Search results for: mobile game based learning

Authors: Amela Ajanovic, Reinhard Haas

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Electric vehicles (EVs) are considered as an important means to cope with current environmental problems in transport. However, their high capital costs and limited driving ranges state major barriers to a broader market penetration. The core objective of this paper is to investigate the future market prospects of various types of EVs from an economic and ecological point of view. Our method of approach is based on the calculation of total cost of ownership of EVs in comparison to conventional cars and a life-cycle approach to assess the environmental benignity. The most crucial parameters in this context are km driven per year, depreciation time of the car and interest rate. The analysis of future prospects it is based on technological learning regarding investment costs of batteries. The major results are the major disadvantages of battery electric vehicles (BEVs) are the high capital costs, mainly due to the battery, and a low driving range in comparison to conventional vehicles. These problems could be reduced with plug-in hybrids (PHEV) and range extenders (REXs). However, these technologies have lower CO₂ emissions in the whole energy supply chain than conventional vehicles, but unlike BEV they are not zero-emission vehicles at the point of use. The number of km driven has a higher impact on total mobility costs than the learning rate. Hence, the use of EVs as taxis and in car-sharing leads to the best economic performance. The most popular EVs are currently full hybrid EVs. They have only slightly higher costs and similar operating ranges as conventional vehicles. But since they are dependent on fossil fuels, they can only be seen as energy efficiency measure. However, they can serve as a bridging technology, as long as BEVs and fuel cell vehicle do not gain high popularity, and together with PHEVs and REX contribute to faster technological learning and reduction in battery costs. Regarding the promotion of EVs, the best results could be reached with a combination of monetary and non-monetary incentives, as in Norway for example. The major conclusion is that to harvest the full environmental benefits of EVs a very important aspect is the introduction of CO₂-based fuel taxes. This should ensure that the electricity for EVs is generated from renewable energy sources; otherwise, total CO₂ emissions are likely higher than those of conventional cars.

Keywords: costs, mobility, policy, sustainability,

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Authors: Hammache Nadir

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The scientific study of human learning and memory is now more than 125 years old. Psychologists have conducted thousands of experiments, correlational analyses, and field studies during this time, in addition to other research conducted by those from neighboring fields. A huge knowledge base has been carefully built up over the decades. Given this backdrop, we may ask ourselves: What great changes in education have resulted from this huge research base? How has the scientific study of learning and memory changed practices in education from those of, say, a century ago? Have we succeeded in building a translational educational science to rival medical science (in which biological knowledge is translated into medical practice) or types of engineering (in which, e.g., basic knowledge in chemistry is translated into products through chemical engineering)? The answer, I am afraid, is rather mixed. Psychologists and psychological research have influenced educational practice, but in fits and starts. After all, some of the great founders of American psychology—William James, Edward L. Thorndike, John Dewey, and others—are also revered as important figures in the history of education. And some psychological research and ideas have made their way into education—for instance, computer-based cognitive tutors for some specific topics have been developed in recent years—and in years past, such practices as teaching machines, programmed learning, and, in higher education, the Keller Plan were all important. These older practices have not been sustained. Was that because they failed or because of a lack of systematic research showing they were effective? At any rate, in 2012, we cannot point to a well-developed translational educational science in which research about learning and memory, thinking and reasoning, and related topics is moved from the lab into controlled field trials (like clinical trials in medicine) and the tested techniques, if they succeed, are introduced into broad educational practice. We are just not there yet, and one question that arises is how we could achieve a translational educational science.

Keywords: affective, education, cognition, pshychology

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Authors: Rodrigo Arnaldo Scarpel

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

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

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Authors: Bulatbayeva A. A., Maxutova I. O., Ergalieva A. N.

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This article presents a study of the practice of self-assessment of the quality of training cadets in a military higher specialized educational institution. The research was carried out by means of a questionnaire survey aimed at identifying the degree of satisfaction of cadets with the organization of the educational process, quality of teaching, the quality of the organization of independent work, and the system of their assessment. In general, the results of the study are of an intermediate nature. Proven tools will be incorporated into the planning and effective management of the learning process. The results of the study can be useful for the administrators and managers of the military education system for teachers of military higher educational institutions for adjusting the content and technologies of training future specialists. The publication was prepared as part of applied grant research for 2020-2022 by order of the Ministry of Education and Science of the Republic of Kazakhstan on the topic "Development of a comprehensive methodology for assessing the quality of education of graduates of military special educational institutions."

Keywords: teaching quality, quality satisfaction, learning management, quality management, process approach, classroom learning, interactive technologies, teaching quality

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Authors: Prabir Mukhopadhyay, Atul Kohale

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Going to school for Indian kids is a panic situation. Many of them are unable to adjust themselves to the confinement of the school building and this problem is compounded by other factors like unknown people in the vicinity, absence of either parents etc. This project aims at addressing these issues by exposing the kids at home to the learning environment. The purpose is to design a physical model with interfaces at each surface. The model would be like a cube with interactive surfaces where the child would be able to draw, paint, complete a picture and do such fun activities.

Keywords: interface, kids, play, computer systems engineering

Procedia PDF Downloads 196

Authors: Shuen-Tai Wang, Yu-Ching Lin, Hsi-Ya Chang

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With the development of virtualization technologies, a new type of service named cloud computing service is produced. Cloud users usually encounter the problem of how to use the virtualized platform easily over the web without requiring the plug-in or installation of special software. The object of this paper is to develop a system and a method enabling process interfacing within an automation scenario for accessing remote application by using the web browser. To meet this challenge, we have devised a web-based interface that system has allowed to shift the GUI application from the traditional local environment to the cloud platform, which is stored on the remote virtual machine. We designed the sketch of web interface following the cloud virtualization concept that sought to enable communication and collaboration among users. We describe the design requirements of remote application technology and present implementation details of the web application and its associated components. We conclude that this effort has the potential to provide an elastic and resilience environment for several application services. Users no longer have to burden the system maintenances and reduce the overall cost of software licenses and hardware. Moreover, this remote application service represents the next step to the mobile workplace, and it lets user to use the remote application virtually from anywhere.

Keywords: virtualization technology, virtualized platform, web interface, remote application

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Authors: Michael A. Sprayberry, Vincent C. Paquit

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Process parameter optimization in metal powder bed electron beam melting (MPBEBM) is crucial to ensure the technology's repeatability, control, and industry-continued adoption. Despite continued efforts to address the challenges via the traditional design of experiments and process mapping techniques, there needs to be more successful in an on-the-fly optimization framework that can be adapted to MPBEBM systems. Additionally, data-intensive physics-based modeling and simulation methods are difficult to support by a metal AM alloy or system due to cost restrictions. To mitigate the challenge of resource-intensive experiments and models, this paper introduces a Multi-Objective Reinforcement Learning (MORL) methodology defined as an optimization problem for MPBEBM. An off-policy MORL framework based on policy gradient is proposed to discover optimal sets of beam power (P) – beam velocity (v) combinations to maintain a steady-state melt pool depth and phase transformation. For this, an experimentally validated Eagar-Tsai melt pool model is used to simulate the MPBEBM environment, where the beam acts as the agent across the P – v space to maximize returns for the uncertain powder bed environment producing a melt pool and phase transformation closer to the optimum. The culmination of the training process yields a set of process parameters {power, speed, hatch spacing, layer depth, and preheat} where the state (P,v) with the highest returns corresponds to a refined process parameter mapping. The resultant objects and mapping of returns to the P-v space show convergence with experimental observations. The framework, therefore, provides a model-free multi-objective approach to discovery without the need for trial-and-error experiments.

Keywords: additive manufacturing, metal powder bed fusion, reinforcement learning, process parameter optimization

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Authors: Akash Amalan, Rui Wang, Yanqi Qiao, Emmanouil Panaousis, Kaitai Liang

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Federated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled General Adversarial Networks (GANs) to benefit from the rich distributed training data while preserving privacy. However, in a non-IID setting, current federated GAN architectures are unstable, struggling to learn the distinct features, and vulnerable to mode collapse. In this paper, we propose an architecture MULTI-FLGAN to solve the problem of low-quality images, mode collapse, and instability for non-IID datasets. Our results show that MULTI-FLGAN is four times as stable and performant (i.e., high inception score) on average over 20 clients compared to baseline FLGAN.

Keywords: federated learning, generative adversarial network, inference attack, non-IID data distribution

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Authors: Peng Li, Luan Wang, Haifeng Fei, Renhong Xie, Yibin Rui, Shanhong Guo

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The applications of satellite-based automatic identification system (S-AIS) pave the road for wide-range maritime traffic monitoring and management. But the coverage of satellite’s view includes multiple AIS self-organizing networks, which leads to the collision of AIS signals from different cells. The contribution of this work is to propose an improved multi-channel blind source separation algorithm based on Artificial Bee Colony (ABC) and advanced stochastic optimization to perform separation of the mixed AIS signals. The proposed approach adopts modified ABC algorithm to get an optimized initial separating matrix, which can expedite the initialization bias correction, and utilizes the Adaptive Moment Estimation (Adam) to update the separating matrix by adjusting the learning rate for each parameter dynamically. Simulation results show that the algorithm can speed up convergence and lead to better performance in separation accuracy.

Keywords: satellite-based automatic identification system, blind source separation, artificial bee colony, adaptive moment estimation

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Authors: Halima Musa Kamilu

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This paper discusses the effective strategies for teaching English language to learners in primary schools in Nigeria. English language development is the systematic use of instructional strategies designed to promote the acquisition of English by pupils in primary schools whose primary language is not English. Learning a second language is through total immersion. These strategies support this learning method, allowing pupils to have the knowledge of English language in a pattern similar to the way they learned their native language through regular interaction with others who already know the language. The focus is on fluency and learning to speak English in a social context with native speakers. The strategies allow for effective acquisition. The paper also looked into the following areas: visuals that reinforce spoken or written words, employ gestures for added emphasis, adjusting of speech, stressing of high-frequency vocabulary words, use of fewer idioms and clarifying the meaning of words or phrases in context, stressing of participatory learning and maintaining a low anxiety level and boosting of enthusiasm. It recommended that the teacher include vocabulary words that will make the content more comprehensible to the learner.

Keywords: effective, strategies, teaching, beginners and primary schools

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Authors: Velimir "monty" Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

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Authors: Fatemeh Miraki

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The use of authentic materials in English Language Teaching (ELT) has become increasingly prominent as educators recognize the value of exposing learners to real-world language use and cultural contexts. The integration of authentic materials in ELT aligns with the understanding that language learning is most effective when situated within authentic contexts (Richards & Rodgers, 2001). Tomlinson (1998) highlights the significance of authentic materials in ELT by research indicating that they offer learners exposure to genuine language use and cultural contexts. Tomlinson's work emphasizes the importance of creating meaningful learning experiences through the use of authentic materials. Research by Dörnyei (2001) underscores the potential of authentic materials to enhance students' intrinsic motivation through their relevance to real-life language use. The goal of this review paper is to explore the use of authentic materials in English Language Teaching (ELT) and its impact on language learning. It also discusses best practices for selecting and integrating such authentic materials into ELT curriculum, highlighting the benefits and challenges of using authentic materials to enhance student engagement, motivation, and language proficiency. Drawing on current research and practical examples, this paper provides insights into how teachers can effectively navigate the world of authentic materials to create dynamic and meaningful learning experiences for 21st century ELT learners. The findings of this study advocates for a shift towards embracing authentic materials within the ELT classroom, acknowledging their profound impact on language proficiency, intercultural competence, and learner engagement. It showed the transformative potential of authentic materials, educators can undergo a vibrant and immersive language learning experience, enriched with real-world application and cultural authenticity.

Keywords: authentic materials, ELT Classroom, ELT curriculum, students’ engagement

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Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

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Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

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Authors: Ioannis Andrianakis, Panagiotis Panagiotopoulos, Kyriakos Chatzidimitriou, Dimitrios Tampakis, Manolis Falelakis

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Live music, whether performed in organized venues, restaurants, hotels or any other spots, creates value chains that support and develop local economies and tourism development. In this paper, we describe PerformAnts, a platform that increases the mobility of musicians and their accessibility to remotely located venues by rationalizing the cost of live acts. By analyzing the event history and taking into account their potential availability, the platform provides bespoke recommendations to both bands and venues while also facilitating the organization of tours and helping rationalize transportation expenses by realizing an innovative mechanism called “chain booking”. Moreover, the platform provides an environment where complicated tasks such as technical and financial negotiations, concert promotion or copyrights are easily manipulated by users using best practices. The proposed solution provides important benefits to the whole spectrum of small/medium size concert organizers, as the complexity and the cost of the production are rationalized. The environment is also very beneficial for local talent, musicians that are very mobile, venues located away from large urban areas or in touristic destinations, and managers who will be in a position to coordinate a larger number of musicians without extra effort.

Keywords: machine learning, music industry, creative industries, web applications

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Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

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Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

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Authors: Bruk Gebregziabher

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The safe and efficient operation of Autonomous Mobile Robots (AMRs) in complex environments, such as manufacturing, logistics, and agriculture, necessitates accurate multiobject tracking and predictive collision avoidance. This paper presents algorithms and techniques for addressing these challenges using Lidar sensor data, emphasizing ensemble Kalman filter. The developed predictive collision avoidance algorithm employs the data provided by lidar sensors to track multiple objects and predict their velocities and future positions, enabling the AMR to navigate safely and effectively. A modification to the dynamic windowing approach is introduced to enhance the performance of the collision avoidance system. The overall system architecture encompasses object detection, multi-object tracking, and predictive collision avoidance control. The experimental results, obtained from both simulation and real-world data, demonstrate the effectiveness of the proposed methods in various scenarios, which lays the foundation for future research on global planners, other controllers, and the integration of additional sensors. This thesis contributes to the ongoing development of safe and efficient autonomous systems in complex and dynamic environments.

Keywords: autonomous mobile robots, multi-object tracking, predictive collision avoidance, ensemble Kalman filter, lidar sensors

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Authors: Cristina-Maria Păcurar

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The paper presents an original Python-based application that outlines the advantages of combining some elementary notions of mathematics with the study of a programming language. The application support refers to some of the first lessons of analytic geometry, meaning conics and quadrics and their reduction to a standard form, as well as some related notions. The chosen programming language is Python, not only for its closer to an everyday language syntax – and therefore, enhanced readability – but also for its highly reusable code, which is of utmost importance for a mathematician that is accustomed to exploit already known and used problems to solve new ones. The purpose of this paper is, on one hand, to support the idea that one of the most appropriate means to initiate one into programming is throughout mathematics, and reciprocal, one of the most facile and handy ways to assimilate some basic knowledge in the study of mathematics is to apply them in a personal project. On the other hand, besides being a mean of learning both programming and analytic geometry, the application subject to this paper is itself a useful tool for it can be seen as an independent original Python package for analytic geometry.

Keywords: analytic geometry, conics, python, quadrics

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Authors: Takahiro Nishigaki, Takashi Onoda

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In recent years, the number of document data has been increasing since the spread of the Internet. Many methods have been studied for extracting topics from large document data. We proposed Independent Topic Analysis (ITA) to extract topics independent of each other from large document data such as newspaper data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis. The topic represented by ITA is represented by a set of words. However, the set of words is quite different from the topics the user imagines. For example, the top five words with high independence of a topic are as follows. Topic1 = {"scor", "game", "lead", "quarter", "rebound"}. This Topic 1 is considered to represent the topic of "SPORTS". This topic name "SPORTS" has to be attached by the user. ITA cannot name topics. Therefore, in this research, we propose a method to obtain topics easy for people to understand by using the web search engine, topics given by the set of words given by independent topic analysis. In particular, we search a set of topical words, and the title of the homepage of the search result is taken as the topic name. And we also use the proposed method for some data and verify its effectiveness.

Keywords: independent topic analysis, topic extraction, topic naming, web search engine

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Authors: Arūnas Burinskas

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Although Europe is on the threshold of a new industrial revolution called Industry 4.0, many believe that this will increase the flexibility of production, the mass adaptation of products to consumers and the speed of their service; it will also improve product quality and dramatically increase productivity. However, as expected, all the benefits of Industry 4.0 face many of the inevitable changes and challenges they pose. One of them is the inevitable transformation of current competition and business models. This article examines the possible results of competitive conversion from the classic Bertrand and Cournot models to qualitatively new competition based on innovation. Ability to deliver a new product quickly and the possibility to produce the individual design (through flexible and quickly configurable factories) by reducing equipment failures and increasing process automation and control is highly important. This study shows that the ongoing transformation of the competition model is changing the game. This, together with the creation of complex value networks, means huge investments that make it particularly difficult for small and medium-sized enterprises. In addition, the ongoing digitalization of data raises new concerns regarding legal obligations, intellectual property, and security.

Keywords: Bertrand and Cournot Competition, competition model, industry 4.0, industrial organisation, monopolistic competition

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Authors: Mercedes Rodriguez Sanchez, Laura Luceño Casals

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With the rise of social media, creative people and brands everywhere are constantly generating content. The students with Bachelor's Degrees in Fashion Design use platforms such as Instagram or TikTok to look for inspiration and entertainment, as well as a way to develop their own ideas and share them with a wide audience. Information and Communications Technologies (ICT) have become a central aspect of higher education, virtually affecting every aspect of the student experience. Following the current trend, during the first semester of the second year, a collaborative project across two subjects –Design Management and History of Fashion Design– was implemented. After an introductory class focused on the relationship between fashion and cinema, as well as a brief history of 20th-century fashion, the students freely chose a work team and an iconic look from a movie costume. They researched the selected movie and its sociocultural context, analyzed the costume and the work of the designer, and studied the style, fashion magazines and most popular films of the time. Students then redesigned and recreated the costume, for which they were compelled to recycle the materials they had available at home as an unavoidable requirement of the activity. Once completed the garment, students delivered in-class, team-based presentations supported by the final design, a project summary poster and a making-of video, which served as a documentation tool of the costume design process. The methodologies used include Challenge-Based Learning (CBL), debates, Internet research, application of Information and Communications Technologies, and viewing clips of classic films, among others. After finishing the projects, students were asked to complete two electronic surveys to measure the acquisition of transversal and specific competencies of each subject. Results reveal that this activity helped the students' knowledge acquisition, a deeper understanding of both subjects and their skills development. The classroom dynamic changed. The multidisciplinary approach encouraged students to collaborate with their peers, while educators were better able to keep students' interest and promote an engaging learning process. As a result, the activity discussed in this paper confirmed the research hypothesis: it is positive to propose innovative teaching projects that combine academic research with playful learning environments.

Keywords: cinema, cooperative learning, fashion design, higher education, upcycling

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Authors: Selinda England, Ben Bodnaryk

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With an unprecedented increase in provincial immigration and government support for greater international and culturally diverse learners, a trade/applied learning-focused polytechnic with four campuses within one Canadian province saw the need for intercultural awareness and an intercultural teaching competence strategy for faculty training. An institution-wide pre-assessment needs survey was conducted in 2018, in which 87% of faculty professed to have some/no training when working with international and/or culturally diverse learners. After researching fellow Polytechnics in Canada and seeing very little in the way of faculty support for intercultural competence, an institutional project team comprised of members from all facets of the Polytechnic was created and included: Indigenous experts, Academic Chairs, Directors, Human Resource Managers, and international/settlement subject matter experts. The project team was organized to develop and implement a new academic model focused on enriching intercultural competence among faculty. Utilizing a competency based model, the project team incorporated inclusive terminology into competency indicators and devised a four-phase proposal for implementing intercultural teacher training: a series of workshops focused on the needs of international and culturally diverse learners, including teaching strategies based on current TESOL methodologies, literature and online resources for quick access when planning lessons, faculty assessment examples and models of interculturally proficient instructors, and future job descriptions - all which promote and encourage development of specific intercultural skills. Results from a post-assessment survey (to be conducted in Spring 2020) and caveats regarding improvements and next steps will be shared. The project team believes its intercultural and inclusive teaching competency-based model is one of the first, institution-wide faculty supported initiatives within the Canadian college and Polytechnic post-secondary educational environment; it aims to become a leader in both the province and nation regarding intercultural competency training for trades, industry, and business minded community colleges and applied learning institutions.

Keywords: cultural diversity and education, diversity training teacher training, teaching and learning, teacher training

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Authors: Shriya Shukla, Lachin Fernando

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Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

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Authors: Jana Beresova

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The paper presents a study carried out in 2015-2016 within the national scheme of research - VEGA 1/0106/15 based on theoretical research and empirical verification of the concept of intercultural communicative competence. It focuses on the current conception concerning target languages teaching compatible with the Common European Framework of Reference for Languages: Learning, teaching, assessment. Our research had revealed how the concept of intercultural communicative competence had been perceived by secondary-school teachers of English in Slovakia before they were intensively trained. Intensive workshops were based on the use of both authentic and instructional materials with the goal to support interculturally oriented language teaching aimed at challenging thinking. The former concept that supported the development of the students´ linguistic knowledge and the use of a target language to obtain information about the culture of the country whose language learners were learning was expanded by the meaning-making framework which views language as a typical means by which culture is mediated. The goal of the workshop was to influence English teachers to better understand the concept of intercultural communicative competence, combining theory and practice optimally. The results of the study will be presented and analysed, providing particular recommendations for language teachers and suggesting some changes in the National Educational Programme from which English learners should benefit in their future studies or professional careers.

Keywords: authentic materials, English language teaching, instructional materials, intercultural communicative competence

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Authors: Muhalim Muhalim

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Drawing on Lakoff’s theory of metaphor conceptualization, this article explores the conceptualization of language two writing (L2W) of ten students-teachers in Indonesia via metaphors. The ten postgraduate English language teaching students and at the same time (former) English teachers received seven days of intervention in teaching and learning L2. Using introspective log and focus group discussion, the results illuminate us that all participants are unanimous on perceiving L2W as process-oriented rather than product-oriented activity. Specifically, the metaphor conceptualizations exhibit three categories of process-oriented L2W: deliberate process, learning process, and problem-solving process. However, it has to be clarified from the outset that this categorization is not rigid because some of the properties of metaphors might belong to other categories. Results of the study and implications for English language teaching will be further discussed.

Keywords: metaphor conceptualisation, second language, learning writing, teaching writing

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Authors: Tomas Seidl

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'Enjoy the Czech traditions with all sences!' is the main communication headline of one of the major current marketing project representing the intangible cultural heritage of the Czech Republic to its visitors. The project CZ.1.06/4.1.00/12.08915 and CZ.1.06/4.1.00/12.08916 which is solved in the period 2013-2015 is co-financed form the EU financial sources from the Integrated Operational Programme. The primary goal of the project was to analyze the dislocation and potential of the intangible cultural heritage in the Czech Republic. Further goal was to prepare a useful regionalization. An as solution based on the outcomes the creative and media strategy was created and prepared. The processor – CzechTourism expect the following web and mobile application development and successful marketing campaign in 2015.

Keywords: traditions, intangible cultural heritage, Czech Republic, CzechTourism, digital performance

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Authors: Ailene B. Anonuevo

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The aim of the study was to evaluate the quality of life presently available for the IP students in selected schools in the Division of Panabo City. This further explores their future dreams and current status in classes and examines some implications relative to their studies. The study adopted the mixed methodology and used a survey research design as the operational framework for data gathering. Data were collected by self-administered questionnaires and interviews with sixty students from three schools in Panabo City. In addition, this study describes the learners’ background and school climate as variables that might influence their performance in school. The study revealed that an IP student needs extra attention due to their unfavorable learning environment. The study also found out that like any other students, IP learners yearns for a brighter future with the support of our government.

Keywords: IP learners, learning landscape, school climate, quality of life

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Authors: Prajwal Prakash Vasisht, Sharath Rajamurthy, Nishanth Dara

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In a supervised machine learning approach, metrics such as precision, accuracy, and coverage can be calculated using ground truth labels to help in model tuning, evaluation, and selection. In an unsupervised setting, however, where the data has no ground truth, there are few interpretable metrics that can guide us to do the same. Our approach creates a framework to adapt the Intersection over Union metric, referred to as Adapted IoU, usually used to evaluate supervised learning models, into the unsupervised domain, which solves the problem by factoring in subject matter expertise and intuition about the ideal output from the model. This metric essentially provides a scale that allows us to compare the performance across numerous unsupervised models or tune hyper-parameters and compare different versions of the same model.

Keywords: general metric, unsupervised learning, classification, intersection over union

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Authors: Cindy Davis

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Higher education is changing globally and there is increasing pressure from professional social work accreditation bodies for academic programs to demonstrate how students have successfully met mandatory graduate competencies. As professional accreditation organizations increase their demand for evidence of graduate competencies, strategies to document and recording learning outcomes becomes increasingly challenging for academics and students. Studies in higher education have found support for the pedagogical value of ePortfolios, a flexible personal learning space that is owned by the student and include opportunity for assessment, feedback and reflection as well as a virtual space to store evidence of demonstration of professional competencies and graduate attributes. Examples of institutional uses of ePortfolios include e-administration of a diverse student population, assessment of student learning, and the demonstration of graduate attributes attained and future student career preparation. The current paper presents a case study on the introduction of ePortfolios for social work graduates in Australia as part of an institutional approach to technology-enhanced learning and e-learning. Social work graduates were required to submit an ePortfolio hosted on PebblePad. The PebblePad platform was selected because it places the student at the center of their learning whilst providing powerful tools for staff to structure, guide and assess that learning. The ePortofolio included documentation and evidence of how the student met each graduate competency as set out by the social work accreditation body in Australia (AASW). This digital resource played a key role in the process of external professional accreditation by clearly documenting and evidencing how students met required graduate competencies. In addition, student feedback revealed a positive outcome on how this resource provided them with a consolidation of their learning experiences and assisted them in obtaining employment post-graduation. There were also significant institutional factors that were key to successful implementation such as investment in the digital technology, capacity building amongst academics, and technical support for staff and students.

Keywords: accreditation, social work, teaching, technology

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Authors: Theodorou Kyriaki, Ypsilantis George

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Vocabulary learning has attracted a lot of attention in recent years, contrary to the neglected part of the past. Along with the interest in finding successful vocabulary teaching strategies, many scholars focused on locating learning strategies used by language learners. As a result, more and more studies in the area of language pedagogy have been investigating the use of strategies in vocabulary learning by different types of learners. A common instrument in this field is the questionnaire, a tool of work that was enriched by questions involving current technology, and it was further implemented to a sample of 300 Greek students whose age varied from 9 and 17 years. Strategies located were grouped into the three categories of memory, cognitive, and compensatory type and associations between these dependent variables were investigated. In addition, relations between dependent and independent variables (such as age, sex, type of school, cultural background, and grade in English) were pursued to investigate the impact on strategy selection. Finally, results were compared to findings of other studies in the same field to contribute to a hypothesis of ethnic differences in strategy selection. Results initially discuss preferred strategies of all participants and further indicate that: a) technology affects strategy selection while b) differences between ethnic groups are not statistically significant. A number of successful strategies are presented, resulting from correlations of strategy selection and final school grade in English.

Keywords: acquisition of English, internet technology, research among Greek students, vocabulary learning strategies

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Authors: Chih-Yang Liao

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Baseball is usually played outdoors in the warmest months of the year. Therefore, baseball players are susceptible to the influence of the hot environment. It has been shown that hitting performance is increased in games played in warm weather, compared to in cold weather, in Major League Baseball. Intermittent cooling during sporting events can prevent the risk of hyperthermia and increase endurance performance. However, the effects of cooling during baseball games played in a hot environment are unclear. This study adopted a cross-over design. Ten Division I collegiate male baseball players in Taiwan volunteered to participate in this study. Each player played two simulated baseball games, with one day in between. Five of the players received intermittent cooling during the first simulated game, while the other five players received intermittent cooling during the second simulated game. The participants were covered in neck and forehand regions for 6 min with towels that were soaked in icy salt water 3 to 4 times during the games. The participants received the cooling treatment in the dugout when they were not on the field for defense or hitting. During the 2 simulated games, the temperature was 31.1-34.1°C and humidity was 58.2-61.8%, with no difference between the two games. Ratings of perceived exertion, thermal sensation, tympanic and forehead skin temperature immediately after each defensive half-inning and after cooling treatments were recorded. Ratings of perceived exertion were measured using the Borg 10-point scale. The thermal sensation was measured with a 6-point scale. The tympanic and skin temperature was measured with infrared thermometers. The data were analyzed with a two-way analysis of variance with repeated measurement. The results showed that intermitted cooling significantly reduced ratings of perceived exertion and thermal sensation. Forehead skin temperature was also significantly decreased after cooling treatments. However, the tympanic temperature was not significantly different between the two trials. In conclusion, intermittent cooling in the neck and forehead regions was effective in alleviating the perceived exertion and heat sensation. However, this cooling intervention did not affect the core temperature. Whether intermittent cooling has any impact on hitting or pitching performance in baseball players warrants further investigation.

Keywords: baseball, cooling, ratings of perceived exertion, thermal sensation

Procedia PDF Downloads 133