Search results for: mobile-assisted language learning

Authors: Ahmed Shuhaiber

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The explosion of the World Wide Web and the electronic trend of university teaching have transformed the learning style to become more learner-centred, Which has popularized the digital delivery of mediated lectures as an alternative or an adjunct to traditional lectures. Despite its potential and popularity, virtual lectures have not been adopted yet in Jordanian universities. This research aimed to fill this gap by studying the factors that influence student’s willingness to accept virtual lectures in one Jordanian University. A quantitative approach was followed by obtaining 216 survey responses and statistically applying the UTAUT model with some modifications. Results revealed that performance expectancy, effort expectancy, social influences and self-efficacy could significantly influence student’s attitudes towards virtual lectures. Additionally, facilitating conditions and attitudes towards virtual lectures were found with significant influence on student’s intention to take virtual lectures. Research implications and future work were specified afterwards.

Keywords: E-learning, student willingness, UTAUT, virtual Lectures, web-based learning systems

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Authors: Hamsini Pulugurtha, P. V. S. L. Jagadamba

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Programmed Speech to Text and Text Summarization Using Graph-based Algorithms can be utilized in gatherings to get the short depiction of the gathering for future reference. This gives signature check utilizing Siamese neural organization to confirm the personality of the client and convert the client gave sound record which is in English into English text utilizing the discourse acknowledgment bundle given in python. At times just the outline of the gathering is required, the answer for this text rundown. Thus, the record is then summed up utilizing the regular language preparing approaches, for example, solo extractive text outline calculations

Keywords: Siamese neural network, English speech, English text, natural language processing, unsupervised extractive text summarization

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Authors: Ilona Buchem, Carolin Gellner

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The call for the support of senior learners in the development of digital literacy has become prevalent in recent years, especially in view of the aging societies paired with advances in digitalization in all spheres of life, including e-health. The goal has been to create opportunities for learning that incorporate the use of context in a reflective and dialogical way. Contextual learning has focused on developing skills through the application of authentic problems. While major research efforts in supporting senior learners in developing digital literacy have been invested so far in e-learning, focusing on knowledge acquisition and cognitive tasks, little research exists in reflective mentoring and coaching with the help of pedagogical agents and addressing the contextual dimensions of learning. This paper describes an approach to creating opportunities for senior learners to improve their digital literacy in the authentic context of the electronic health record (EHR) with the support of an intelligent virtual learning coach. The paper focuses on the design of the virtual coach as part of an e-learning system, which was developed in the EPA-Coach project founded by the German Ministry of Education and Research. The paper starts with the theoretical underpinnings of contextual learning and the related design considerations for a virtual learning coach based on previous studies. Since previous research in the area was mostly designed to cater to the needs of younger audiences, the results had to be adapted to the specific needs of senior learners. Next, the paper outlines the stages in the design of the virtual coach, which included the adaptation of the design requirements, the iterative development of the prototypes, the results of the two evaluation studies and how these results were used to improve the design of the virtual coach. The paper then presents the four prototypes of a senior-friendly virtual learning coach, which were designed to represent different preferences related to the visual appearance, the communication and social interaction styles, and the pedagogical roles. The first evaluation of the virtual coach design was an exploratory, qualitative study, which was carried out in October 2020 with eight seniors aged 64 to 78 and included a range of questions about the preferences of senior learners related to the visual design, gender, age, communication and role. Based on the results of the first evaluation, the design was adapted to the preferences of the senior learners and the new versions of prototypes were created to represent two male and two female options of the virtual coach. The second evaluation followed a quantitative approach with an online questionnaire and was conducted in May 2021 with 41 seniors aged 66 to 93 years. Following three research questions, the survey asked about (1) the intention to use, (2) the perceived characteristics, and (3) the preferred communication/interaction style of the virtual coach, i. e. task-oriented, relationship-oriented, or a mix. This paper follows with the discussion of the results of the design process and ends with conclusions and next steps in the development of the virtual coach including recommendations for further research.

Keywords: virtual learning coach, virtual mentor, pedagogical agent, senior learners, digital literacy, electronic health records

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Authors: Felix Golla

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In an era where technology increasingly intersects with education, the potential of chatbots and ChatGPT agents in enhancing student learning experiences in higher education is both significant and timely. This study explores the integration of these AI-driven tools in educational settings, emphasizing their design and functionality to meet the specific needs of students. Recognizing the gap in literature concerning student-centered AI applications in education, this research offers valuable insights into the role and efficacy of chatbots and ChatGPT agents as educational tools. Employing qualitative research methodologies, the study involved conducting semi-structured interviews with university students. These interviews were designed to gather in-depth insights into the students' experiences and expectations regarding the use of AI in learning environments. The High-Performance Cycle Model, renowned for its focus on goal setting and motivation, served as the theoretical framework guiding the analysis. This model helped in systematically categorizing and interpreting the data, revealing the nuanced perceptions and preferences of students regarding AI tools in education. The major findings of the study indicate a strong preference among students for chatbots and ChatGPT agents that offer personalized interaction, adaptive learning support, and regular, constructive feedback. These features were deemed essential for enhancing student engagement, motivation, and overall learning outcomes. Furthermore, the study revealed that students perceive these AI tools not just as passive sources of information but as active facilitators in the learning process, capable of adapting to individual learning styles and needs. In conclusion, this study underscores the transformative potential of chatbots and ChatGPT agents in higher education. It highlights the need for these AI tools to be designed with a student-centered approach, ensuring their alignment with educational objectives and student preferences. The findings contribute to the evolving discourse on AI in education, suggesting a paradigm shift towards more interactive, responsive, and personalized learning experiences. This research not only informs educators and technologists about the desirable features of educational chatbots but also opens avenues for future studies to explore the long-term impact of AI integration in academic curricula.

Keywords: chatbot design in education, high-performance cycle model application, qualitative research in AI, student-centered learning technologies

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Authors: Adisorn Banhan, Toansakul Santiboon, Prasong Saihong

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Using the Socio-Scientific Issues-Based Learning Method is to compare of the blended instruction of STEM education with a sample consisted of 84 students in 2 classes at the 11th grade level in Sarakham Pittayakhom School. The 2-instructional models were managed of five instructional lesson plans in the context of electronic wave issue. These research procedures were designed of each instructional method through two groups, the 40-experimental student group was designed for the instructional STEM education (STEMe) and 40-controlling student group was administered with the Socio-Scientific Issues-Based Learning (SSIBL) methods. Associations between students’ learning achievements of each instructional method and their science attitudes of their predictions to their exploring activities toward physics with the STEMe and SSIBL methods were compared. The Measuring Reasonable Ethics Test (MRET) was assessed students’ reasonable ethics with the STEMe and SSIBL instructional design methods on two each group. Using the pretest and posttest technique to monitor and evaluate students’ performances of their reasonable ethics on electromagnetic wave issue in the STEMe and SSIBL instructional classes were examined. Students were observed and gained experience with the phenomena being studied with the Socio-Scientific Issues-Based Learning method Model. To support with the STEM that it was not just teaching about Science, Technology, Engineering, and Mathematics; it is a culture that needs to be cultivated to help create a problem solving, creative, critical thinking workforce for tomorrow in physics. Students’ attitudes were assessed with the Test Of Physics-Related Attitude (TOPRA) modified from the original Test Of Science-Related Attitude (TOSRA). Comparisons between students’ learning achievements of their different instructional methods on the STEMe and SSIBL were analyzed. Associations between students’ performances the STEMe and SSIBL instructional design methods of their reasonable ethics and their science attitudes toward physics were associated. These findings have found that the efficiency of the SSIBL and the STEMe innovations were based on criteria of the IOC value higher than evidence as 80/80 standard level. Statistically significant of students’ learning achievements to their later outcomes on the controlling and experimental groups with the SSIBL and STEMe were differentiated between students’ learning achievements at the .05 level. To compare between students’ reasonable ethics with the SSIBL and STEMe of students’ responses to their instructional activities in the STEMe is higher than the SSIBL instructional methods. Associations between students’ later learning achievements with the SSIBL and STEMe, the predictive efficiency values of the R2 indicate that 67% and 75% for the SSIBL, and indicate that 74% and 81% for the STEMe of the variances were attributable to their developing reasonable ethics and science attitudes toward physics, consequently.

Keywords: socio-scientific issues-based learning method, STEM education, science attitudes, measurement, reasonable ethics, physics classes

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Authors: Mary Rosa Espinosa-Ochoa

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Some constructions appear suddenly in children’s speech and are productive from the beginning. These constructions are supported by others, previously acquired, with which they share semantic and pragmatic features. Thus, for example, the acquisition of the passive voice in German is supported by other constructions with which it shares the lexical verb sein (“to be”). This also occurs in Spanish, in the acquisition of the progressive aspectual periphrasis estar (“to be”) + verb root + -ndo (present participle), supported by locative constructions acquired earlier with the same verb. The periphrasis shares with the locative constructions not only the lexical verb estar, but also pragmatic relations. Both constructions can be used to answer the question ¿Dónde está? (“Where is he/she/it?”), whose answer could be either Está aquí (“He/she/it is here”) or Se está bañando (“He/she/it is taking a bath”).This study is a corpus-based analysis of two children (1;08-2;08) and the input directed to them: it proposes that the pragmatic and semantic support from previously-acquired constructions comes from the input, during interaction with others. This hypothesis is based on analysis of constructions with estar, whose use to express temporal change (which differentiates it from its counterpart ser [“to be”]), is given in variation sets, similar to those described by Küntay and Slobin (2002), that allow the child to perceive the change of place experienced by nouns that function as its grammatical subject. For example, at different points during a bath, the mother says: El jabón está aquí “The soap is here” (beginning of bath); five minutes later, the soap has moved, and the mother says el jabón está ahí “the soap is there”; the soap moves again later on and she says: el jabón está abajo de ti “the soap is under you”. “The soap” is the grammatical subject of all of these utterances. The Spanish verb + -ndo is a progressive phase aspect encoder of a dynamic state that generates a token. The verb + -ndo is also combined with verb estar to encode. It is proposed here that the phases experienced in interaction with the adult, in events related to the verb estar, allow a child to generate this dynamicity and token reading of the verb + -ndo. In this way, children begin to produce the periphrasis suddenly and productively, even though neither the periphrasis nor the verb + -ndo itself are frequent in adult speech.

Keywords: child language acquisition, input, variation sets, Spanish language

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Authors: Kimhong Ann, Nathaphon Boonnam

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The prediction of student performance has become a key area of interest for researchers, with efforts focused on developing models to enhance student competence and improve educational institutions. These models help educators design effective teaching strategies, address specific student needs, and guide learning outcomes at all educational levels, including high school students, preservice teachers, and beyond. Therefore, we aim to identify the most accurate machine learning technique for predicting preservice teachers’ performance. Additionally, it seeks to explore the relationships between various features and identify key indicators for creating an effective prediction model. Key attributes considered in this study includes courses taken during their study, course grades, cumulative grades, and student satisfaction. The data of preservice teachers will be collected from two universities in Cambodia and Thailand for 2021 to 2024. We will employ Chi-Square as a feature selection method. Various machine learning algorithms will be used and evaluated using classification metrics such as accuracy, precision, recall, and F1-score with K-fold cross-validation as the validation technique. Due to the characteristics of data collection and the theoretical concerns in the classification, the preliminary expectations indicate that the Random Forest algorithm may yield the highest accuracy for predicting preservice teachers’ performance, though the actual outcomes may vary based on the research findings. The outcomes of this study have the potential to assist educators in developing effective strategies for predicting and enhancing the performance of preservice teachers. By leveraging the predictive model, educators can anticipate student performance, identify challenges and difficulties faced by students, and provide timely feedback and support.

Keywords: machine learning, preservice teacher, classification algorithm, predictive modeling

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Authors: Joseph Lillington, Tom Gout, Mike Harrison, Ian Farnan

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The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data.

Keywords: machine learning, predictive modelling, pattern recognition, radioactive waste glass

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Authors: Luigi Iannacci

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This study critically explores how disability and disability are presently and problematically configured within education. As such, pedagogies, discourses, and practices that shape this configuration are examined to forward a reconceptualization of disability as it relates to education and the inclusion of students with special needs in mainstream classroom contexts. The study examines how the dominant medical/deficit model of disability positions students with special needs and advocates for a shift towards a social/critical model of disability as applied to education and classrooms. This is demonstrated through a critical look at how language, processes, and ‘interventions’ name and address deficits people who have a disability are presumed to have and, as such, conceptualize these deficits as inherent flaws that are in need of ‘fixing.’ The study will demonstrate the necessary shifts in thinking, language and practice required to forward a critical/social model of disability. The ultimate aim of this research is to offer a much-needed reconceptualization of inclusion that recognizes disability as epistemology, identity, and diversity through a critical exploration of dominant discourses that impact language, policy, instruction and ultimately, the experiences students with disabilities have within mainstream classrooms. The presentation seeks to explore disability as neurodiversity and therefore elucidate how people with disabilities can demonstrate these ways of knowing within inclusive education that avoids superficial approaches that are not responsive to their needs. This research is, therefore, of interest and use to educators teaching at the elementary, secondary, and in-service levels as well as graduate students and scholars working in the areas of inclusion, special education, and literacy. Ultimately the presentation attempts to foster a social justice and human rights-focused approach to inclusion that is responsive to students with disabilities and, as such ensures a reconceptualization of present language, understandings and practices that continue to configure disability in problematic ways.

Keywords: inclusion, disability, critical approach, social justice

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Authors: Siddhartha Dhungana

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This article primarily explores dimensions in English language teacher's professionalism so that a teacher could reflect and make a strategic professional devotion to implement effective educational programs for the present and the future. The paper substantially incorporates the eastern Hindu practices, especially life values from the Bhagavad Gita, as a basis of teacher’s professional enrichment. Basically, it applies three categorical practices, i.e., Karma Yoga, Jnana Yoga, and Bhakti Yoga, in teachers’ professionality to illustrate, ignite further ahead and sharpen academic journey, professional journey, and professional devotion reflecting common practices. In this journey, a teacher comes to a stage of professional essence as s/he surpasses Karma Yoga, Jnana Yoga, and Bhakti Yoga with their basic quality formation. To illustrate their essence-making process, the three narrative stories for each category mentioned above are analyzed. The data collected from a research participant who has a high level of professional success and who inspires all English Language teachers in Nepal to develop stories for narrative analysis. The narrative analysis is based on eastern themes that are supported by Vygotsky's concept of developmental psychology. Moreover, the structural analysis is based on Gary Barkhuizen's narrative analysis.

Keywords: Karma Yoga, Jnana Yoga, Bhakti Yoga, Vygotsky's concepts, narrative analysis

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Authors: Raymond Feng, Shadi Ghiasi

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An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy.

Keywords: EEG, electroencephalogram, machine learning, mood, music enjoyment, physiological signals

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Authors: Chinlun Lai, Lunjyh Jiang

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Due to the heavy burden and pressure of caring for infants, an integrated automatic baby watching system based on IoT smart sensing and deep learning machine vision techniques is proposed in this paper. By monitoring infant body conditions such as heartbeat, breathing, body temperature, sleeping posture, as well as the surrounding conditions such as dangerous/sharp objects, light, noise, humidity and temperature, the proposed system can analyze and predict the obvious/potential dangerous conditions according to observed data and then adopt suitable actions in real time to protect the infant from harm. Thus, reducing the burden of the caregiver and improving safety efficiency of the caring work. The experimental results show that the proposed system works successfully for the infant care work and thus can be implemented in various life fields practically.

Keywords: baby care system, Internet of Things, deep learning, machine vision

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Authors: Rajaian Hoonejani Mohammad, Eshraghi Pegah, Zomorodian Zahra Sadat, Tahsildoost Mohammad

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Prakash Singh

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The focus of this exploratory study is on whether teachers demonstrate emphatic concern for their learners in planning, implementing and assessing learning outcomes in their regular classrooms. Empathy must be shown to all learners equally and not only for high-risk learners at the expense of other ability learners. Empathy demonstrated by teachers allows them to build a stronger bond with all their learners. This bond based on trust leads to positive outcomes for learners to be able to excel in their work. Empathic teachers must make every effort to simplify the subject matter for high risk learners so that these learners not only enjoy their learning activities but are also successful like their more able peers. A total of 87.5% of the participants agreed that empathy allows teachers to demonstrate humanistic values in their choice of learning materials for learners of different abilities. It is therefore important for teachers to select content and instructional materials that will contribute to the learners’ success in the mainstream of education. It is also imperative for teachers to demonstrate empathic skills and consequently, to be attuned to the emotions and emotional needs of their learners. Schools need to be reformed, not by simply lengthening the school day or by simply adding more content in the curriculum, but by making school more satisfying to learners. This must be consistent with their diverse learning needs and interests so that they gain a sense of power, fulfillment, and importance in their regular classrooms. Hence, teacher - pupil relationships based on empathic concern for the latter’s educational needs lays the foundation for quality education to be offered.

Keywords: emotional intelligence, empathy, learners’ emotional needs, teachers’ empathic skills

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Authors: Carlos Gordon, Patricio Encalada, Henry Lema, Diego Leon, Dennis Chicaiza

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The following work presents a proposal of autonomous navigation of mobile robots implemented in an omnidirectional robot Kuka Youbot. We have been able to perform the integration of robotic operative system (ROS) and machine learning algorithms. ROS mainly provides two distributions; ROS hydro and ROS Kinect. ROS hydro allows managing the nodes of odometry, kinematics, and path planning with statistical and probabilistic, global and local algorithms based on Adaptive Monte Carlo Localization (AMCL) and Dijkstra. Meanwhile, ROS Kinect is responsible for the detection block of dynamic objects which can be in the points of the planned trajectory obstructing the path of Kuka Youbot. The detection is managed by artificial vision module under a trained neural network based on the single shot multibox detector system (SSD), where the main dynamic objects for detection are human beings and domestic animals among other objects. When the objects are detected, the system modifies the trajectory or wait for the decision of the dynamic obstacle. Finally, the obstacles are skipped from the planned trajectory, and the Kuka Youbot can reach its goal thanks to the machine learning algorithms.

Keywords: autonomous navigation, machine learning, path planning, robotic operative system, open source computer vision library

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Authors: Björn de Kruijf, Arjen Edzes, Sietske Waslander

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In an ever-quickly changing society, lifelong development is seen as a solution to labor market problems by politicians and policymakers. In this paper, we investigate how policy instruments are used to increase participation in lifelong development and on which behavioral principles policy is based. Digitization, automation, and an aging population change society and the labor market accordingly. Skills that were once most sought after in the workforce can become abundantly present. For people to remain relevant in the working population, they need to continue adapting new skills useful in the current labor market. Many reports have been written that focus on the role of lifelong development in this changing society and how lifelong development can help keep people adapt and stay relevant. Inspired by these reports, governments have implemented a broad range of policies to support participation in lifelong development. The question we ask ourselves is how government policies promote participation in lifelong development. This stems from a complex interplay of policy instruments and learning. Regulation, economic and soft instruments can be combined to promote lifelong development, and different types of education further complex policies on lifelong development. Literature suggests that different stages in people’s lives might warrant different methods of learning. Governments could anticipate this in their policies. In order to influence people’s behavior, the government can tap into a broad range of sociological, psychological, and (behavioral) economic principles. The traditional economic assumption that behavior is rational is known to be only partially true, and the government can use many biases in human behavior to stimulate participation in lifelong development. In this paper, we also try to find which biases the government taps into to promote participation if they tap into any of these biases. The goal of this paper is to analyze government policies intended to promote participation in lifelong development. To do this, we develop a framework to analyze the policies on lifelong development. We specifically incorporate the role of learning and the behavioral principles underlying policy instruments in the framework. We apply this framework to the case of the Netherlands, where we examine a set of policy documents. We single out the policies the government has put in place and how they are vertically and horizontally related. Afterward, we apply the framework and classify the individual policies by policy instrument and by type of learning. We find that the Dutch government focuses on formal and non-formal learning in their policy instruments. However, the literature suggests that learning at a later age is mainly done in an informal manner through experiences.

Keywords: learning, lifelong development, policy analysis, policy instruments

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Authors: Changqiao Wu, Guoqing Ding, Xin Chen

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In this paper, ways of modeling dynamic measurement systems are discussed. Specially, for linear system with single-input single-output, it could be modeled with shallow neural network. Then, gradient based optimization algorithms are used for searching the proper coefficients. Besides, method with normal equation and second order gradient descent are proposed to accelerate the modeling process, and ways of better gradient estimation are discussed. It shows that the mathematical essence of the learning objective is maximum likelihood with noises under Gaussian distribution. For conventional gradient descent, the mini-batch learning and gradient with momentum contribute to faster convergence and enhance model ability. Lastly, experimental results proved the effectiveness of second order gradient descent algorithm, and indicated that optimization with normal equation was the most suitable for linear dynamic models.

Keywords: dynamic system modeling, neural network, normal equation, second order gradient descent

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Authors: Amal Machtalay

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In this survey, we aim to review the rapidly growing literature on numerical methods to solve different forms of mean field problems, namely mean field games (MFG), mean field controls (MFC), potential MFGs, and master equations, as well as their corresponding recent applications. Here, we distinguish two families of numerical methods: iterative methods based on mesh generation and those called mesh-free, normally related to neural networking and learning frameworks.

Keywords: mean-field games, numerical schemes, partial differential equations, complex systems, machine learning

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Authors: Zhilong Xie, Jinwen Huang, Guofang Zeng

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The existing body of research on bilingualism has consistently linked the use of multiple languages to enhanced cognitive control. Numerous studies have demonstrated that bilingual individuals exhibit advantages in non-linguistic tasks demanding cognitive control. However, recent investigations have challenged these findings, leading to a debate regarding the extent and nature of bilingual advantages. The adaptive control hypothesis posits that variations in bilingual experiences hold the key to resolving these controversies. This study aims to contribute to this discussion by exploring the impact of second language (L2) listening experience on cognitive control among young Chinese-English bilinguals. By examining this specific aspect of bilingualism, the study offers a perspective on the origins of bilingual advantages. This study employed a range of cognitive tasks, including the Flanker task, Wisconsin Card Sorting Test (WCST), Operation Span Task (OSPAN), and a second language listening comprehension test. After controlling for potential confounding variables such as intelligence, socioeconomic status, and overall language proficiency, independent sample t-test analysis revealed significant differences in performance between groups with high and low L2 listening proficiency in the Flanker task and OSPAN. However, no significant differences emerged between the two groups in the WCST. These findings suggest that L2 listening proficiency has a significant impact on inhibitory control and working memory but not on conflict monitoring or mental set shifting. These specific findings provide a more nuanced understanding of the origins of bilingual advantages within a specific bilingual context, highlighting the importance of considering the nature of bilingual experience when exploring cognitive benefits.

Keywords: bilingual advantage, inhibitory control, L2 listening, working memory

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Authors: David Sinfield, Thomas Cochrane, Marcos Steagall

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While there is much hype around the potential and development of mobile virtual reality (VR), the two key critical success factors are the ease of user experience and the development of a simple user-generated content ecosystem. Educational technology history is littered with the debris of over-hyped revolutionary new technologies that failed to gain mainstream adoption or were quickly superseded. Examples include 3D television, interactive CDROMs, Second Life, and Google Glasses. However, we argue that this is the result of curriculum design that substitutes new technologies into pre-existing pedagogical strategies that are focused upon teacher-delivered content rather than exploring new pedagogical strategies that enable student-determined learning or heutagogy. Visual Communication design based learning such as Graphic Design, Illustration, Photography and Design process is heavily based on the traditional forms of the classroom environment whereby student interaction takes place both at peer level and indeed teacher based feedback. In doing so, this makes for a healthy creative learning environment, but does raise other issue in terms of student to teacher learning ratios and reduced contact time. Such issues arise when students are away from the classroom and cannot interact with their peers and teachers and thus we see a decline in creative work from the student. Using AR and VR as a means of stimulating the students and to think beyond the limitation of the studio based classroom this paper will discuss the outcomes of a student project considering the virtual classroom and the techniques involved. The Atelier learning environment is especially suited to the Visual Communication model as it deals with the creative processing of ideas that needs to be shared in a collaborative manner. This has proven to have been a successful model over the years, in the traditional form of design education, but has more recently seen a shift in thinking as we move into a more digital model of learning and indeed away from the classical classroom structure. This study focuses on the outcomes of a student design project that employed Augmented Reality and Virtual Reality technologies in order to expand the dimensions of the classroom beyond its physical limits. Augmented Reality when integrated into the learning experience can improve the learning motivation and engagement of students. This paper will outline some of the processes used and the findings from the semester-long project that took place.

Keywords: augmented reality, blogging, design in community, enhanced learning and teaching, graphic design, new technologies, virtual reality, visual communications

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Authors: Fonseca Jully, Chia Maribel, Rodríguez Ilba

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This paper is a qualitative research report. A group of students form a public university in a small town in Colombia participated in this study which aimed at describing to what extend the use of social ads, published on the internet, helped to develop their critical thinking skills. Students’ productions, field notes, video recordings and direct observation were the instruments and techniques used by the researches in order to gather the data which was analyzed under the principles of grounded theory and triangulation. The implementation of social ads into the classroom evidenced a noticeable improvement in students’ ability to interpret and argue social issues, as well as, their self-improvement in oral and written production in English, as a foreign language.

Keywords: Ads, critical argumentation, critical thinking, social issues

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Authors: Ifeoluwa Omotehinse Oloruntoba

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Through comparative and causal models of translation, this paper examined the translation of ‘bread’ into the Yoruba language in three Yoruba versions of the Bible: Bibeli Yoruba Atoka (YBA), Bibeli Mimo ni Ede Yoruba Oni (BMY) and Bibeli Mimo (BM). In biblical times, bread was a very important delicacy that it was synonymous with food in general and in the Bible, bread sometimes refers to a type of food (a mixture of flour, water, and yeast that is baked) or food in general. However, this is not the case in the Yoruba culture. In fact, some decades ago, bread was not known in Nigeria and had no name in the Yoruba language until the 1900s when it was codified as burẹdi in Yoruba, a term borrowed from English and transliterated. Nevertheless, in Nigeria presently, bread is not a special food and it is not appreciated or consumed like in the West. This makes it difficult to translate bread in the Bible into Yoruba. From an investigation on the translation of this term, it was discovered that bread which has 330 occurrences in the English Bible translation (King James) has few occurrences in the three Yoruba Bible versions. In the first version (YBA) published in the 1880s, where bread is synonymous with food in general, it is mostly translated as oúnjẹ (food) or the verb jẹ (to eat), revealing that something is eaten but not indicating what it is. However, when the bread is a type of food, it is rendered as akara, a special delicacy of the Yoruba people made from beans flour. In the later version (BMY) published in the 1990s, bread as food, in general, is also mainly translated as oúnjẹ or the verb jẹ, but when it is a type of food, it is translated as akara with few occurrences of burẹdi. In the latest edition (BM), bread as food is either rendered as ounje or literally translated as burẹdi. Where it is a type of food in this version, it is mainly rendered as burẹdi with few occurrences of akara, indicating the assimilation of bread into the Yoruba culture. This result, although limited, shows that the Bible was translated into Yoruba to make it accessible to Yoruba speakers in their everyday language, hence the application of both domesticating and foreignising strategies. This research also emphasizes the role of the translator as an intermediary between two cultures.

Keywords: translation, Bible, Yoruba, cultural problems

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Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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Authors: Ngong Eugene Ekia

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Education is the foundation for the success of any society as its impact transcends across all sectors, including economics, politics, and social welfare. It is through education that individuals acquire the necessary knowledge and skills to succeed in life and contribute meaningfully to society. However, the world is changing rapidly, and it is vital for education systems to adapt to these changes to remain relevant. In this paper, we will discuss the current trends and challenges in education and management and propose solutions that can enable individuals to thrive in an ever-evolving world.

Keywords: access to education, effective teaching and learning, strong management practices, and empowering and personal development

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Authors: Laila Shikaki

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‘Writings about Home’ discusses the poetry of Palestinian American female poets, especially ones who write about their homelands, living away from home, as well as their family ties to the land. This is a paper about poetry, but it is also about Palestinian American women who use English to convey issues pertaining to homesickness, family, and language. She study poems by Naomi Shihab Nye and Natalie Hanal. In ‘My Father and the Fig Tree,’ for example, Nye depicts her father’s life away from Palestine and his attachment to a tree that represents his homeland and nostalgia. Nye’s style is diverse and unified, and her attention is to details and images. While her words and imagery are usually simple, they are always rich in meaning. Nathalie Handal’s poetry, on the other hand, has a more complicated, multi-layered, and nuanced style as the poet herself lived in many areas and spoke multiple languages. ‘Bethlehem,’ for instance, depicts her city of origin, recalling her grandfather. Her poem ‘Blue Hours’ illustrates a persona’s difficulty in belonging, switching from one language to the next, and feeling a betrayal in both. This paper pays attention to language and how being bilingual adds another level of exile and pain to those who have fled or were forced to leave Palestine. This paper is very timely as the issue of Palestinian freedom and its right to autonomy and self-determination is the central stage for many Americans, seen in their protests, university encampments, and graduation ceremonies, not forgetting its effect on voters’ decisions for president and elected officials.

Keywords: Palestinian American, poetry, homeland, Nye, Handal

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Authors: A. Dinelka Panagoda, Bathiya Bandara, Chamod Wijetunga, Chathura Malinda, Lakmal Rupasinghe, Chethana Liyanapathirana

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This research takes us forward with the concepts of Federated Learning and Software-Defined Networking (SDN) to introduce an efficient malware detection technique and provide a mitigation mechanism to give birth to a resilient and automated healthcare sector network system by also adding the feature of extended privacy preservation. Due to the daily transformation of new malware attacks on hospital Integrated Clinical Environment (ICEs), the healthcare industry is at an undefinable peak of never knowing its continuity direction. The state of blindness by the array of indispensable opportunities that new medical device inventions and their connected coordination offer daily, a factor that should be focused driven is not yet entirely understood by most healthcare operators and patients. This solution has the involvement of four clients in the form of hospital networks to build up the federated learning experimentation architectural structure with different geographical participation to reach the most reasonable accuracy rate with privacy preservation. While the logistic regression with cross-entropy conveys the detection, SDN comes in handy in the second half of the research to stack up the initial development phases of the system with malware mitigation based on policy implementation. The overall evaluation sums up with a system that proves the accuracy with the added privacy. It is no longer needed to continue with traditional centralized systems that offer almost everything but not privacy.

Keywords: software-defined network, federated learning, privacy, integrated clinical environment, decentralized learning, malware detection, malware mitigation

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Authors: Deepa Kapoor, Rajshree Singh

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This paper appreciates the emergence and growing importance as a new production factor makes the development of technologies, methodologies and strategies for measurement, creation, and diffusion into one of the main priorities of the organizations in the knowledge society. There are many models for creation and management of knowledge and diverse and varied perspectives for study, analysis, and understanding. In this article, we will conduct a theoretical approach to the type of models for the creation and management of knowledge; we will discuss some of them and see some of the difficulties and the key factors that determine the success of the processes for the creation and management of knowledge.

Keywords: knowledge creation, knowledge management, organizational development, organization learning

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Authors: Shalini Attri

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Silence has been 'scriptured' in women within dominating social structures, as the modes of speaking and behaving which deny women free investiture to language. A woman becomes the product of ideological constructions as language substantiates andro-centric bias. Constrained from writing/speaking in the public sphere, women have traditionally been confined to expressing themselves in writing private poetry, letters or diaries. The helplessness of a woman is revealed in the ways in which she is expected to speak a language, which, in fact, is man-made. There are visible binaries of coloniser- colonised; Western-Eastern; White-Black, Nature-Culture, even Male-Female that contribute significantly to our understanding of the concept of representation and its resultant politics. Normally, an author is labeled as feminist, humanist, or propagandist and this process of labeling correspond to a sense of politics besides his inclination to a particular field. One cannot even think of contemporary literature without this representational politics. Thus, each and every bit of analysis of a work of literature demands a political angle to be dealt with. Besides literature, the historical facts and manuscripts are also subject to this politics. The image of woman as someone either dependent on man or is exploited by him only provides half the picture of this representational politics. The present paper is an attempt to study Panchali’s (Draupadi of Mahabharata) voiceless articulation and her representation as a strong woman in Chitra Divakurani’s The Palace of Illusions.

Keywords: politics, representation, silence, social structures

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Authors: Irina-Ana Drobot

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Teaching English to Engineers is part of English for Specific Purposes, a domain which is under the attention of English students especially under the current conditions of finding jobs and establishing partnerships outside Romania. The paper will analyse the existing textbooks together with the teaching strategies they adopt. Teaching English to Engineering students can intersect with domains such as psychology and cultural studies in order to teach them efficiently. Textbooks for students of ESP, ranging from those at the Faculty of Economics to those at the Faculty of Engineers, have shifted away from using specialized vocabulary, drills for grammar and reading comprehension questions and toward communicative methods and the practical use of language. At present, in Romania, grammar is neglected in favour of communicative methods. The current interest in translation studies may indicate a return to this type of method, since only translation specialists can distinguish among specialized terms and determine which are most suitable in a translation. Engineers are currently encouraged to learn English in order to do their own translations in their own field. This paper will analyse the issue of the extent to which it is useful to teach Engineering students to do translations in their field using cognitive psychology applied to language teaching, including issues such as motivation and social psychology. Teaching general English to engineering students can result in lack of interest, but they can be motivated by practical aspects which will help them in their field. This is why this paper needs to take into account an interdisciplinary approach to teaching English to Engineers.

Keywords: cognition, ESP, motivation, psychology

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Authors: Carlos Theran, Yohn Parra Bautista, Victor Adankai, Richard Alo, Jimwi Liu, Clement G. Yedjou

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Alzheimer's disease (AD) is a neurodegenerative disorder primarily characterized by deteriorating cognitive functions. AD has gained relevant attention in the last decade. An estimated 24 million people worldwide suffered from this disease by 2011. In 2016 an estimated 40 million were diagnosed with AD, and for 2050 is expected to reach 131 million people affected by AD. Therefore, detecting and confirming AD at its different stages is a priority for medical practices to provide adequate and accurate treatments. Recently, Machine Learning (ML) models have been used to study AD's stages handling missing values in multiclass, focusing on the delineation of Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and normal cognitive (CN). But, to our best knowledge, robust performance information of these models and the missing data analysis has not been presented in the literature. In this paper, we propose studying the performance of five different machine learning models for AD's stages multiclass prediction in terms of accuracy, precision, and F1-score. Also, the analysis of three imputation methods to handle the missing value problem is presented. A framework that integrates ML model for AD's stages multiclass prediction is proposed, performing an average accuracy of 84%.

Keywords: alzheimer's disease, missing value, machine learning, performance evaluation

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