Search results for: language learning strategies

Authors: Afia Shahid

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The writing of Samuel Beckett is associated with meaning in the meaninglessness and the production of what he calls ‘literature of unword’. The casual escape from the world of words in the form of silences and pauses, in his play Waiting for Godot, urges to ask question of their existence and ultimately leads to investigate the theory behind their use in the play. This paper proposes that these absences (silence and pause) in Beckett’s play force to think ‘beyond’ language. This paper asks how silence and pause in Beckett’s text speak for the emergence of poststructuralist text. It aims to identify the significant features of the philosophy of deconstruction in the play of Beckett to demystify the hostile complicity between literature and philosophy. With the interpretive paradigm of poststructuralism this research focuses on the text as a research data. It attempts to delineate the relationship between poststructuralist theoretical concerns and text of Beckett. Keeping in view the theoretical concerns of Poststructuralist theorist Jacques Derrida, the main concern of the discussion is directed towards the notion of ‘beyond’ language into the absences that are aimed at silencing the existing discourse with the ‘radical irony’ of this anti-formal art that contains its own denial and thus represents the idea of ceaseless questioning and radical contradiction in art and any text. This article asks how text of Beckett vibrates with loud silence and has disrupted language to demonstrate the emptiness of words and thus exploring the limitless void of absences. Beckett’s text resonates with silence and pause that is neither negation nor affirmation rather a poststructuralist’s suspension of reality that is ever changing with the undecidablity of all meanings. Within the theoretical notion of Derrida’s Différance this study interprets silence and pause in Beckett’s art. The silence and pause behave like Derrida’s Différance and have questioned their own existence in the text to deconstruct any definiteness and finality of reality to extend an undecidable threshold of poststructuralists that aims to evade the ‘labyrinth of language’.

Keywords: Différance, language, pause, poststructuralism, silence, text

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Authors: Sibnath Sarkar

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Developing countries are facing many Social problems. In India, too there are several such problems. The problem of street children is one of them. No country or city anywhere in the world today is without the presence of street children, but the problem is most acute in developing countries. Thousands of street children can be seen in our populous cities like Mumbai, Kolkata, Delhi, and Chennai. Most of them are in the age group of 5-15 years. The number of street children is increasing gradually. Poverty, unemployment, rapid urbanization, rural-urban migrations are the root causes of street children. Being deprive from many of their, they have escaped to the street as a safe place for living. Street children always related with the urban spaces in the developing world and it represents a sad outcome of the rapid urbanization process. After coming to the streets, these children have to cope with the new situation every day. They also adopt or develop many complex survival strategies and a variety of different informal or even illegal activities in public space and form supportive social networks in order to survive in street life. Street children use the different suitable urban spaces as their earning, living, entertaining spot. Therefore, the livelihoods of young people on the street should analyze in relation to the spaces they use, as well as their age and length of stay on the streets. This paper tries to explore the livelihood strategies and copping situation of street children in Sealdah station area. One hundred seventy-five street living children are included in the study living in and around the railway station.

Keywords: strategies, street children, survive, urban-space

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: Marian Lissett Olaya

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This article examined the factors associated with incorporating peer assessment into English language classes in a public university in Colombia. This is done in the context of writing English class for 4th-semester students. The research instruments consisted of peer assessment questionnaires, student diaries, and interviews. Findings showed that among the factors, motivation, frustration, anxiety, and lack of confidence appeared. Data revealed that peer assessment enables students to write competencies through training, teachers' guidance, and the provision of a collaborative environment.

Keywords: writing skills, peer assessment, formative assessment, language acquisition

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Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

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Authors: R. W. A. V. A. Wijenayake, P. M. R. N. Fernando, S. Nilesh, M. D. G. M. S. Diddeniya, M. Weligodapola, P. Shamila

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The study is designed to examine the impact of a lifelong learning mindset on the career success of accounting and finance professionals in the western province of Sri Lanka. The learning mindset impacts the career success of accounting and finance professionals. The main objective of this study is to identify how the lifelong-learning mindset impacts on the career success of accounting and finance professionals. The lifelong learning mindset is the desire to learn new things and curiosity, resilience, and strategic thinking are the selected constructs to measure the lifelong learning mindset. Career success refers to certain objectives and emotional measures of improvement in one’s work life. The related variables of career success are measured through the number of promotions that have been granted in his/her work life. Positivism is the research paradigm, and the deductive approach is involved as this study relies on testing an existing theory. To conduct the study, the accounting and finance professionals in the western province in Sri Lanka were selected because most reputed international and local companies and specifically, headquarters of most of the companies are in western province. The responses cannot be collected from the whole population. Therefore, this study used a simple random sampling method, and the sample size was 120. Therefore, to identify the impact, 5-point Likert scale is used to perform this quantitative data. Required data gathered through an online questionnaire and the final outputs of the study will offer certain important recommendations to several parties such as universities, undergraduates, companies, and the policymakers to improve, help mentally and financially and motivate the students and the employees to continue their studies without ceasing after completion of their degree.

Keywords: career success, curiosity, lifelong learning mindset, resilience, strategic thinking

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Authors: Morakinyo Akintolu, Moeketsi Letseka

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In the year 2015, the United Nation member states, through the representative of all heads of states present, adopted the 17 Global goals known as the Sustainable Development Goals in their capacity to bring about social, economic, and cultural development to the world. Therefore, the need to accommodate equitable development one of the major goals is to achieve equitable and quality education for all to bring about international development. In this light, the study investigates the role of open distance learning in achieving sustainable development goals. Open distance learning comes as a second chance to individuals in disseminating educational content to students who missed the opportunity of attending the traditional school setting. Therefore, this study investigates if the SDGs reflect this type of learning (ODL) in creating Education for all according to the 2030 agenda by the United Nations. It further ascertains the role of ODL in achieving SDGs, the challenges encountered as well as the way forward.

Keywords: open distance learning, sustainable development goals, distance education, achieving, 2030 agenda

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Authors: Kathy Gibbs

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There is a paucity of research in Australia about educators’ use of differentiated instruction (DI) to support the learning of students with ADHD. This study reports on small-scale, qualitative research using interviews with teachers and school leaders to identify how they use DI as an effective teaching instruction for students with ADHD. Findings showed that teachers and school leaders have a good understanding of ADHD; teachers use DI as an effective teaching practice to enhance learning for this student group and ensure the classroom environment is safe and secure. However, they do not adjust assessments for students with ADHD. School leaders are not clear on how teachers differentiate assessments or adapt to the classroom environment. These results highlight the need for further research at the teacher and teacher-educator level teachers to ensure teaching practices are effective in reducing unwanted behaviours that prevent students with ADHD from achieving their full academic potential.

Keywords: teachers, differentiated instruction, ADHD, student learning, educators knowledge

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Authors: Shael Brown, Reza Farivar

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Persistence diagrams capture valuable topological features of datasets that other methods cannot uncover. Still, their adoption in data pipelines has been limited due to the lack of publicly available tools in R (and python) for analyzing groups of them with machine learning and statistical inference. In an easy-to-use and scalable R package called TDApplied, we implement several applied analysis methods tailored to groups of persistence diagrams. The two main contributions of our package are comprehensiveness (most functions do not have implementations elsewhere) and speed (shown through benchmarking against other R packages). We demonstrate applications of the tools on simulated data to illustrate how easily practical analyses of any dataset can be enhanced with topological information.

Keywords: machine learning, persistence diagrams, R, statistical inference

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Authors: T. Dubayova

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Slovak, as well as foreign authors, believe that the influence of non-cognitive factors on a student's academic success or failure is unquestionable. The aim of this paper is to establish a link between the mental vulnerability and coping strategies used by 4^th grade elementary school students in dealing with stressful situations and their academic performance, which was used as a simple quantitative indicator of academic success. The research sample consists of 320 students representing the standard population and 60 students with special education needs (SEN), who were assessed by the Strengths and Difficulties Questionnaire (SDQ) by their teachers and the Children’s Coping Strategies Checklist (CCSC-R1) filled in by themselves. Students with SEN recorded an extraordinarily high frequency of mental vulnerability (34.5 %) than students representing the standard population (7 %). The poorest academic performance of students with SEN was associated with the avoidance behavior displayed during stressful situations. Students of the standard population did not demonstrate this association. Students with SEN are more likely to display mental health problems than students of the standard population. This may be caused by the accumulation of and frequent exposure to situations that they perceive as stressful.

Keywords: coping, mental vulnerability, pupil with special education needs, school performance, school success

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Authors: Kousay Abid

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The aim of this paper is to better understand talent and talent management (TM) in small French companies as well as in medium-sized ones (SME). While previous empirical investigations have largely focused on multinationals and big companies and concentrated on the Anglo-Saxon context, we focus on the pressing need for implementing TM strategies and practices, not only on a new ground of SME but also within a new European context related to France and the French context. This study also aims at understanding strategies adopted by those firms as means to attract, retain, maintain and to develop talents. We contribute to TM issues by adopting a multilevel approach, holding the goal of reaching a global holistic vision of interactions between various levels while applying TM, to make it more and more familiar to us. A qualitative research methodology based on a multiple-case study design, bottomed firstly on a qualitative survey and secondly on two in-depth case study, both built on interviews, will be used in order to develop an ideal analysis for TM strategies and practices. The findings will be based on data collected from more than 15 French SMEs. Our theoretical contributions are the fruit of context considerations and the dynamic of multilevel approach. Theoretically, we attempt first to clarify how talents and TM are seen and defined in French SMEs and consequently to enrich the literature on TM in SMEs out of the Anglo-Saxon context. Moreover, we seek to understand how SMEs manage jointly their talents and their TM strategies by setting up this contextualized pilot study. As well, we focus on the systematic TM model issue from French SMEs. Our prior managerial goal is to shed light on the need for TM to achieve a better management of these organizations by directing leaders to better identify the talented people whom they hold at all levels. In addition, our TM systematic model strengthens our analysis grid as recommendations for CEO and Human Resource Development (HRD) to make them rethink about the companies’ HR business strategies. Therefore, our outputs present a multiple lever of action that should be taken into consideration while reviewing HR strategies and systems, as well as their impact beyond organizational boundaries.

Keywords: french context, multilevel approach, small and medium-sized enterprises, talent management

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Authors: Durvi Yogesh Vagani

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This research paper explores the effectiveness of Research-informed teaching and Case-based teaching in enhancing the retention and recall of memory during discussions among university students. Additionally, it investigates the impact of using Artificial Intelligence (AI) tools on the quality of research conducted by students and its correlation with better recollection. The study hypothesizes that Case-based teaching will lead to greater recall and storage of information. The research gap in the use of AI in educational settings, particularly with actual participants, is addressed by leveraging a multi-method approach. The hypothesis is that the use of AI, such as ChatGPT and Bard, would lead to better retention and recall of information. Before commencing the study, participants' attention levels and IQ were assessed using the Digit Span Test and the Wechsler Adult Intelligence Scale, respectively, to ensure comparability among participants. Subsequently, participants were divided into four conditions, each group receiving identical information presented in different formats based on their assigned condition. Following this, participants engaged in a group discussion on the given topic. Their responses were then evaluated against a checklist. Finally, participants completed a brief test to measure their recall ability after the discussion. Preliminary findings suggest that students who utilize AI tools for learning demonstrate improved grasping of information and are more likely to integrate relevant information into discussions compared to providing extraneous details. Furthermore, Case-based teaching fosters greater attention and recall during discussions, while Research-informed teaching leads to greater knowledge for application. By addressing the research gap in AI application in education, this study contributes to a deeper understanding of effective teaching methodologies and the role of technology in student learning outcomes. The implication of the present research is to tailor teaching methods based on the subject matter. Case-based teaching facilitates application-based teaching, and research-based teaching can be beneficial for theory-heavy topics. Integrating AI in education. Combining AI with research-based teaching may optimize instructional strategies and deepen learning experiences. This research suggests tailoring teaching methods in psychology based on subject matter. Case-based teaching suits practical subjects, facilitating application, while research-based teaching aids understanding of theory-heavy topics. Integrating AI in education could enhance learning outcomes, offering detailed information tailored to students' needs.

Keywords: artificial intelligence, attention, case-based teaching, memory recall, memory retention, research-informed teaching

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Authors: Omar Alshehri, Vic Lally

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This paper examined the attitudes of using social media tools to support learning at a university in Saudi Arabia. Moreover, it investigated the students’ current usage of these tools and examined the barriers they could face during the use of social media tools in the education process. Participants in this study were 42 university students. A web-based survey was used to collect data for this study. The results indicate that all of the students were familiar with social media and had used at least one type of social media for learning. It was found out that all students had very positive attitudes towards the use of social media and welcomed using these tools as a supplementary to the curriculum. However, the results indicated that the major barriers to using these tools in learning were distraction, opposing Islamic religious teachings, privacy issues, and cyberbullying. The study recommended that this study could be replicated at other Saudi universities to investigate factors and barriers that might affect Saudi students’ attitudes toward using social media to support learning.

Keywords: barriers to social media use, benefits of social media use, higher education, Saudi Arabia, social media

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Authors: Natasha Perez

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This autobiographical narrative inquiry explores the relationship between religious practice, identity, language and literacy in the author’s life experience as a second-generation Cuban-American growing up in the bilingual spaces of South Florida. The author describes how the social practices around language, including the flexibility to communicate in English and Spanish simultaneously, known as translanguaging, were instrumental to developing a biliterate cosmopolitan identity, along with a greater sense of Latinidad through interactions with diverse Latinx church members. This narrative study involved cycles of writing, reading, and reflection within a three-dimensional narrative inquiry space in order to discover the ways in which language and literacy development in the relationship between the personal and the social, across time and space, as historically situated phenomena. The findings show that Catholic faith practices have always been a source and expression of Cuban-ness, a means of sustaining Cuban identity, as well as a medium for bilingual language and literacy practice in the author’s life. Despite lacking formal literacy education in Spanish, she benefitted from the Catholic Church’s response to the surge of Spanish-speaking immigrants in South Florida in the 1980s and the subsequent flexibility of language practice in church-sponsored youth groups. The faith-sharing practices of the youth group created a space to use Spanish in more sophisticated ways that served to build confidence as a bilingual speaker and expand bilingual competence. These experiences also helped the author develop a more salient identity as Cuban-American and a deeper connection to her Cuban-ness in relation to the Nicaraguan, Venezuelan, and first-generation Cuban identities of my peers. The youth group also fostered cosmopolitan identity building through interactions with pan-ethnic Spanish speakers, with Catholicism as a common language and culture that served as a uniting force. Interaction with these peers also fostered cosmopolitan understandings that deepened the author’s knowledge of the geographical boundaries, political realities, and socio-historical differences between these groups of immigrants. This narrative study opens a window onto the micro-processes and socio-cultural dynamics of language and identity development in the second generation, with the potential to deepen our understanding of the impact of religious practice on these.

Keywords: literacy, religion, identity, comopolitanism, culture, language, translanguaging

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Authors: R. Nandhini, Gaurab Mudbhari

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Ranging from the field of health care to self-driving cars, machine learning and deep learning algorithms have revolutionized the field with the proper utilization of images and visual-oriented data. Segmentation, regression, classification, clustering, dimensionality reduction, etc., are some of the Machine Learning tasks that helped Machine Learning and Deep Learning models to become state-of-the-art models for the field where images are key datasets. Among these tasks, classification and clustering are essential but difficult because of the intricate and high-dimensional characteristics of image data. This finding examines and assesses advanced techniques in supervised classification and unsupervised clustering for image datasets, emphasizing the relative efficiency of Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Deep Embedded Clustering (DEC), and self-supervised learning approaches. Due to the distinctive structural attributes present in images, conventional methods often fail to effectively capture spatial patterns, resulting in the development of models that utilize more advanced architectures and attention mechanisms. In image classification, we investigated both CNNs and ViTs. One of the most promising models, which is very much known for its ability to detect spatial hierarchies, is CNN, and it serves as a core model in our study. On the other hand, ViT is another model that also serves as a core model, reflecting a modern classification method that uses a self-attention mechanism which makes them more robust as this self-attention mechanism allows them to lean global dependencies in images without relying on convolutional layers. This paper evaluates the performance of these two architectures based on accuracy, precision, recall, and F1-score across different image datasets, analyzing their appropriateness for various categories of images. In the domain of clustering, we assess DEC, Variational Autoencoders (VAEs), and conventional clustering techniques like k-means, which are used on embeddings derived from CNN models. DEC, a prominent model in the field of clustering, has gained the attention of many ML engineers because of its ability to combine feature learning and clustering into a single framework and its main goal is to improve clustering quality through better feature representation. VAEs, on the other hand, are pretty well known for using latent embeddings for grouping similar images without requiring for prior label by utilizing the probabilistic clustering method.

Keywords: machine learning, deep learning, image classification, image clustering

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Authors: Prasenjit Panda

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Jean Rhys’s Wide Sargasso Sea, a prequel to Bronte’s Jane Eyre, explores the history of the other and gives voices to the people who were silenced and kept under the darkness of negation and denial for a long time. Jean Wide Sargasso Sea provides an alternative understanding of Charlotte Brontë’s mad Creole woman, i.e., Bertha Mason of Jane Eyre in the postcolonial context. Rhys transforms Charlotte Bronte’s Victorian romance into a realistic narrative. In doing so, she re-reads Bertha as Antoinette, the unspeakable figure of otherness, into an unnameable self, and creates a new stage for the inner self. She in the novel is no longer a lunatic heiress in Rochester’s attic rather in this novel she finds her fantasy, dream and most importantly, voice. Rhys peeps through the character of Antoinette through her fragmented memories, dreams, and identity. Antoinette’s identity is mutilated constantly in the conflicts between colonizers and colonized, male and female, black and white. We shall use postcolonial theories like Bhaba’s hybridity and third space as a methodology to reveal the dialectics of struggle of a doubly colonized woman.We shall see that Bertha Mason was neglected by Bronte because of her madness and was locked in the Rochester’s Attic, but here Rhys beautifully converts her madness as a language of Antoinette, a language for her protest, a language for her liberation, a language for her dreams. In this present paper, we shall try to show how Antoinette tries to free her soul and body from the clutches of her multiple existences, identity, and narratives.

Keywords: colonizer, dislocation, fragmented memories, identity, narratives

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Authors: Irina Rönnqvist

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The world is constantly changing; therefore previous views or cultural patterns and programs formed by the “old world” cannot be suitable for solving actual problems. Indeed, reformation of an education system is unlikely to be effective without understanding of the processes that emerge in the field of employment. There is a problem in overcoming of the negative trends that determine imbalance of needs of the qualified work force and preparation of professionals by an education system. At the contemporary stage of economics the processes occurring in the field of labor and employment reproduce the picture of economic development of the country that cannot be imagined without the factor of labor mobility (e.g. migration). On the one hand, adult education has a significant impact on multifaceted development of economy. On the other hand, Sweden has one of the world's most generous asylum reception systems and the most liberal labor migration policy among the OECD countries. This effect affects the increased productivity. The focus of this essay is on problems of education and employment concerning social inclusion of migrants in working life in Sweden.

Keywords: migration, adaptation, formal learning, informal learning, Sweden

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Authors: Ashish James, Anusha James

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The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions.

Keywords: uncertainty estimation, neural networks, transfer learning, regression

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Authors: Yonatan Sverdlov, Shimon Ullman

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Artificial neural networks encounter a notable challenge known as continual learning, which involves acquiring knowledge of multiple tasks over an extended period. This challenge arises due to the tendency of previously learned weights to be adjusted to suit the objectives of new tasks, resulting in a phenomenon called catastrophic forgetting. Most approaches to this problem seek a balance between maximizing performance on the new tasks and minimizing the forgetting of previous tasks. In contrast, our approach attempts to maximize the performance of the new task, while ensuring zero forgetting. This is accomplished through the introduction of task-specific modulation parameters for each task, and only these parameters are learned for the new task, after a set of initial tasks have been learned. Through comprehensive experimental evaluations, our model demonstrates superior performance in acquiring and retaining novel tasks that pose difficulties for other multi-task models. This emphasizes the efficacy of our approach in preventing catastrophic forgetting while accommodating the acquisition of new tasks.

Keywords: continual learning, life-long learning, neural analogies, adaptive modulation

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Authors: Charlotte Brenner, Fisayo Latilo, McKenna Causey

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This study explores the transformation of teacher candidates' beliefs regarding inclusion and inclusive teaching practices during their instructional and practicum experiences in the Canadian context. With the increasing diversity of schools, the study investigates how teacher candidates' beliefs impact their implementation of inclusive teaching practices, which are essential for meeting diverse student needs. The research examines the influence of teacher education programs, transformative learning experiences, and inclusive practicum placements on teacher candidates' beliefs about inclusion. Using a multiple case study approach, the study assesses teacher candidates' initial beliefs, documents changes in these beliefs after coursework on inclusion, and explores the supports and constraints affecting belief development in both university and practicum settings. Preliminary findings suggest that teacher candidates generally hold positive beliefs about inclusion at the outset of their teacher education programs. However, coursework and practicum experiences significantly shape their understanding of diversity, strategies for inclusion, and awareness of broader social issues related to inclusive classrooms. The research underscores the critical role of teacher education programs in shaping teacher candidates' beliefs about inclusion and highlights the value of transformative learning experiences and inclusive practicum placements in enhancing their understanding of equity and inclusion. Continued research is necessary to identify specific elements within courses and practicum experiences that promote positive beliefs about inclusive teaching practices, ultimately contributing to the creation of more equitable classrooms and improved student outcomes.

Keywords: inclusion, beliefs, teacher candidates, inclusive teaching practices

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Authors: Peiming Li

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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

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Authors: Francisco A. Ribeiro Da Costa

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Who is responsible for teaching architecture; consider various ways to participate in learning, manipulating various pedagogical tools to streamline the creative process. The Architectural Design Studio (ADS) should become a holistic, systemic process responding to the complexity of our world. This essay corresponds to a deep reflection developed by the author on the teaching of architecture. The outcomes achieved are the corollary of experimentation; discussion and application of pedagogical methods that allowed consolidate the creativity applied by students. The purpose is to show the conjectures that have been considered effective in creating an intellectual environment that nurtures the subject of Architectural Design Studio (ADS), as an operational synthesis in the final stage of the degree. These assumptions, which are part of the proposed model, displaying theories and teaching methodologies that try to respect the learning process based on student learning styles Kolb, ensuring their latent specificities and formulating the structure of the ASD discipline. In addition, the assessing methods are proposed, which consider the architectural Design Studio as an operational synthesis in the teaching of architecture.

Keywords: teaching-learning, architectural design studio, architecture, education

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Authors: Munirah Alaboudi

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This paper discusses a research project carried out with 12 first-year graduate students enrolled in research methods course prior to undertaking a graduate thesis during the academic year 2019. The research was designed for the objective of creating research methods course structure that embraces an individualized and activity-based approach to learning in a highly engaging group environment. This approach targeted innovating the traditional research methods lecture-based, theoretical format where students reported less engagement and limited learning. This study utilized action research methodology in developing a different approach to research methods course instruction where student performance indicators and feedback were periodically collected to assess the new teaching method. Student learning was achieved through utilizing the flipped classroom approach where students learned the material at home and classroom activities were designed to implement and experiment with the newly acquired information, with the guidance of the course instructor. Student learning in class was practiced through a series of activities based on different research methods. With the goal of encouraging student engagement, a wide range of activities was utilized including workshops, role play, mind-mapping, presentations, peer evaluations. Data was collected through an open-ended qualitative questionnaire to establish whether students were engaged in the material they were learning, and to what degree were they engaged, and to test their mastery level of the concepts discussed. Analysis of the data presented positive results as around 91% of the students reported feeling more engaged with the active learning experience and learning research by “actually doing research, not just reading about it”. The students expressed feeling invested in the process of their learning as they saw their research “gradually come to life” through peer learning and practice during workshops. Based on the results of this study, the research methods course structure was successfully remodeled and continues to be delivered.

Keywords: research methods, higher education instruction, flipped classroom, graduate education

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Authors: Aishi Kundu, Elhan Raza

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"Necessity is the mother of invention" - Rapid increase in the global human population has directed the agricultural domain toward machine learning. The basic need of human beings is considered to be food which can be satisfied through farming. Farming is one of the major revenue generators for the Indian economy. Agriculture is not only considered a source of employment but also fulfils humans’ basic needs. So, agriculture is considered to be the source of employment and a pillar of the economy in developing countries like India. This paper provides a brief review of the progress made in implementing Machine Learning in the agricultural sector. Accurate predictions are necessary at the right time to boost production and to aid the timely and systematic distribution of agricultural commodities to make their availability in the market faster and more effective. This paper includes a thorough analysis of various machine learning algorithms applied in different aspects of agriculture (crop management, soil management, water management, yield tracking, livestock management, etc.).Due to climate changes, crop production is affected. Machine learning can analyse the changing patterns and come up with a suitable approach to minimize loss and maximize yield. Machine Learning algorithms/ models (regression, support vector machines, bayesian models, artificial neural networks, decision trees, etc.) are used in smart agriculture to analyze and predict specific outcomes which can be vital in increasing the productivity of the Agricultural Food Industry. It is to demonstrate vividly agricultural works under machine learning to sensor data. Machine Learning is the ongoing technology benefitting farmers to improve gains in agriculture and minimize losses. This paper discusses how the irrigation and farming management systems evolve in real-time efficiently. Artificial Intelligence (AI) enabled programs to emerge with rich apprehension for the support of farmers with an immense examination of data.

Keywords: machine Learning, artificial intelligence, crop management, precision farming, smart farming, pre-harvesting, harvesting, post-harvesting

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Authors: Renga Rao Krishnamoorthy, Raihan Tahir

Abstract:

The quality of classroom learning and teaching delivery has been and will continue to be debated at various levels worldwide. The regional cooperation programme to improve the quality and labour market orientation of the Technical and Vocational Education and Training (RECOTVET), ‘Deutsche Gesellschaft für Internationale Zusammenarbeit’ (GIZ), in line with the sustainable development goals (SDG), has taken the initiative in the development of quality TVET in the ASEAN region by developing the Quality Toolbox for Better TVET Delivery (Quality Toolbox). This initiative aims to provide quick and practical materials to trainers, instructors, and personnel involved in education and training at an institute to shape the quality of classroom learning and teaching. The Quality Toolbox for Better TVET Delivery was developed in three stages: literature review and development, validation, and finalization. Thematic areas in the Quality Toolbox were derived from collective input of concerns and challenges raised from experts’ workshops through moderated sessions involving representatives of TVET institutes from 9 ASEAN Member States (AMS). The sessions were facilitated by professional moderators and international experts. TVET practitioners representing AMS further analysed and discussed the structure of the Quality Toolbox and content of thematic areas and outlined a set of specific requirements and recommendations. The application exercise of the Quality Toolbox was carried out by TVET institutes among ASM. Experience sharing sessions from participating ASEAN countries were conducted virtually. The findings revealed that TVET institutes use two types of approaches in shaping the quality of learning and teaching, which is ascribed to inductive or deductive, shaping of quality in learning and teaching is a non-linear process and finally, Q-tools can be adopted and adapted to shape the quality of learning and teaching at TVET institutes in the following: improvement of the institutional quality, improvement of teaching quality and improvement on the organisation of learning and teaching for students and trainers. The Quality Toolbox has good potential to be used at education and training institutes to shape quality in learning and teaching.

Keywords: AMS, GIZ, RECOTVET, quality tools

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Authors: Mostafa ‘Neo’ Mohsenvand, Mohammad Rasool Izadi, Pattie Maes

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EEG-based biometrics (user identification) has been explored on small datasets of no more than 157 subjects. Here we show that the accuracy of modern supervised methods falls rapidly as the number of users increases to a few thousand. Moreover, supervised methods require a large amount of labeled data for training which limits their applications in real-world scenarios where acquiring data for training should not take more than a few minutes. We show that using contrastive learning for pre-training, it is possible to maintain high accuracy on a dataset of 2130 subjects while only using a fraction of labels. We compare 5 different self-supervised tasks for pre-training of the encoder where our proposed method achieves the accuracy of 96.4%, improving the baseline supervised models by 22.75% and the competing self-supervised model by 3.93%. We also study the effects of the length of the signal and the number of channels on the accuracy of the user-identification models. Our results reveal that signals from temporal and frontal channels contain more identifying features compared to other channels.

Keywords: brainprint, contrastive learning, electroencephalo-gram, self-supervised learning, user identification

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Authors: Hassan Mahdi

Abstract:

The integration of computer-assisted translation (CAT) tools in translation creates several opportunities for translators. However, this integration is not useful in all types of English structures. This study aims at examining the impact of using CAT tools in translating collocations. Seventy students of English as a foreign language participated in this study. The participants were divided into three groups (i.e., CAT tools group, Machine Translation group, and the control group). The comparison of the results obtained from the translation output of the three groups demonstrated the improvement of translation using CAT tools. The results indicated that the participants who used CAT tools outscored the participants who used MT, and in turn, both groups outscored the control group who did not use any type of technology in translation. In addition, there was a significant difference in the use of CAT for translation different types of collocations. The results also indicated that CAT tools were more effective in translation fixed and medium-strength collocations than weak collocations. Finally, the results showed that CAT tools were effective in translation collocations in both types of languages (i.e. target language or source language). The study suggests some guidelines for translators to use CAT tools.

Keywords: machine translation, computer-assisted translation, collocations, technology

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Authors: Hugh Taylor

Abstract:

Industry analysts project that edge computing will be generating tens of billions in revenue in coming years. It’s not clear, however, if this will actually happen, and who, if anyone, will make it happen. Edge computing is seen as a critical success factor in industries ranging from telecom, enterprise IT and co-location. However, will any of these industries actually step up to make edge computing into a viable technology business? This paper looks at why the edge seems to be in a chasm, on the edge of realization, so to speak, but failing to coalesce into a coherent technology category like the cloud—and how the segment’s divergent industry players can come together to build a viable business at the edge.

Keywords: edge computing, multi-edge strategies, edge data centers, edge cloud

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Authors: Victor Ademulegun Arijeniwa

Abstract:

This is a sociolinguistic study of Olu Obafemi’s Naira Has No Gender as a microcosm of life experiences. The paper assesses how Olu Obafemi’s use of language in the dramatic world serves as both social relationships and symbolic order of communicative roadmap that are capable of yielding well expressed and richly articulated sociolinguistic implications. Being the interface between language and social institutions, sociolinguistics and its application is highly utilitarian in linguistics analysis, especially where the language of a text appears to be deeply tensed, such as found in dramatic texts. The aim of this paper has been (i) to assess the symbolic orderly presentation of form in Olu Obafemi’Naira Has No Gender; (ii) to find out the linguistic elements and textual organization that represent social relationships in Olu Obafemi’s Naira Has No Gender. Using qualitative research design in data generation with insights from John Gumperz Interactional Sociolinguistics Theory with particular reference to contextualization cues and miscommunication, the paper identifies the implication of the dramatic discourse on society.

Keywords: sociolinguistics, Microcosm, contextualisation, miscommunication variable, identity, symbolic order

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Authors: Aneia Rajiel Busmente, Renato Gunio Jr., Jazin Mautante, Denise Joy Mendoza, Raymond Benedict Tagorio, Gabriel Uy, Natalie Quinn Valenzuela, Ma. Elayza Villa, Francine Yezha Vizcarra, Sofia Madelle Yapan, Eugene Kurt Yboa

Abstract:

COVID-19’s rapid spread caused a dramatic change in the nation, especially the educational system. The Department of Education was forced to adopt a practical learning platform without neglecting health, a printed modular distance learning. The Philippines' K–12 curriculum includes Statistics and Probability as one of the key courses as it offers students the knowledge to evaluate and comprehend data. Due to student’s difficulty and lack of understanding of the concepts of Statistics and Probability in Normal Distribution. The Self-Learning Module in Statistics and Probability about the Normal Distribution created by the Department of Education has several problems, including many activities, unclear illustrations, and insufficient examples of concepts which enables learners to have a difficulty accomplishing the module. The purpose of this study is to determine the effectiveness of self-learning module on the academic performance of students in the subject Statistics and Probability, it will also explore students’ perception towards the quality of created Self-Learning Module in Statistics and Probability. Despite the availability of Self-Learning Modules in Statistics and Probability in the Philippines, there are still few literatures that discuss its effectiveness in improving the performance of Senior High School students in Statistics and Probability. In this study, a Self-Learning Module on Normal Distribution is evaluated using a quasi-experimental design. STEM students in Grade 11 from National University's Nazareth School will be the study's participants, chosen by purposive sampling. Google Forms will be utilized to find at least 100 STEM students in Grade 11. The research instrument consists of 20-item pre- and post-test to assess participants' knowledge and performance regarding Normal Distribution, and a Likert scale survey to evaluate how the students perceived the self-learning module. Pre-test, post-test, and Likert scale surveys will be utilized to gather data, with Jeffreys' Amazing Statistics Program (JASP) software being used for analysis.

Keywords: self-learning module, academic performance, statistics and probability, normal distribution

Procedia PDF Downloads 116