achievements in the factors of reading comprehension.

Keywords: foreign language learning, linguistic intervention program, reading comprehension, social learning

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Authors: Genute Gedviliene, Vidmantas Tutlys

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The aim of this paper is to reveal the deployment and development of the social competence in the higher education programmes of adult education and in the continuing training and competence development of the andragogues. There will be compared how the issues of cooperation and communication in the learning and teaching processes are treated in the study programmes and in the courses of continuing training of andragogues. Theoretical and empirical research methods were combined for research analysis. For the analysis the following methods were applied: 1) Literature and document analysis helped to highlight the communication and cooperation as fundamental phenomena of the social competence, it’s important for the adult education in the context of digitalization and globalization. There were also analyzed the research studies on the development of social competence in the field of andragogy, as well as on the place and weight of the social competence in the overall competence profile of the andragogue. 2) The empirical study is based on questionnaire survey method. The population of survey consists of 240 students of bachelor and master degree studies of andragogy in Lithuania and of 320 representatives of the different bodies and institutions involved in the continuing training and professional development of the adult educators in Lithuania. The themes of survey questionnaire were defined on the basis of findings of the literature review and included the following: 1) opinions of the respondents on the role and place of a social competence in the work of andragogue; 2) opinions of the respondents on the role and place of the development of social competence in the curricula of higher education studies and continuing training courses; 3) judgements on the implications of the higher education studies and courses of continuing training for the development of social competence and it’s deployment in the work of andragogue. Data analysis disclosed a wide range of ways and modalities of the deployment and development of social competence in the preparation and continuing training of the adult educators. Social competence is important for the students and adult education providers not only as the auxiliary capability for the communication and transfer of information, but also as the outcome of collective learning leading to the development of new capabilities applied by the learners in the learning process, their professional field of adult education and their social life. Equally so, social competence is necessary for the effective adult education activities not only as an auxiliary capacity applied in the teaching process, but also as a potential for improvement, development and sustainability of the didactic competence and know-how in this field. The students of the higher education programmes in the field of adult education treat social competence as important generic capacity important for the work of adult educator, whereas adult education providers discern the concrete issues of application of social competence in the different processes of adult education, starting from curriculum design and ending with assessment of learning outcomes.

Keywords: adult education, andragogues, social competence, curriculum

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Authors: Qijiao He, Lin Ye

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To evaluate the effect of the ribs on internal structure in film hole and the film cooling performance on outer surface, the numerical study investigates on the effects of rib configuration on the film cooling performance with ribbed cross-flow coolant channel. The base smooth case and three ribbed cases, including the continuous rib case and two cross-truncated rib cases with different arrangement, are studied. The distributions of adiabatic film cooling effectiveness and heat transfer coefficient are obtained under the blowing ratios with the value of 0.5 and 1.0, respectively. A commercial steady RANS (Reynolds-averaged Navier-Stokes) code with realizable k-ε turbulence model and enhanced wall treatment were performed for numerical simulations. The numerical model is validated against available experimental data. The two cross-truncated rib cases produce approximately identical cooling effectiveness compared with the smooth case under lower blowing ratio. The continuous rib case significantly outperforms the other cases. With the increase of blowing ratio, the cases with ribs are inferior to the smooth case, especially in the upstream region. The cross-truncated rib I case produces the highest cooling effectiveness among the studied the ribbed channel case. It is found that film cooling effectiveness deteriorates with the increase of spiral intensity of the cross-flow inside the film hole. Lower spiral intensity leads to a better film coverage and thus results in better cooling effectiveness. The distinct relative merits among the cases at different blowing ratios are explored based on the aforementioned dominant mechanism. With regard to the heat transfer coefficient, the smooth case has higher heat transfer intensity than the ribbed cases under the studied blowing ratios. The laterally-averaged heat transfer coefficient of the cross-truncated rib I case is higher than the cross-truncated rib II case.

Keywords: cross-flow, cross-truncated rib, film cooling, numerical simulation

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Authors: Vishnu Pratap Singh Kirar

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Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

Keywords: neural network, backpropagation, local minima, fast convergence rate

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Authors: Yoones Tavoosy

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This present study concentrates on the organizational implication the Iranian students with learning difficulties (LD) experience when they write an English essay. Particularly, the present study aims at exploring students' structural problems in EFL essay writing. A mixed method research design was employed including a questionnaire and a semi-structured in-depth interview. Technical Data Analysis of findings exposed that students experience a number of difficulties in the structure of EFL essay writing. Discussion and implications of these findings are presented respectively.

Keywords: Iranian students, learning difficulties, organizational implication, writing

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Authors: Melvin Chan, Chew Lee Teo

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There has been a growing emphasis in elevating the teachers’ proficiency and competencies through continuing professional development (CPD) opportunities. In this era of a Volatile, Uncertain, Complex, Ambiguous (VUCA) world, teachers are expected to be collaborative designers, critical thinkers and creative builders. However, many of the CPD structures are still revolving in the model of transmission, which stands in contradiction to the cultivation of future-ready teachers for the innovative world of emerging technologies. This article puts forward the framing of CPD through a Principle-Based, Technological-Driven Knowledge Building Approach grounded in the essence of andragogy and progressive learning theories where growth is best exemplified through an authentic immersion in a social/community experience-based setting. Putting this Knowledge Building Professional Development Model (KBPDM) in operation via a Professional Learning Team (PLT) situated in a Secondary School in Singapore, research findings reveal that the intervention has led to a fundamental change in the learning paradigm of the teachers, henceforth equipping and empowering them successfully in their pedagogical design and practices for a 21^st century classroom experience. This article concludes with the possibility in leveraging the Learning Analytics to deepen the CPD experiences for educators.

Keywords: continual professional development, knowledge building, learning paradigm, principle-based

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Authors: Donald C. Lantu, Henndy Ginting, M. Yorga Permana, Dany M. A. Ramdlany

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We constructed a training program for top-talents of a Bank with Scharmer Theory-U as the model. In this training program, we implemented the action learning perspective, as it is claimed to be the most effective one currently available. In the process, participants were encouraged to be more involved, especially compared to traditional lecturing. The goal of this study is to assess the effectiveness of this particular training. The program consists of six days non-residential workshop within two months. Between each workshop, the participants were involved in the works of action learning group. They were challenged by dealing with the real problem related to their tasks at work. The participants of the program were 30 best talents who were chosen according to their yearly performance. Using paired difference statistical test in the behavioral assessment, we found that the training was not effective to increase participants’ leadership competencies. For the future development program, we suggested to modify the goals of the program toward the next stage of development.

Keywords: action learning, behavior, leadership development, Theory-U

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