Search results for: conventional learning method

Authors: Mamani Acurio Alex, Aguilar Alonso Igor

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The lack of interest and the lack of motivation are related. The lack of both in school generates serious problems such as school dropout or a low level of learning. Augmented reality has been very useful in different areas, and in this research, it was implemented in the area of education. Information necessary for the correct development of this mobile application with augmented reality was searched using six different research repositories. It was concluded that the application must be immersive, attractive, and fun for students, and the necessary technologies for its construction were defined.

Keywords: augmented reality, Vuforia, school interest, learning

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Authors: Saule Shazhanbayeva, Denise van der Merwe

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Study hypothesis: Nazarbayev Intellectual School of Kyzylorda’s Biology teachers can use STEM-integrated learning to improve students' problem-solving ability and responsibility as global citizens. The significance of this study is to indicate how the use of STEM integrative learning during Biology lessons could contribute to forming globally-minded students who are responsible community members. For the purposes of this study, worldview is defined as a view that is broader than the country of Kazakhstan, allowing students to see the significance of their scientific contributions to the world as global citizens. The context of worldview specifically indicates that most students have never traveled outside of their city or region within Kazakhstan. In order to broaden student understanding, it is imperative that students are exposed to different world views and contrasting ideas within the educational setting of Biology as the science being used for the research. This exposure promulgates students understanding of the significance they have as global citizens alongside the obligations which would rest on them as scientifically minded global citizens. Integrative learning should be Biological Science - with Technology and engineering in the form of problem-solving, and Mathematics to allow improved problem-solving skills to develop within the students of Nazarbayev Intellectual School (NIS) of Kyzylorda. The school's vision is to allow students to realise their role as global citizens and become responsible community members. STEM allows integrations by combining four subject skills to solve topical problems designed by educators. The methods used are based on qualitative analysis: for students’ performance during a problem-solution scenario; and Biology teacher interviews to ascertain their understanding of STEM implementation and willingness to integrate it into current lessons. The research indicated that NIS is ready for a shift into STEM lessons to promote globally responsible students. The only additional need is for proper STEM integrative lesson method training for teachers.

Keywords: global citizen, STEM, Biology, high-school

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Authors: Anusha P. Wijesundara, Dulap I. Rathnayake, Nihal D. Perera

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Bathymetric information is fundamental importance to coastal and marine planning and management, nautical navigation, and scientific studies of marine environments. Satellite-derived bathymetry data provide detailed information in areas where conventional sounding data is lacking and conventional surveys are inaccessible. The two empirical approaches of log-linear bathymetric inversion model and non-linear bathymetric inversion model are applied for deriving bathymetry from high-resolution multispectral satellite imagery. This study compares these two approaches by means of geographical error analysis for the site Kankesanturai using WorldView-2 satellite imagery. Based on the Levenberg-Marquardt method calibrated the parameters of non-linear inversion model and the multiple-linear regression model was applied to calibrate the log-linear inversion model. In order to calibrate both models, Single Beam Echo Sounding (SBES) data in this study area were used as reference points. Residuals were calculated as the difference between the derived depth values and the validation echo sounder bathymetry data and the geographical distribution of model residuals was mapped. The spatial autocorrelation was calculated by comparing the performance of the bathymetric models and the results showing the geographic errors for both models. A spatial error model was constructed from the initial bathymetry estimates and the estimates of autocorrelation. This spatial error model is used to generate more reliable estimates of bathymetry by quantifying autocorrelation of model error and incorporating this into an improved regression model. Log-linear model (R²=0.846) performs better than the non- linear model (R²=0.692). Finally, the spatial error models improved bathymetric estimates derived from linear and non-linear models up to R²=0.854 and R²=0.704 respectively. The Root Mean Square Error (RMSE) was calculated for all reference points in various depth ranges. The magnitude of the prediction error increases with depth for both the log-linear and the non-linear inversion models. Overall RMSE for log-linear and the non-linear inversion models were ±1.532 m and ±2.089 m, respectively.

Keywords: log-linear model, multi spectral, residuals, spatial error model

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Authors: Aiydh Aljeddani, Fran Martin

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Sustainable human development is an essential component of a sustainable economic, social and environmental development. Addressing sustainable learning only through the addition of new teaching methods, or embedding certain approaches, is not sufficient on its own to support the goals of sustainable human development. This research project seeks to explore how the process of redesigning the current principles of curriculum based on the concept of sustainable learning could contribute to preparing a citizen who could later contribute towards sustainable human development. Multiple qualitative methodologies were employed in order to achieve the aim of this study. The main research methods were teachers’ field notes, artefacts, informal interviews (unstructured interview), a passive participant observation, a mini nominal group technique (NGT), a weekly diary, and weekly meeting. The study revealed that the integration of a curriculum for sustainable development, in addition to the use of innovative teaching approaches, highly valued by students and teachers in social studies’ sessions. This was due to the fact that it created a positive atmosphere for interaction and aroused both teachers and students’ interest. The content of the new curriculum also contributed to increasing students’ sense of shared responsibility through involving them in thinking about solutions for some global issues. This was carried out through addressing these issues through the concept of sustainable development and the theory of Thinking Activity in a Social Context (TASC). Students had interacted with sustainable development sessions intellectually and they also practically applied it through designing projects and cut-outs. Ongoing meetings and workshops to develop work between both the researcher and the teachers, and by the teachers themselves, played a vital role in implementing the new curriculum. The participation of teachers in the development of the project through working papers, exchanging experiences and introducing amendments to the students' environment was also critical in the process of implementing the new curriculum. Finally, the concept of sustainable learning can contribute to the learning outcomes much better than the current curriculum and it can better develop the learning objectives in educational institutions.

Keywords: redesigning, social studies and citizenship education curriculum, sustainable learning, thinking activity in a social context

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Authors: Yuh-Jen Wu, Chun-Min Lin

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Homophonic words are common in Mandarin Chinese which belongs to the tonal language family. Using homophonic cues to study foreign languages is one of the learning techniques of mnemonics that can aid the retention and retrieval of information in the human memory. When learning difficult foreign words, some learners transpose them with words in a language they are familiar with to build an association and strengthen working memory. These phonological clues are beneficial means for novice language learners. In the classroom, if mnemonic skills are used at the appropriate time in the instructional sequence, it may achieve their maximum effectiveness. For Chinese-speaking students, proper use of Chinese-English homophonic word pairs may help them learn difficult vocabulary. In this study, a database program is developed by employing Visual Basic. The database contains two corpora, one with Chinese lexical items and the other with English ones. The Chinese corpus contains 59,053 Chinese words that were collected by a web crawler. The pronunciations of this group of words are compared with words in an English corpus based on WordNet, a lexical database for the English language. Words in both databases with similar pronunciation chunks and batches are detected. A total of approximately 1,000 Chinese lexical items are located in the preliminary comparison. These homophonic word pairs can serve as a valuable tool to assist Chinese-speaking students in learning and memorizing new English vocabulary.

Keywords: Chinese, corpus, English, homophonic words, vocabulary

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Authors: Paulomi Polly Burey, Mark Lynch

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It has been shown that laboratory activities can help cement understanding of theoretical concepts, but it is difficult to deliver such an activity to an online cohort and issues such as occupational health and safety in the students’ learning environment need to be considered. Chemistry, in particular, is one of the sciences where practical experience is beneficial for learning, however typical university experiments may not be suitable for the learning environment of a distance learner. Food provides an ideal medium for demonstrating chemical concepts, and along with a few simple physical and virtual tools provided by educators, analytical chemistry can be experienced by distance learners. Food chemistry experiments were designed to be carried out in a home-based environment that 1) Had sufficient scientific rigour and skill-building to reinforce theoretical concepts; 2) Were safe for use at home by university students and 3) Had the potential to enhance student learning by linking simple hands-on laboratory activities with high-level virtual science. Two main components of the resources were developed, a home laboratory experiment component, and a virtual laboratory component. For the home laboratory component, students were provided with laboratory kits, as well as a list of supplementary inexpensive chemical items that they could purchase from hardware stores and supermarkets. The experiments used were typical proximate analyses of food, as well as experiments focused on techniques such as spectrophotometry and chromatography. Written instructions for each experiment coupled with video laboratory demonstrations were used to train students on appropriate laboratory technique. Data that students collected in their home laboratory environment was collated across the class through shared documents, so that the group could carry out statistical analysis and experience a full laboratory experience from their own home. For the virtual laboratory component, students were able to view a laboratory safety induction and advised on good characteristics of a home laboratory space prior to carrying out their experiments. Following on from this activity, students observed laboratory demonstrations of the experimental series they would carry out in their learning environment. Finally, students were embedded in a virtual laboratory environment to experience complex chemical analyses with equipment that would be too costly and sensitive to be housed in their learning environment. To investigate the impact of the intervention, students were surveyed before and after the laboratory series to evaluate engagement and satisfaction with the course. Students were also assessed on their understanding of theoretical chemical concepts before and after the laboratory series to determine the impact on their learning. At the end of the intervention, focus groups were run to determine which aspects helped and hindered learning. It was found that the physical experiments helped students to understand laboratory technique, as well as methodology interpretation, particularly if they had not been in such a laboratory environment before. The virtual learning environment aided learning as it could be utilized for longer than a typical physical laboratory class, thus allowing further time on understanding techniques.

Keywords: chemistry, food science, future pedagogy, STEM education

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Authors: Ya-Ting Carolyn Yang, Ping-Han Cheng, Shi-Hui Gilbert Chang, Terry Yuan-Fang Chen, Chih-Chieh Li

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Nanotechnology is widely applied in various areas so professionals in the related fields have to know more than nano knowledge. In the study, we focus on adopting ICT-assisted PBL in college general education to foster professionals who possess multiple abilities. The research adopted a pretest and posttest quasi-experimental design. The control group received traditional instruction, and the experimental group received ICT-assisted PBL instruction. Descriptive statistics will be used to describe the means, standard deviations, and adjusted means for the tests between the two groups. Next, analysis of covariance (ANCOVA) will be used to compare the final results of the two research groups after 6 weeks of instruction. Statistics gathered in the end of the research can be used to make contrasts. Therefore, we will see how different teaching strategies can improve students’ understanding about nanotechnology and learning skills.

Keywords: nanotechnology, science education, project-based learning, information and communication technology

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Authors: Archit Yajnik, Rustam Ali

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In this paper, numerical method based on discrete GHM multiwavelets is presented for solving the Fredholm integral equations of second kind. There is hardly any article available in the literature in which the integral equations are numerically solved using discrete GHM multiwavelet. A number of examples are demonstrated to justify the applicability of the method. In GHM multiwavelets, the values of scaling and wavelet functions are calculated only at t = 0, 0.5 and 1. The numerical solution obtained by the present approach is compared with the traditional Quadrature method. It is observed that the present approach is more accurate and computationally efficient as compared to quadrature method.

Keywords: GHM multiwavelet, fredholm integral equations, quadrature method, function approximation

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Authors: Rita Rani, Ramesh Kumar

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The intend of the current study is to date an old grand Peepal tree that is still alive. The tree is situated in Kalibard village, Sector 9, Chandigarh (India). Due to its huge structure, it has got the status of ‘Heritage tree.’ Optically Stimulated Luminescence of sediments beneath the roots is used to determine the age of the tree. Optical dating is preferred over conventional dating methods due to more precession. The methodology includes OSL of quartz grain using SAR protocol for accumulated dose measurement. The age determination of an alive tree using sedimentary quartz is in close agreement with the approximated age provided by the related agency. This is the first attempt at using optically stimulated luminescence in the age determination of alive trees in this region. The study concludes that the Luminescence dating of alive trees is the nondestructive and more precise method.

Keywords: luminescence, dose rate, optical dating, sediments

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Authors: Karl Eddie T. Malabanan

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Due to the quick transmission and public health risk of coronavirus disease, schools and universities have shifted to distant learning. Teachers everywhere were forced to shift gears instantly in order to react to the needs of students and families using synchronous and asynchronous virtual teaching. This study aims to explore the lived experiences of physical education teachers who are currently experiencing remote learning in teaching during the time of the COVID-19 pandemic. Specifically, the challenges that the physical education teachers encounter during remote learning and teaching. The participants include 12 physical education teachers who have taught in higher education institutions for at least five years. The researcher utilized qualitative research; specifically, the researcher used Consensual Qualitative Research (CQR). The results of this study showed that there are five categories for the Lived Experiences of Physical Education Teachers with thirty-one subcategories. This study revealed that physical education teachers experienced very challenging situations during the time of the pandemic. It also found that students had challenges in the abrupt transition from traditional to virtual learning classes, but it also showed that students are tenacious and willing to face any adversity. The researcher also finds that teachers are mentally drained during this time. Furthermore, one of the main focuses for the teachers should be on improving their well-being. And lastly, to cope with the challenges, teachers employ socializing to relieve tension and anxiety.

Keywords: lived experiences, consensual qualitative research, pandemic, education

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Authors: Prodromos E. Atlamazoglou

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The method of moments combined with the method of symmetrical components is used for the analysis of interstitial hyperthermia applicators. The basis and testing functions are both piecewise sinusoids, qualifying our technique as a Galerkin one. The dielectric coatings are modeled by equivalent volume polarization currents, which are simply related to the conduction current distribution, avoiding in that way the introduction of additional unknowns or numerical integrations. The results of our method for a four dipole circular array, are in agreement with those already published in literature for a same hyperthermia configuration. Apart from being accurate, our approach is more general, more computationally efficient and takes into account the coupling between the antennas.

Keywords: hyperthermia, integral equations, insulated antennas, method of symmetrical components

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Authors: He Lingsong, Wang Junfeng, Tan Qiong, Xu Jiang

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The paper describes efforts on reconstruction methods of engineering testing and measurement technology course by applying new techniques and applications. Firstly, flipped classroom was introduced. In-class time was used for in-depth discussions and interactions while theory concept teaching was done by self-study course outside of class. Secondly, two hands-on practices of technique applications, including the program design of MATLAB Signal Analysis and the measurement application of Arduino sensor, have been covered in class. Class was transformed from an instructor-centered teaching process into an active student-centered learning process, consisting of the pre-class massive open online course (MOOC), in-class discussion and after-class practice. The third is to change sole written homework to the research-oriented application practice assignments, so as to enhance the breadth and depth of the course.

Keywords: testing and measurement, flipped classroom, MOOC, research-oriented learning, practice-oriented learning

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Authors: Edgardo V. Gubatanga Jr., Mark Joshua Salvacion

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Most aerial LiDAR systems have accompanying aerial cameras in order to capture not only the terrain of the surveyed area but also its true-color appearance. However, the presence of atmospheric clouds, poor lighting conditions, and aerial camera problems during an aerial survey may cause absence of aerial photographs. These leave areas having terrain information but lacking aerial photographs. Intensity images can be derived from LiDAR data but they are only grayscale images. A deep learning model is developed to create a complex function in a form of a deep neural network relating the pixel values of LiDAR-derived intensity images and true-color images. This complex function can then be used to predict the true-color images of a certain area using intensity images from LiDAR data. The predicted true-color images do not necessarily need to be accurate compared to the real world. They are only intended to look realistic so that they can be used as base maps.

Keywords: aerial LiDAR, colorization, deep learning, intensity images

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Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

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Authors: Ann Devitt, Mairead Brady, Markus Lamest, Stephen Gomez

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This study set out to explore the use of an online collaborative serious game for student learning in a postgraduate introductory marketing module. The simulation game aimed to bridge the theory-practice divide in marketing by allowing students to apply theory in a safe, simulated marketplace. This study addresses the following research questions: Does an online marketing simulation game engage students higher order cognitive skills? Does collaborative activity required develop students’ “soft” skills, such as communication and negotiation? What specific affordances of the online simulation promote learning? This qualitative case study took place in 2014 with 40 postgraduate students on a Business Masters Programme. The two-week intensive module combined lectures with collaborative activity on a marketing simulation game, MMX from Pearsons. The game requires student teams to compete against other teams in a marketplace and design a marketing plan to maximize key performance indicators. The data for this study comprise essays written by students after the module reflecting on their learning on the module. A thematic analysis was conducted of the essays using the following a priori theme sets: 6 levels of the cognitive domain of Blooms taxonomy; 5 principles of Cooperative Learning; affordances of simulation environments including experiential learning; motivation and engagement; goal orientation. Preliminary findings would strongly suggest that the game facilitated students identifying the value of theory in practice, in particular for future employment; enhanced their understanding of group dynamics and their role within that; and impacted very strongly, both positively and negatively on motivation. In particular the game mechanics of MMX, which hinges on the correct identification of a target consumer group, was identified as a key determinant of extrinsic and intrinsic motivation for learners. The findings also suggest that the situation of the simulation game within a broader module which required post-game reflection was valuable in identifying key learning of marketing concepts in both the positive and the negative experiences of the game.

Keywords: simulation, marketing, serious game, cooperative learning, bloom's taxonomy

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Authors: S. Biondi, V. Catania, R. Di Natale, A. R. Intilisano, D. Panno

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One of the major goals of Spoken Dialog Systems (SDS) is to understand what the user utters. In the SDS domain, the Spoken Language Understanding (SLU) Module classifies user utterances by means of a pre-definite conceptual knowledge. The SLU module is able to recognize only the meaning previously included in its knowledge base. Due the vastity of that knowledge, the information storing is a very expensive process. Updating and managing the knowledge base are time-consuming and error-prone processes because of the rapidly growing number of entities like proper nouns and domain-specific nouns. This paper proposes a solution to the problem of Name Entity Recognition (NER) applied to a SDS domain. The proposed solution attempts to automatically recognize the meaning associated with an utterance by using the PANKOW (Pattern based Annotation through Knowledge On the Web) method at runtime. The method being proposed extracts information from the Web to increase the SLU knowledge module and reduces the development effort. In particular, the Google Search Engine is used to extract information from the Facebook social network.

Keywords: spoken dialog system, spoken language understanding, web semantic, name entity recognition

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: Yan Cheng, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, Qing Zeng-Treitler

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Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning.

Keywords: weakly-supervised learning, support vector machine, prediction, delirium, simulation

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Authors: B. Sellami, Y. Laskri, M. Belloufi

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Conjugate gradient methods are an important class of methods for unconstrained optimization, especially for large-scale problems. Recently, they have been much studied. In this paper, a new family of conjugate gradient method is proposed for unconstrained optimization. This method includes the already existing two practical nonlinear conjugate gradient methods, which produces a descent search direction at every iteration and converges globally provided that the line search satisfies the Wolfe conditions. The numerical experiments are done to test the efficiency of the new method, which implies the new method is promising. In addition the methods related to this family are uniformly discussed.

Keywords: conjugate gradient method, global convergence, line search, unconstrained optimization

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Authors: Henriette van Rensburg, Betty Adcock

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Concern about the lack of knowledge of quality teaching and teacher retention in rural and remote areas of Australia, has caused academics to improve pre-service teachers’ understanding of this problem. The participants in this study were forty students enrolled in an undergraduate educational course (EDO3341 Teaching in rural and remote communities) at the University of Southern Queensland in Toowoomba in 2012. This study involved an innovative case-based teaching approach in order to broaden their generally under-informed understanding of teaching in a rural and remote area. Three themes have been identified through analysing students’ critical reflections: learning expertise, case-based learning support and authentic learning. The outcomes identified the changes in pre-service teachers’ understanding after they have deepened their knowledge of the realities of teaching in rural and remote areas.

Keywords: rural and remote education, case based teaching, innovative education approach, higher education

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Authors: Chaithanya Pothuganti

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Open and semi-open spaces in educational buildings like corridors, mid landings, seating spaces, lobby, courtyards are traditionally have been the places of social communion and interaction which helps in promoting the knowledge, performance, activeness, and motivation in students. Factors like availability of land, commercialization, of educational facilities, especially in e-techno and smart schools, led to closed classrooms to accommodate students thereby lack quality open and semi-open spaces. This insufficient attention towards open space design which is a means of informal learning misses an opportunity to encourage the student’s skill development, behavior and learning skills. The core objective of this paper is to find the level of impact on student learning behavior and to identify the suitable proportions and configuration of spaces that shape the schools. In order to achieve this, different types of open spaces in schools and their impact on student’s performance in various existing models are analysed using case studies to draw some design principles. The study is limited to indoor open spaces like corridors, break out spaces and courtyards. The expected outcome of the paper is to suggest better design considerations for the development of semi-open and open spaces which functions as an element for informal learnings. Its focus is to provide further thinking on designing and development of open spaces in educational buildings.

Keywords: configuration of spaces and proportions, informal learning, open spaces, schools, student’s behavior

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Authors: M. Rodionov, Z. Dedovets

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This article discusses ways to implement a differentiated approach to developing academic motivation for mathematical studies which relies on defining the primary structural characteristics of motivation. The following characteristics are considered: features of realization of cognitive activity, meaning-making characteristics, level of generalization and consistency of knowledge acquired by personal experience. The assessment of the present level of individual student understanding of each component of academic motivation is the basis for defining the relevant educational strategy for its further development.

Keywords: learning activity, mathematics, motivation, student

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Authors: Salah Algabli

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As online learning takes center stage; Arabic language instructors face the imperative to adapt their practices for the digital realm. This study investigates the experiences of online Arabic instructors to unveil the pedagogical opportunities and challenges this format presents. Utilizing a transcendental phenomenological approach with 15 diverse participants, the research shines a light on the unique realities of online language teaching at the university level, specifically in the United States. The study proposes theoretical and practical solutions to maximize the benefits of online language learning while mitigating its challenges. Recommendations cater to instructors, researchers, and program coordinators, paving the way for enhancing the quality of online Arabic language education. The findings highlight the need for pedagogical approaches tailored to the online environment, ultimately shaping a future where both instructors and learners thrive in this digital landscape.

Keywords: online Arabic language learning, pedagogical opportunities and challenges, online Arabic teachers, online language instruction, digital pedagogy

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Authors: Shaila Rana, Wasim Alhamdani

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Effective cybersecurity learning relies on an engaging, interactive, and entertaining activity that fosters positive learning outcomes. VR cybersecurity training may promote these aforementioned variables. However, a methodological approach and framework have not yet been created to allow trainers and educators to employ VR cybersecurity training methods to promote positive learning outcomes to the author’s best knowledge. Thus, this paper aims to create an approach that cybersecurity trainers can follow to create a VR cybersecurity training module. This methodology utilizes concepts from other cybersecurity training frameworks, such as NICE and CyTrONE. Other cybersecurity training frameworks do not incorporate the use of VR. VR training proposes unique challenges that cannot be addressed in current cybersecurity training frameworks. Subsequently, this ontology utilizes concepts unique to developing VR training to create a relevant methodology for creating VR cybersecurity training modules. The outcome of this research is to create a methodology that is relevant and useful for designing VR cybersecurity training modules.

Keywords: virtual reality cybersecurity training, VR cybersecurity training, traditional cybersecurity training, ontology

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Authors: Kishore Kumar R., Kaustav Sengupta, Shalini Sood Sehgal, Poornima Santhanam

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Fashion is a human expression that is constantly changing. One of the prime factors that consistently influences fashion is the change in colour preferences. The role of colour in our everyday lives is very significant. It subconsciously explains a lot about one’s mindset and mood. Analyzing the colours by extracting them from the outfit images is a critical study to examine the individual’s/consumer behaviour. Several research works have been carried out on extracting colours from images, but to the best of our knowledge, there were no studies that extract colours to specific apparel and identify colour patterns geographically. This paper proposes a framework for accurately extracting colours from T-shirt images and predicting dominant colours geographically. The proposed method consists of two stages: first, a U-Net deep learning model is adopted to segment the T-shirts from the images. Second, the colours are extracted only from the T-shirt segments. The proposed method employs the iMaterialist (Fashion) 2019 dataset for the semantic segmentation task. The proposed framework also includes a mechanism for gathering data and analyzing India’s general colour preferences. From this research, it was observed that black and grey are the dominant colour in different regions of India. The proposed method can be adapted to study fashion’s evolving colour preferences.

Keywords: colour analysis in t-shirts, convolutional neural network, encoder-decoder, k-means clustering, semantic segmentation, U-Net model

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Authors: J. Bartnicka, J. A. Piedrabuena, R. Portilla, L. Moyano - Cuevas, J. B. Pagador, P. Augat, J. Tokarczyk, F. M. Sánchez Margallo

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Orthopaedic surgeries are characterized by a high degree of complexity. This is reflected by four main groups of resources: 1) surgical team which is consisted of people with different competencies, educational backgrounds and positions; 2) information and knowledge about medical and technical aspects of surgery; 3) medical equipment including surgical tools and materials; 4) space infrastructure which is important from an operating room layout point of view. These all components must be integrated and build a homogeneous organism for achieving an efficient and ergonomically correct surgical workflow. Taking this as a background, there was formulated a concept of international project, called “Online Vocational Training course on ergonomics for orthopaedic Minimally Invasive” (Train4OrthoMIS), which aim is to develop an e-learning tool available in 4 languages (English, Spanish, Polish and German). In the article, there is presented the first project research outcomes focused on three aspects: 1) ergonomic needs of surgeons who work in hospitals around different European countries, 2) the concept of structure of e-learning course, 3) the definition of tools and methods for knowledge assessment adjusted to users’ expectation. The methodology was based on the expert panels and two types of surveys: 1) on training needs, 2) on evaluation and self-assessment preferences. The major findings of the study allowed describing the subjects of four training modules and learning sessions. According to peoples’ opinion there were defined most expected test methods which are single choice test and right after quizzes: “True or False” and “Link elements”. The first project outcomes confirmed the necessity of creating a universal training tool for orthopaedic surgeons regardless of the country in which they work. Because of limited time that surgeons have, the e-learning course should be strictly adjusted to their expectation in order to be useful.

Keywords: international e-learning, ergonomics, orthopaedic surgery, Train4OrthoMIS

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Authors: Cristian Crespo

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Conditional cash transfers (CCTs) have been targeted towards the poor. Thus, their targeting assessments check whether these schemes have been allocated to low-income households or individuals. However, CCTs have more than one goal and target group. An additional goal of CCTs is to increase school enrolment. Hence, students at risk of dropping out of school also are a target group. This paper analyses whether one of the most common targeting mechanisms of CCTs, a proxy means test (PMT), is suitable to identify the poor and future school dropouts. The PMT is compared with alternative approaches that use the outputs of a predictive model of school dropout. This model was built using machine learning algorithms and rich administrative datasets from Chile. The paper shows that using machine learning outputs in conjunction with the PMT increases targeting effectiveness by identifying more students who are either poor or future dropouts. This joint targeting approach increases effectiveness in different scenarios except when the social valuation of the two target groups largely differs. In these cases, the most likely optimal approach is to solely adopt the targeting mechanism designed to find the highly valued group.

Keywords: conditional cash transfers, machine learning, poverty, proxy means tests, school dropout prediction, targeting

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Authors: W. Wen, G. Jackson, S. Maskill, D. G. McCartney, W. Sun

Abstract:

CoNiCrAlY alloys have been widely used as bond coats for thermal barrier coating (TBC) systems because of low cost, improved control of composition, and the feasibility to tailor the coatings microstructures. Coatings are in general very thin structures, and therefore it is impossible to characterize the mechanical responses of the materials via conventional mechanical testing methods. Due to this reason, miniature specimen testing methods, such as the small punch test technique, have been developed. This paper presents some of the recent research in evaluating the mechanical properties of the CoNiCrAlY coatings at room and high temperatures, through the use of small punch testing and the developed miniature specimen tensile testing, applicable to a range of temperature, to investigate the elastic-plastic and creep behavior as well as ductile-brittle transition temperature (DBTT) behavior. An inverse procedure was developed to derive the mechanical properties from such tests for the coating materials. A two-layer specimen test method is also described. The key findings include: 1) the temperature-dependent coating properties can be accurately determined by the miniature tensile testing within a wide range of temperature; 2) consistent DBTTs can be identified by both the SPT and miniature tensile tests (~ 650 °C); and 3) the FE SPT modelling has shown good capability of simulating the early local cracking. In general, the temperature-dependent material behaviors of the CoNiCrAlY coating has been effectively characterized using miniature specimen testing and inverse method.

Keywords: NiCoCrAlY coatings, mechanical properties, DBTT, miniature specimen testing

Procedia PDF Downloads 148

Authors: Manthan Patel, Vinit Ahlawat, Anshh Singh Claire, Pijush Samui

Abstract:

This paper focuses on the analysis of quicksand condition for a dam foundation. The quicksand condition occurs in cohesion less soil when effective stress of soil becomes zero. In a dam, the saturated sediment may appear quite solid until a sudden change in pressure or shock initiates liquefaction. This causes the sand to form a suspension and lose strength hence resulting in failure of dam. A soil profile shows different properties at different points and the values obtained are uncertain thus reliability analysis is performed. The reliability is defined as probability of safety of a system in a given environment and loading condition and it is assessed as Reliability Index. The reliability analysis of dams under quicksand condition is carried by Gaussian Process Regression (GPR). Reliability index and factor of safety relating to liquefaction of soil is analysed using GPR. The results of reliability analysis by GPR is compared to that of conventional method and it is demonstrated that on applying GPR the probabilistic analysis reduces the computational time and efforts.

Keywords: factor of safety, GPR, reliability index, quicksand

Procedia PDF Downloads 468

Authors: Yangzhi Li

Abstract:

Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

Procedia PDF Downloads 72