Search results for: English language learning experiences

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: Melanie Adrian, Elspeth McCulloch, Emily-Jean Gallant

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Resources -financial, physical, and human- are increasingly constrained in higher education. University classes are getting bigger, and the concomitant grading burden on faculty is growing rapidly. Multiple-choice exams are seen by some as one solution to these changes. How much students retain, however, and what their testing experience is, continues to be debated. Are multiple-choice exams serving students well, or are they bearing the burden of these developments? Is there a way to address both the resource constraints and make these types of exams more meaningful? In short, how do we engender evaluation methods for large-scale classes that provide opportunities for heightened student learning and enrichment? The following article lays out a testing approach we have employed in four iterations of the same third-year law class. We base our comments in this paper on our initial observations as well as data gathered from an ethics-approved study looking at student experiences. This testing approach provides students with multiple opportunities for revision (thus increasing chances for long term retention), is both individually and collaboratively driven (thus reflecting the individual effort and group effort) and is automatically graded (thus draining limited institutional resources). We found that overall students appreciated the approach and found it more ‘humane’, that it notably reduced pre-exam and intra-exam stress levels, increased ease, and lowered nervousness.

Keywords: exam, higher education, multiple-choice, law

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Authors: Sina Saadati, Mohammadreza Razzazi

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Since Java is an object-oriented language, It can be used to solve a wide range of problems. One of the considerable usages of this language can be found in Agent-based modeling and simulation. Despite the significant power of Java, There is not an easy method to render a 3-dimensional human model. In this article, we are about to develop a library which helps modelers present a 3D human model and control it with Java. The library runs two server programs. The first one is a web page server that can connect to any browser and present an HTML code. The second server connects to the browser and controls the movement of the model. So, the modeler will be able to develop a simulation and display a good-looking human model without any knowledge of any graphical tools.

Keywords: agent-based modeling and simulation, human model, graphics, Java, distributed systems

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Authors: Vanessa Matias Leite, Jandira Guenka Palma, Flávio Henrique de Oliveira

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This paper presents a model-oriented development approach to software development in the Model-View-Controller (MVC) architectural standard. This approach aims to expose a process of extractions of information from the models, in which through rules and syntax defined in this work, assists in the design of the initial model and its future conversions. The proposed paper presents a syntax based on the natural language, according to the rules agreed in the classic grammar of the Portuguese language, added to the rules of conversions generating models that follow the norms of the Object Management Group (OMG) and the Meta-Object Facility MOF.

Keywords: BNF Syntax, model driven architecture, model-view-controller, transformation, UML

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Authors: Laura Brennan, Ram Balakumar

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ChatGPT has been developed by Open AI (Nov 2022) as a powerful artificial intelligence (AI) language model which has been designed to produce human-like text from user written prompts. To gain the most from the system, user written prompts must give context specific information. This article aims to give guidance on how to optimise the ChatGPT system in the context of education for otolaryngology. Otolaryngology is a specialist field which sees little time dedicated to providing education to both medical students and doctors. Additionally, otolaryngology trainees have seen a reduction in learning opportunities since the COVID-19 pandemic. In this article we look at these various barriers to medical education in Otolaryngology training and suggest ways that ChatGPT can overcome them and assist in simulation-based training. Examples provide how this can be achieved using the Authors’ experience to further highlight the practicalities. What this article has found is that while ChatGPT cannot replace traditional mentorship and practical surgical experience, it can serve as an invaluable supplementary resource to simulation based medical education in Otolaryngology.

Keywords: artificial intelligence, otolaryngology, surgical training, medical education

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Authors: Ming-Jen Huang, Chun-Fang Huang, Chiching Wei

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With the recent advance of the deep neural network, we observe new applications of NLP (natural language processing) and CV (computer vision) powered by deep neural networks for processing business documents. However, creating a real-world document processing system needs to integrate several NLP and CV tasks, rather than treating them separately. There is a need to have a unified approach for processing documents containing textual and graphical elements with rich formats, diverse layout arrangement, and distinct semantics. In this paper, a framework that fulfills this unified approach is presented. The framework includes a representation model definition for holding the information generated by various tasks and specifications defining the coordination between these tasks. The framework is a blueprint for building a system that can process documents with rich formats, styles, and multiple types of elements. The flexible and lightweight design of the framework can help build a system for diverse business scenarios, such as contract monitoring and reviewing.

Keywords: document processing, framework, formal definition, machine learning

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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: B. K. Manjunatha Goud, Aruna Chanu Oinam

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The undergraduate medical curriculum is oriented towards training the students to undertake the responsibilities of a physician. During the training period, adequate emphasis is placed on inculcating logical and scientific habits of thought; clarity of expression and independence of judgment; and ability to collect and analyze information and to correlate them. At Ras Al Khaimah Medical and Health Sciences University (RAKMHSU), Biochemistry a basic medical science subject is taught in the 1st year of 5 years medical course with vertical interdisciplinary interaction with all subjects, which needs to be taught and learned adequately by the students to be related to clinical case or clinical problem in medicine and future diagnostics so that they can practice confidently and skillfully in the community. Based on these facts study was done to know the extent of usage of library resources by the students and the impact of study materials on their preparation for examination. It was a comparative cross sectional study included 100 and 80 1st and 2nd-year students who had successfully completed Biochemistry course. The purpose of the study was explained to all students [participants]. Information was collected on a pre-designed, pre-tested and self-administered questionnaire. The questionnaire was validated by the senior faculties and pre tested on students who were not involved in the study. The study results showed that 80.30% and 93.15% of 1st and 2nd year students have the clear idea of course outline given in course handout or study guide. We also found a statistically significant number of students agreed that they were benefited from the practical session and writing notes in the class hour. A high percentage of students [50% and 62.02%] disagreed that that reading only the handouts is enough for their examination as compared to other students. The study also showed that only 35% and 41% of students visited the library on daily basis for the learning process, around 65% of students were using lecture notes and text books as a tool for learning and to understand the subject and 45% and 53% of students used the library resources (recommended text books) compared to online sources before the examinations. The results presented here show that students perceived that e-learning resources like power point presentations along with text book reading using SQ4R technique had made a positive impact on various aspects of their learning in Biochemistry. The use of library by students has overall positive impact on learning process especially in medical field enhances the outcome, and medical students are better equipped to treat the patient. But it’s also true that use of library use has been in decline which will impact the knowledge aspects and outcome. In conclusion, a student has to be taught how to use the library as learning tool apart from lecture handouts.

Keywords: medical education, learning resources, study guide, biochemistry

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Authors: Ya-Hsuan Wang

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This project of intercultural education aimed to explore pluricultural people’s interpretation and evaluation of the transnational community in Taiwan. Based on transnationalism and transculturalism, this study concerns the human right issues for immigrants and pluricultural people. Research participants as immigrants in Taiwan were asked about their typical thinking styles in the transnational community, their cultural integration in terms of transnational behaviors, and their collective memory of the transnational community. Interview questions included what key factors were involved in their identity negotiation, what roles the transnational community and collective memory would be for their identity negotiation and what were the positive or negative aspects impacting cross-border identity. Based on the experiences of pluricultural people and transnational communities, this project expected to enhance the depth and width of developing transcultural knowledge in textbook reform on History in K-12 schools. It is to transform cross-border identity into knowledge embedded with local culture in response to globalization and localization. The purpose of this paper is to portrait the cross-cultural practice of transnational community for Taiwan’s immigrants. It is to report their external socio-cultural expectation of ethnic economics, to understand their internal life course of national identity, and to clarify transnational community in relation to their cross-border identity. In conclusion, the cross-cultural practice of transnational community combined the external contexts such as ethnic economic interaction among transnational communities, social report and ethnic industry, and the internal contexts such as ethnic identity, language use, and collective memory in ethnic history.

Keywords: cross-cultural practice, immigrants, pluricultural people, transnational community

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Authors: Ebtisam Alqahtani

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The purpose of this study is to investigate academic staff perspectives on using Augmented Reality in teaching practice to support students learning remotely during the COVID pandemic. the study adopted the DTPB theoretical model to guide the identification of key potential factors that could motivate academic staff to use or not use AR in teaching practices. A mixing method design was adopted for a better understanding of the study problem. A survey was completed by 851 academic staff, and this was followed by interviews with 20 academic staff. Statistical analyses were used to assess the survey data, and thematic analysis was used to assess the interview data. The study finding indicates that 75% of academic staff were aware of AR as a pedagogical tool, and they agreed on the potential benefits of AR in teaching and learning practices. However, 36% of academic staff use it in teaching and learning practice, and most of them agree with most of the potential barriers to adopting AR in educational environments. In addition, the study results indicate that 91% of them are planning to use it in the future. The most important factors that motivated them to use it in the future are the COVID pandemic factor, hedonic motivation factor, and academic staff attitude factor. The perceptions of academic staff differed according to the universities they attended, the faculties they worked in, and their gender. This study offers further empirical support for the DTPB model, as well as recommendations to help higher education implement technology in its educational environment based on the findings of the study. It is unprecedented the study the necessity of the use of AR technologies in the time of Covid-19. Therefore, the contribution is both theoretical and practice

Keywords: higher education, academic staff, AR technology as pedological tools, teaching and learning practice, benefits of AR, barriers of adopting AR, and motivating factors to adopt AR

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Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

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Authors: Omran Alharbi, Victor Lally

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The advantages of the adoption of new technology such as learning management systems (LMSs) in education and teaching methods have been widely recognised. This has led a large number of universities to integrate this type of technology into their daily learning and teaching activities in order to facilitate the education process for both learners and teachers. On the other hand, in some developing countries such as Saudi Arabia, educators have seldom used this technology. As a result, this study was conducted in order to investigate the factors that impede teachers’ use of technology (LMSs) in their teaching in Saudi Arabian institutions. This study used a qualitative approach. Eight participants were invited to take part in this study, and they were asked to give their opinions about the most significant factors that prevented them from integrating technology into their daily activities. The results revealed that a lack of LMS skills, interest in and knowledge about the LMS among teachers were the most significant factors impeding them from using technology in their lessons. The participants suggested that incentive training should be provided to reduce these challenges.

Keywords: LMS, factors, KSA, teachers

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Authors: Arazan Jane V., Badiang, Ronesito Jr. R., Clavesillas Cristine Joy H., Belleza Saramie S.

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This study examined the coaching style among the PE Teachers in terms of Autonomy, Supportive style, and Controlling Style. On the other hand, gives opportunities to an athlete to be independent, task-oriented, and acknowledge their feelings and perspective of each individual. A controlling coaching style is also portrayed by the rises and falls over an athlete's training development; when this variance is identified, it might harm training. The selection of the respondents of the study will use a random sample of High School PE teachers of the Division of Davao del Norte with a total of 78 High School PE teachers, which can be broken down into 70 High School PE Teachers for Quantitative data for the survey questionnaire and 8 PE Teachers for Qualitative data (IDI). In the quantitative phase, a set of survey questionnaires will be used to gather data from the participants—the extent of the Implementation Questionnaire. The tool will be a researcher-made questionnaire based on the Coaching Styles of selected High School PE teachers of Davao Del Norte. In the qualitative phase, an interview guide questionnaire will be used. Focus group discussions will be conducted to determine themes and patterns or participants' experiences and insights. The researchers conclude that the degree of coaching style among PE Teachers from the Division of Davao del Norte is high, as seen by the findings of this study, and that coaching style among these teachers is highly noticeable.

Keywords: supportive autonomy style, controlling style, live experiences, exemplified

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

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Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.

Keywords: human motion recognition, motion representation, Laban Movement Analysis, Discrete Hidden Markov Model

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Authors: Yi Fung Lin, Pei Chen Tsai

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Purpose: Many breast cancer survivors actively seek information about diet, exercise, and nutritional supplements to expedite recovery, reduce the risk of recurrence, and enhance their quality of life. While weight loss is not a primary concern for overweight or obese patients during the active treatment phase, they continue to face the additional burden and risks associated with excess weight post-treatment. This group has complex views on weight loss: they are concerned that exercise or dietary control might exacerbate symptoms, yet they also believe that obesity could negatively impact health. These perceptions can have both positive and negative effects on weight management. Therefore, this qualitative study aimed to explore the factors that influence overweight or obese breast cancer survivors' ability to manage their weight. Method: Eight women participated in in-depth interviews in 2022. Data was extracted based on the verbatim transcripts of the audio files, and the analysis was conducted through careful reading of the text. This qualitative research collected data until data saturation was reached. Results: A total of 22 codes were identified and subsequently integrated into three main themes: (1) Facilitators, including support systems, others' successful weight loss experiences, education, responsibility to others, and motivation; (2) Barriers, encompassing physical limitations, the stigma associated with weight loss, the cost of weight loss, antiestrogen medication, and cancer symptoms; (3) Variable Factors, which comprise perspectives on cancer, perspectives on obesity, and past experiences with weight loss. Conclusions: These findings are similar to previous research on barriers and motivators, highlighting variable factors that exist and can lead to either positive or negative effects on weight loss. The results can inform the development of weight management programs tailored to the needs of overweight or obese breast cancer survivors. Additionally, the identified factors represent vital variables for future studies, serving as valuable references for further research in this area.

Keywords: weight management, breast cancer, overweight, obese, cancer survivors

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Authors: Michael Barker

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Men’s rights activists are largely antifeminism. Evaluation of men’s rights discourses, however, shows that men’s rights’ goals would be better achieved by working with feminism. Discussion of men’s rights discourses, though, is prone to confusion because there is no commonly used men’s rights language. In the presentation ‘male sexism’, ‘matriarchy’ and ‘masculism’ will be unpacked as part of a suggested men’s rights language. Once equipped with a men’s rights vocabulary, sustained philosophical assessment of the extent to which several categories of male disadvantages are wrongful will be offered. Following this, conditions that cause each category of male sexism will be discussed. It shall be argued that male sexism is caused more so by matriarchy than by patriarchy or by feminism. In closing, the success at which various methods address the categories of male sexism will be contrasted. Ultimately, it will be shown that male disadvantages are addressed more successfully by methods that work with, than against, feminism.

Keywords: gender studies, feminism, patriarchy, men’s rights, male sexism, matriarchy, masculism

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Authors: Larasati Zuhro, Ida F Priyanto

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Background: In March 2020, Indonesia started to be affected by Covid-19, and the number of victims increased slowly but surely until finally, the highest number of victims reached the highest—about 50,000 persons—for the daily cases in the middle of 2021. Like other institutions, schools and universities were suddenly closed in March 2020, and students had to change their ways of studying from face-to-face to online. This sudden changed affected students and faculty, including LIS students and faculty because they never experienced online classes in Indonesia due to the previous regulation that academic and school activities were all conducted onsite. For almost two years, school and academic activities were held online. This indeed has affected the way students learned and faculty delivered their courses. This raises the question of whether students are now ready for their new learning activities due to the covid-19 disruption. Objectives: this study was conducted to find out the impact of covid-19 pandemic on the LIS learning process and the effectiveness of online classes for students of LIS in Indonesia. Methodology: This was qualitative research conducted among LIS students at UIN Sunan Kalijaga, Yogyakarta, Indonesia. The population are students who were studying for masters’program during covid-19 pandemic. Results: The study showed that students were ready with the online classes because they are familiar with the technology. However, the Internet and technology infrastructure do not always support the process of learning. Students mention slow WIFI is one factor that causes them not being able to study optimally. They usually compensate themselves by visiting a public library, a café, or any other places to get WIFI network. Noises come from the people surrounding them while they are studying online.Some students could not concentrate well when attending the online classes as they studied at home, and their families sometimes talk to other family members, or they asked the students while they are attending the online classes. The noise also came when they studied in a café. Another issue is that the classes were held in shorter time than that in the face-to-face. Students said they still enjoyed the onsite classes instead of online, although they do not mind to have hybrid model of learning. Conclusion: Pandemic of Covid-19 has changed the way students of LIS in Indonesia learn. They have experienced a process of migrating the way they learn from onsite to online. They also adapted their learning with the condition of internet access speed, infrastructure, and the environment. They expect to have hybrid classes in the future.

Keywords: learning, LIS students, pandemic, covid-19

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Authors: Virginia Chambers, Kamryn York, Mark Marnich

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T.I.M.E. for STEM (Transforming Integrative Maker Education for STEM learning) focuses on improving the quality and effectiveness of STEM education for pre-service teachers through a focus on the integration of maker space pedagogy. This National Science Foundation-funded project primarily focuses on undergraduate pre-service teaching students majoring in elementary education. The study contributes to the knowledge about teaching and learning by developing, implementing, and assessing faculty development, interactive instruction, and STEM lesson plan development. This project offers a valuable opportunity to improve STEM thinking skills by formally integrating STEM concepts throughout the pre-service teacher curriculum using an interdisciplinary approach. T.I.M.E. for STEM utilizes a maker space laboratory at Point Park University in Pittsburgh, PA, USA. However, the project design is such that other institutions of higher education can replicate the program with or without a physical maker space lab as the project’s findings and “maker mindset” are employed. Utilizing qualitative research methodology, the project investigates the following research question: What do pre-service teachers (education students) and faculty members identify as areas of pedagogical growth in STEM learning and teaching in a makerspace environment? This research highlights the impact of makerspace pedagogy on improving STEM education learning outcomes through an interdisciplinary constructivist approach. The project is expected to have a multiplier effect as it impacts STEM disciplinary and higher education faculty, pre-service teachers, and teacher preparation programs at other universities that benefit from what is learned at Point Park University. Ultimately, the future elementary students of the well-prepared pre-service teachers steeped in maker pedagogy and STEM content will have the potential to develop higher-level thinking skills and improve their mathematics and scientific achievement, which are essential for the 21st century STEM workforce.

Keywords: maker education, STEM learning, teacher education, elementary education

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Authors: Zhi Huang

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This study follows on from previous survey and focus group research exploring the effective teaching process in a translation classroom in Australian universities through case study method. The data analysis draws on social constructivist theory in translation teaching and focuses on teaching process aiming to discover how effective translation teachers conduct teaching in the classroom. The results suggest that effective teaching requires the teacher to have ability in four aspects: classroom management, classroom pedagogy, classroom communication, and teacher roles. Effective translation teachers are able to control the whole learning process, facilitate students in independent learning, guide students to be more critical about translation, giving both positive and negative feedback for students to reflect on their own, and being supportive, patient and encouraging to students for better classroom communication and learning outcomes. This study can be applied to other teachers in translation so that they can reflect on their own teaching in their education contexts and strive for being a more qualified translation teacher and achieving teaching effectiveness.

Keywords: case study, classroom observation, classroom teaching, effective translation teaching, teacher effectiveness

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Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

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Authors: Vitalii Yu. Kapitan, Aleksandr V. Perzhu, Konstantin V. Nefedev

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The necessity of studying magnetic multilayer structures is explained by the prospects of their practical application as a technological base for creating new storages medium. Magnetic multilayer films have many unique features that contribute to increasing the density of information recording and the speed of storage devices. Multilayer structures are structures of alternating magnetic and nonmagnetic layers. In frame of the classical Heisenberg model, lattice spin systems with direct short- and long-range exchange interactions were investigated by Monte Carlo methods. The thermodynamic characteristics of multilayer structures, such as the temperature behavior of magnetization, energy, and heat capacity, were investigated. The processes of magnetization reversal of multilayer structures in external magnetic fields were investigated. The developed software is based on the new, promising programming language Rust. Rust is a new experimental programming language developed by Mozilla. The language is positioned as an alternative to C and C++. For the Monte Carlo simulation, the Metropolis algorithm and its parallel implementation using MPI and the Wang-Landau algorithm were used. We are planning to study of magnetic multilayer films with asymmetric Dzyaloshinskii–Moriya (DM) interaction, interfacing effects and skyrmions textures. This work was supported by the state task of the Ministry of Education and Science of the Russia # 3.7383.2017/8.9

Keywords: The Monte Carlo methods, Heisenberg model, multilayer structures, magnetic skyrmion

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Authors: Beazidou Eleftheria

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Roma children face discrimination in schools where they are the minority. On the other hand, teachers do not identify the specific needs of Roma students for educational and social inclusion and generally use a very restricted repertoire of insufficient strategies for helping them. Modern classrooms can and should look different. Therefore, engaging in transformational learning with young children is a deliberate choice. Transformation implies a different way of thinking and acting. This requires new knowledge that incorporates multiple perspectives and actions in order to generate experiences for further learning. In this way, we build knowledge based on empirical examples, and we share what works efficiently. The present research aims at assisting the participating teachers to improve their teaching inclusive practice, thus ultimately benefiting their students. To increase the impact of transformative efforts with a ‘new’ teaching approach, we implemented a classroom-based action research program for over six months in five kindergarten classrooms with Roma and non-Roma students. More specifically, we explore a) information about participants’ experience of the program and b) if the program is successful in helping participants to change their teaching practice. Action research is, by definition, a form of inquiry that is intended to have both action and research outcomes. The action research process that we followed included five phases: 1. Defining the problem: As teachers said, the Roma students are often the most excluded group in schools (Low social interaction and participation in classroom activities) 2. Developing a plan to address the problem: We decided to address the problem by improving/transforming the inclusive practices that teachers implemented in their classrooms. 3. Acting: implementing the plan: We incorporated new activities for all students with the goals: a) All students being passionate about their learning, b) Teachers must investigate issues in the educational context that are personal and meaningful to children's growth, c) Establishment of a new module for values and skills for all students, d) Raising awareness in culture of Roma, e) Teaching students to reflect. 4. Observing: We explore the potential for transformation in the action research program that involves observations of students’ participation in classroom activities and peer interaction. – thus, generated evidence from data. 5. Reflecting and acting: After analyzing and evaluating the outcomes from data and considering the obstacles during the program’s implementation, we established new goals for the next steps of the program. These are centered in: a) the literacy skills of Roma students and b) the transformation of teacher’s perceptions and believes, which have a powerful impact on their willingness to adopt new teaching strategies. The final evaluation of the program showed a significant achievement of the transformative goals, which were related to the active participation of the Roma students in classroom activities and peer interaction, while the activities which were related to literacy skills did not have the expected results. In conclusion, children were equipped with relevant knowledge and skills to raise their potential and contribute to wider societal development as well as teachers improved their teaching inclusive practice.

Keywords: action research, inclusive practices, kindergarten, transformation

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

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Authors: K. R. Mukuna

Abstract:

Teachers are faced many challenges in the South African rural schools such as stress, depression, lack of resources, poor working relationships, inflexible curriculum etc. These could affect their wellbeing and effectiveness at the workplace. As a result, the study had a significance in the teacher’s lives, and community due teachers worked under conditions that are unfavourable to perform their jobs effectively. Despite these conditions, they still managed to do their jobs and the community is uplifted. However, this study aimed to explore factors that enable teachers to cope with workplace adversities at a rural school district in the Free State Province. It adopted a qualitative case study as a research design. Semi-structured interviews and colleges had employed as tools to collect data. Ten participants (n=10; 5 males and 5 females) were selected through purposive and convenience sampling. All participants selected from a South African rural school. Sesotho culture was their home language, and most of them had 5 years of teaching experiences. The thematic findings revealed that they developed abilities to cope with and adjust to the social and cultural environment. These included self-efficacy, developing problem-solving skills, awareness of strengths and asserts, self-managing of emotions, and self-confidence. This study concluded that these psychosocial factors contributed to coping with teacher’s diversities, and effectively stabilized their wellbeing in the schools.

Keywords: psychosocial factors, teachers counselling, teacher stress, workplace adversity, rural school, teachers’ wellbeing, teachers’ resilience, teachers’ self-efficacy, social interaction

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Authors: Faisal Kalota

Abstract:

Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon.

Keywords: big data, learning analytics, analytics, big data in education, Hadoop

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Authors: Promothes Saha

Abstract:

Recently, transportation engineering industry members at the study university showed concern that students lacked the skills needed to solve real-world engineering problems using spreadsheet data analysis. In response to the concerns shown by industry members, this study investigated how to engage students in a better way by incorporating spreadsheet analysis during class - also, help them learn the course topics. Helping students link theoretical knowledge to real-world problems can be a challenge. In this effort, in-class activities and worksheets were redesigned to integrate with Excel to solve example problems using built-in tools including cell referencing, equations, data analysis tool pack, solver tool, conditional formatting, charts, etc. The effectiveness of this technique was investigated using students’ evaluations of the course, enrollment data, and students’ comments. Based on the data of those criteria, it is evident that the spreadsheet activities may increase student learning.

Keywords: civil, engineering, active learning, transportation

Procedia PDF Downloads 128

Authors: I. Pelizzoni, C. Cavallini, I. Salvaderi, F. Cavallini

Abstract:

We present the improvement in writing skills obtained by 94 participants (aged between six and 10 years) with special educational needs through a writing enhancement program based on fluency principles. The study was planned and conducted with a single-subject experimental plan for each of the participants, in order to confirm the results in the literature. These results were obtained using precision teaching (PT) methodology to increase the number of written graphemes per minute in the pre- and post-test, by curriculum based measurement (CBM). Results indicated an increase in the number of written graphemes for all participants. The average overall duration of the intervention is 144 minutes in five months of treatment. These considerations have been analyzed taking account of the complexity of the implementation of measurement systems in real operational contexts (an Italian learning center) and important aspects of replicability and cost-effectiveness of such interventions.

Keywords: curriculum based measurement, precision teaching, writing skill, Italian learning center

Procedia PDF Downloads 116

Authors: Gaurav Kumar Sinha

Abstract:

The volatility of the oil market, influenced by geopolitical, economic, and environmental factors, presents significant challenges for stakeholders in predicting trends and demand. This article explores the application of artificial intelligence (AI) in developing robust forecasting models to anticipate changes in the oil market more accurately. We delve into various AI techniques, including machine learning, deep learning, and time series analysis, that have been adapted to analyze historical data and current market conditions to forecast future trends. The study evaluates the effectiveness of these models in capturing complex patterns and dependencies in market data, which traditional forecasting methods often miss. Additionally, the paper discusses the integration of external variables such as political events, economic policies, and technological advancements that influence oil prices and demand. By leveraging AI, stakeholders can achieve a more nuanced understanding of market dynamics, enabling better strategic planning and risk management. The article concludes with a discussion on the potential of AI-driven models in enhancing the predictive accuracy of oil market forecasts and their implications for global economic planning and strategic resource allocation.

Keywords: AI forecasting, oil market trends, machine learning, deep learning, time series analysis, predictive analytics, economic factors, geopolitical influence, technological advancements, strategic planning

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Authors: Silvia Tamayo-Moreno, Diana Pérez-Marín

Abstract:

In this paper, the possibility of children studying by using an interactive learning technology called Pedagogic Conversational Agent is presented. The main benefit is that the agent is able to adapt the dialogue to each student and to provide automatic feedback. Moreover, according to Math teachers, in many cases students are unable to solve the problems even knowing the procedure to solve them, because they do not understand what they have to do. The hypothesis is that if students are helped to understand what they have to solve, they will be able to do it. Taken that into account, we have started the development of Dr. Roland, an agent to help students understand Math problems following a User-Centered Design methodology. The use of this methodology is proposed, for the first time, to design pedagogic agents to teach any subject from Secondary down to Pre-Primary education. The reason behind proposing a methodology is that while working on this project, we noticed the lack of literature to design and evaluate agents. To cover this gap, we describe how User-Centered Design can be applied, and which usability techniques can be applied to evaluate the agent.

Keywords: pedagogic conversational agent, human-computer interaction, user-centered design, natural language interface

Procedia PDF Downloads 307

Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

Abstract:

In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

Procedia PDF Downloads 45