Search results for: learning Maltese as a second language

Authors: Basel Halak, Christian Hall, Syed Abdul Father, Nelson Chow Wai Kit, Ruwaydah Widaad Raymode

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The attack surface on computing devices is becoming very sophisticated, driven by the sheer increase of interconnected devices, reaching 50B in 2025, which makes it easier for adversaries to have direct access and perform well-known physical attacks. The impact of increased security vulnerability of electronic systems is exacerbated for devices that are part of the critical infrastructure or those used in military applications, where the likelihood of being targeted is very high. This continuously evolving landscape of security threats calls for a new generation of defense methods that are equally effective and adaptive. This paper proposes an intelligent defense mechanism to protect from physical tampering, it consists of a tamper detection system enhanced with machine learning capabilities, which allows it to recognize normal operating conditions, classify known physical attacks and identify new types of malicious behaviors. A prototype of the proposed system has been implemented, and its functionality has been successfully verified for two types of normal operating conditions and further four forms of physical attacks. In addition, a systematic threat modeling analysis and security validation was carried out, which indicated the proposed solution provides better protection against including information leakage, loss of data, and disruption of operation.

Keywords: anti-tamper, hardware, machine learning, physical security, embedded devices, ioT

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Authors: Antonella Poce, Deborah Seid Howes, Maria Rosaria Re, Mara Valente

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The Creative Industries education contexts, Museum Education in particular, generally presents a low emphasis on the use of new digital technologies, digital abilities and transversal skills development. The spread of the Covid-19 pandemic has underlined the importance of these abilities and skills in cultural heritage education contexts: gaining digital skills, museum professionals will improve their career opportunities with access to new distribution markets through internet access and e-commerce, new entrepreneurial tools, or adding new forms of digital expression to their work. However, the use of web, mobile, social, and analytical tools is becoming more and more essential in the Heritage field, and museums, in particular, to face the challenges posed by the current worldwide health emergency. Recent studies highlight the need for stronger partnerships between the cultural and creative sectors, social partners and education and training providers in order to provide these sectors with the combination of skills needed for creative entrepreneurship in a rapidly changing environment. Considering the above conditions, the paper presents different examples of digital learning experiences carried out in Italian and USA contexts with the aim of promoting digital skills in museum professionals. In particular, a quali-quantitative research study has been conducted on two international Postgraduate courses, “Advanced Studies in Museum Education” (2 years) and “Museum Education” (1 year), in order to identify the educational effectiveness of the online learning strategies used (e.g., OBL, Digital Storytelling, peer evaluation) for the development of digital skills and the acquisition of specific content. More than 50 museum professionals participating in the mentioned educational pathways took part in the learning activity, providing evaluation data useful for research purposes.

Keywords: digital skills, museum professionals, technology, education

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Authors: Mary Luna Mancao Ninal

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This study aimed to determine the performance of the kindergarten teachers and its relation to pupils’ achievement in different learning areas in the Division of Kabankalan City. Using the standardized assessment and evaluation of the Department of Education secondary data, 100 kinder teachers and 2901 kinder pupils were investigated to determine the performance of the kindergarten teachers based on their Competency–Based Performance Appraisal System for Teachers and the periodic assessment of kinder pupils collected as secondary data. Weighted mean, Pearson–r, chi-square, Analysis of Variance were used in the study. Findings revealed that the kindergarten teacher respondents were 26-31 years old and most of them were female and married; they spent teaching for two years and less and passed the Licensure Examination for Teachers. They were very satisfactory as to instructional competences, school, and home and community involvement, personal, social, and professional characteristics. It also revealed that performance of the kindergarten pupils on their period of assessment shows that they were slightly advanced in their development. It also shows that domain as to performance of the kindergarten pupils were average overall development. Based on the results, it is recommended that Kindergarten teacher must augment their educational qualification and pursue their graduate studies and must develop the total personality of the children for them to achieve high advanced development to become productive individual.

Keywords: performance, kindergarten teacher, learning areas, professional, pupil

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Authors: Leon Pan

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The first programming course within post-secondary education has long been recognized as a challenging endeavor for both educators and students alike. Historically, these courses have exhibited high failure rates and a notable number of dropouts. Instructors often lament students' lack of effort in their coursework, and students often express frustration that the teaching methods employed are not effective. Drawing inspiration from the successful principles of Extreme Programming, this study introduces an approach—the Extremes-based teaching model — aimed at enhancing the teaching of introductory programming courses. To empirically determine the effectiveness of the model, a comparison was made between a section taught using the extreme-based model and another utilizing traditional teaching methods. Notably, the extreme-based teaching class required students to work collaboratively on projects while also demanding continuous assessment and performance enhancement within groups. This paper details the application of the extreme-based model within the post-secondary online classroom context and presents the compelling results that emphasize its effectiveness in advancing the teaching and learning experiences. The extreme-based model led to a significant increase of 13.46 points in the weighted total average and a commendable 10% reduction in the failure rate.

Keywords: extreme-based teaching model, innovative pedagogical methods, project-based learning, team-based learning

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Authors: Mariam Matiashvili

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Argumentation is an integral part of our daily communications - formal or informal. Argumentative reasoning, techniques, and language tools are used both in personal conversations and in the business environment. Verbalization of the opinions requires the use of extraordinary syntactic-pragmatic structural quantities - arguments that add credibility to the statement. The study of argumentative structures allows us to identify the linguistic features that make the text argumentative. Knowing what elements make up an argumentative text in a particular language helps the users of that language improve their skills. Also, natural language processing (NLP) has become especially relevant recently. In this context, one of the main emphases is on the computational processing of argumentative texts, which will enable the automatic recognition and analysis of large volumes of textual data. The research deals with the linguistic analysis of the argumentative structures of Georgian political speeches - particularly the linguistic structure, characteristics, and functions of the parts of the argumentative text - claims, support, and attack statements. The research aims to describe the linguistic cues that give the sentence a judgmental/controversial character and helps to identify reasoning parts of the argumentative text. The empirical data comes from the Georgian Political Corpus, particularly TV debates. Consequently, the texts are of a dialogical nature, representing a discussion between two or more people (most often between a journalist and a politician). The research uses the following approaches to identify and analyze the argumentative structures Lexical Classification & Analysis - Identify lexical items that are relevant in argumentative texts creating process - Creating the lexicon of argumentation (presents groups of words gathered from a semantic point of view); Grammatical Analysis and Classification - means grammatical analysis of the words and phrases identified based on the arguing lexicon. Argumentation Schemas - Describe and identify the Argumentation Schemes that are most likely used in Georgian Political Speeches. As a final step, we analyzed the relations between the above mentioned components. For example, If an identified argument scheme is “Argument from Analogy”, identified lexical items semantically express analogy too, and they are most likely adverbs in Georgian. As a result, we created the lexicon with the words that play a significant role in creating Georgian argumentative structures. Linguistic analysis has shown that verbs play a crucial role in creating argumentative structures.

Keywords: georgian, argumentation schemas, argumentation structures, argumentation lexicon

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Authors: Valerie Follonier, Henrike Hamelmann, Jean-Michel Jullien

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The Distance Learning University in Switzerland (UniDistance) is offering bachelor and master courses as well as further education programs. The professors and their assistants work at traditional Swiss universities and are giving their courses at UniDistance following a blended learning and flipped classroom approach. A standardized course evaluation by the students has been established as a component of a quality improvement process. The students’ feedback enables the stakeholders to identify areas of improvement, initiate professional development for the teaching teams and thus continuously augment the quality of instruction. This paper describes the evaluation process, the tools involved and how the approach involving all stakeholders helps forming a culture of quality in teaching. Additionally, it will present the first evaluation results following the new process. Two software tools have been developed to support all stakeholders in the process of the semi-annual formative evaluation. The first tool allows to create the survey and to assign it to the relevant courses and students. The second tool presents the results of the evaluation to the stakeholders, providing specific features for the teaching teams, the dean, the directorate and EDUDL+ (Educational development unit distance learning). The survey items were selected in accordance with the e-learning strategy of the institution and are formulated to support the professional development of the teaching teams. By reviewing the results the teaching teams become aware of the opinion of the students and are asked to write a feedback for the attention of their dean. The dean reviews the results of the faculty and writes a general report about the situation of the faculty and the possible improvements intended. Finally, EDUDL+ writes a final report summarising the evaluation results. A mechanism of adjustable warnings allows it to generate quality indicators for each module. These are summarised for each faculty and globally for the whole institution in order to increase the vigilance of the responsible. The quality process involves changing the indicators regularly to focus on different areas each semester, to facilitate the professional development of the teaching teams and to progressively augment the overall teaching quality of the institution.

Keywords: continuous improvement process, course evaluation, distance learning, software tools, teaching quality

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Authors: Meltem Eryılmaz

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With the emergence of the COVID-19 infection outbreak, the socio-cultural, political, economic, educational systems dynamics of the world have gone through a major change, especially in the educational field, specifically dentistry preclinical education, where the students must have a certain amount of real-time experience in endodontics and other various procedures. The totality of the digital and physical elements that make our five sense organs feel as if we really exist in a virtual world is called virtual reality. Virtual reality, which is very popular today, has started to be used in education. With the inclusion of developing technology in education and training environments, virtual learning platforms have been designed to enrich students' learning experiences. The field of health is also affected by these current developments, and the number of virtual reality applications developed for students studying dentistry is increasing day by day. The most widely used tools of this technology are virtual reality glasses. With virtual reality glasses, you can look any way you want in a world designed in 3D and navigate as you wish. With this project, solutions that will respond to different types of dental practices of students who study dentistry with virtual reality applications are produced. With this application, students who cannot find the opportunity to work with patients in distance education or who want to improve themselves at home have unlimited trial opportunities. Unity 2021, Visual Studio 2019, Cardboard SDK are used in the study.

Keywords: dentistry, intelligent tutoring system, virtual reality, online learning, COVID-19

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Authors: Taoufik Djennane

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Algeria is characterized by a fragmented language education policy. While pre-university education is entirely conducted in Arabic, higher education remains linguistically divided, with some fields offered in Arabic and others exclusively based on French. Within this linguistic policy, English remains far behind French. However, there has been a significant shift in the state’s linguistic orientation since the social riot of March 2019, known as El-Hirak, which ousted away the ex-president. Since then, social calls were voiced to get rid of French, and English started to receive an unprecedented political push. The historical decision only came at the beginning of the academic year 2023-2024 when the ministry of higher education imposed English as medium of instruction (hereafter EMI), especially in scientific and technological fields. As such, this paper considered this abrupt switch in the medium of instruction and its effects on the community of teachers. Building on a socio-psychological approach, teachers’ attitudes towards EMI were measured. Data were collected using classroom observation, semi-structured interviews and a survey. The results showed that a clear majority of teachers hold negative attitudes towards EMI. The point is that they are linguistically incompetent, and they are not ready yet to deliver content subjects in a language they have no, or little, command of. The study showed the importance of considering attitudes in the ‘policy-formation’ stage before the ‘implementation’ stage. The findings also proved that teachers are not passive bystanders; they can rather be the final arbiters imposing themselves as policy-makers resisting ministerial instructions through their linguistic practices inside the classroom which only acknowledge French. The study showed the necessity to avoid sudden switch and opt for gradual change, without putting aside those who are directly concerned with political/pedagogical measures (teachers, learners, etc).

Keywords: micro planning, EMI, language education policy, agency

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Authors: Khiari Nor El Houda, Hiouani Amira Sarra

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Many EFL students with high capacities are hidden because they suffer from speaking anxiety (SA). Most of them find public speaking much demanding. They feel unable to communicate, they fear to make mistakes and they fear negative evaluation or being called on. With the growing number of the learners who suffer from foreign language speaking anxiety (FLSA), it is becoming increasingly difficult to ignore its harmful outcomes on their performance and success, especially during their first contact with the pupils, as they will be teaching in the near future. Different researchers suggested different ways to minimize the negative effects of FLSA. The present study sheds light on emotional intelligence skills training as an effective strategy not only to influence public speaking success but also to help pre-service EFL teachers lessen their speaking anxiety and eventually to prepare them for their professional career. A quasi-experiment was used in order to examine the research hypothesis. We worked with two groups of third-year EFL students at Oum El Bouaghi University. The Foreign Language Classroom Anxiety Scale (FLCAS) and the Emotional Quotient Inventory (EQ-i) were used to collect data about the participants’ FLSA and EI levels. The analysis of the data has yielded that the assumption that there is a negative correlation between EI and FLSA was statistically validated by the Pearson Correlation Test, concluding that, the more emotionally intelligent the individual is the less anxious s/he will be. In addition, the lack of amelioration in the results of the control group and the noteworthy improvement in the experimental group results led us to conclude that EI skills training was an effective strategy in minimizing the FLSA level and therefore, we confirmed our research hypothesis.

Keywords: emotional intelligence, emotional intelligence skills training, EQ-I, FLCAS, foreign language speaking anxiety, pre-service EFL teachers

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Authors: Mahshid Asadollahi, Mohammad Akbari Asl

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The restaurant industry is highly competitive, and restaurants are constantly looking for ways to attract new customers and retain their existing ones. Nonverbal communication is an important factor in creating a positive customer experience and can play a significant role in attracting customers to restaurants. Nonverbal communication can include body language, facial expressions, tone of voice, and physical proximity, among other things. The present study aimed to investigate the impact of nonverbal communication between restaurant employees and customers on attracting customers in food courts in Tehran. The research method was descriptive-correlational, and the statistical population of this study included all customers of food court restaurants in Tehran, which was about 30 restaurants. The research sample was selected through probability sampling, and 440 customers completed emotional response, customer satisfaction, and nonverbal communication questionnaires in person. The data obtained were analyzed using multiple regression analysis. The results showed that vocal language, employee proximity, physical appearance, and speech movements, as components of nonverbal communication of restaurant employees, had an impact on attracting customers. Additionally, positive and negative emotions of customers have a significant relationship with customer attraction in Food Court restaurants. The study shows that various nonverbal communication factors can play a significant role in attracting customers, and that positive and negative customer emotions can affect customer satisfaction. Therefore, restaurant owners and managers should pay attention to nonverbal communication and train their employees accordingly to create a positive and welcoming atmosphere for customers.

Keywords: verbal language, proximity of employees, physical appearance, speech gestures, nonverbal communication, customer emotions, customer attraction

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Authors: Mohammed Saleh Lahmdi, Hassan Obeid Alfadly

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This study explores an optimality-theoretic account of Indian loanwords in Hadhrami Arabic (henceforth HA), a variety of Arabic spoken in Hadhramout Province in the coastal areas and Hadhramout Valley. The purpose of this paper is to find out how the phonological forms of Indian loanwords can be accounted for from an OT standpoint. To achieve this purpose, two main instruments were implemented: participant observation and interview. The sample of this study was selected carefully with certain characteristics by judgment sampling consisting of eleven informants. An ethnographic qualitative approach was employed to find out the phonological articulations that the researcher encountered during the implementation. Many phonological processes are used and several markedness and faithfulness constraints have been interacted in conflict in order to choose the optimal form of Hadhrami realisations. The findings of the study confirm that the Hadhrami syllable structure prevails over the donor language, i.e., the Indian (mainly Urdu) language. Specifically, markedness constraints dominate faithfulness ones when most of the Indian loanwords are incorporated into HA.

Keywords: linguistic borrowing, optimality theory, Hadhrami Arabic, loanword, phonological processes

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Authors: Digby Warren, Jiri Kropac

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This comparative study seeks to develop a deeper understanding of what is meant by “education for social justice” (ESJ) - an aspiration articulated by universities, though often without much definition. The research methodology involved thematic analysis of data from in-depth interviews with academics (voluntary participants) in different disciplines and institutions in the UK, Czech Republic and other EU countries. The interviews explored lecturers’ conceptions of ESJ, their practice of it, and associated challenges and enabling factors. Main findings are that ESJ is construed as provision of equitable and conscientising education opportunities that run across the whole higher education (HE) journey, from widening access to HE to stimulating critical learning and awareness that can empower graduates to transform their lives and societies. Teaching practice featured study of topics related to social justice; collaborative and creative learning activities, and assignments offering choice and connection to students’ realities. Student responses could be mixed, occasionally resistant, but mostly positive in terms of gaining increased confidence and awareness of equality and social responsibility. Influences at the macro, meso and mico level could support or limit scope for ESJ. Overall, the study highlights the strong, values-based commitment of HE teachers to facilitating student learning engagement, wellbeing and development towards building a better world.

Keywords: higher education, social justice, inclusivity, diversity

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Authors: Amit Prakash Singh, Shyamli Singh, Chavi Srivastav

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This paper presents the idea of Internet of Thing (IoT) for the infrastructure of smart cities. Internet of Thing has been visualized as a communication prototype that incorporates myriad of digital services. The various component of the smart cities shall be implemented using microprocessor, microcontroller, sensors for network communication and protocols. IoT enabled systems have been devised to support the smart city vision, of which aim is to exploit the currently available precocious communication technologies to support the value-added services for function of the city. Due to volume, variety, and velocity of data, it requires analysis using Big Data concept. This paper presented the various techniques used to analyze big data using machine learning.

Keywords: IoT, smart city, embedded systems, sustainable environment

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Authors: Ahmedou Moulaye Idriss, Tfeil Yahya, Tamas Ungi, Gabor Fichtinger

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Image-guided therapy (IGT) plays a crucial role in minimally invasive procedures for liver interventions. Accurate segmentation of the liver and precise needle placement is essential for successful interventions such as needle aspiration. In this study, we propose a deep learning-based liver 3D slicer designed to enhance segmentation accuracy and facilitate needle aspiration procedures. The developed 3D slicer leverages state-of-the-art convolutional neural networks (CNNs) for automatic liver segmentation in medical images. The CNN model is trained on a diverse dataset of liver images obtained from various imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). The trained model demonstrates robust performance in accurately delineating liver boundaries, even in cases with anatomical variations and pathological conditions. Furthermore, the 3D slicer integrates advanced image registration techniques to ensure accurate alignment of preoperative images with real-time interventional imaging. This alignment enhances the precision of needle placement during aspiration procedures, minimizing the risk of complications and improving overall intervention outcomes. To validate the efficacy of the proposed deep learning-based 3D slicer, a comprehensive evaluation is conducted using a dataset of clinical cases. Quantitative metrics, including the Dice similarity coefficient and Hausdorff distance, are employed to assess the accuracy of liver segmentation. Additionally, the performance of the 3D slicer in guiding needle aspiration procedures is evaluated through simulated and clinical interventions. Preliminary results demonstrate the effectiveness of the developed 3D slicer in achieving accurate liver segmentation and guiding needle aspiration procedures with high precision. The integration of deep learning techniques into the IGT workflow shows great promise for enhancing the efficiency and safety of liver interventions, ultimately contributing to improved patient outcomes.

Keywords: deep learning, liver segmentation, 3D slicer, image guided therapy, needle aspiration

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Authors: Behzad Behnia, Noah LaRussa-Trott

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In recent years, fibers have been successfully used as an additive to reinforce asphalt concrete materials and to enhance the sustainability and resiliency of transportation infrastructure. Roads covered with fiber-reinforced asphalt concrete (FRAC) require less frequent maintenance and tend to have a longer lifespan. The present work investigates the application of sasobit-coated aramid fibers in asphalt pavements and employs machine learning to develop prediction models to evaluate the cracking performance of FRAC materials. For the experimental part of the study, the effects of several important parameters such as fiber content, fiber length, and testing temperature on fracture characteristics of FRAC mixtures were thoroughly investigated. Two mechanical performance tests, i.e., the disk-shaped compact tension [DC(T)] and indirect tensile [ID(T)] strength tests, as well as the non-destructive acoustic emission test, were utilized to experimentally measure the cracking behavior of the FRAC material in both macro and micro level, respectively. The experimental results were used to train the supervised machine learning approach in order to establish prediction models for fracture performance of the FRAC mixtures in the field. Experimental results demonstrated that adding fibers improved the overall fracture performance of asphalt concrete materials by increasing their fracture energy, tensile strength and lowering their 'embrittlement temperature'. FRAC mixtures containing long-size fibers exhibited better cracking performance than regular-size fiber mixtures. The developed prediction models of this study could be easily employed by pavement engineers in the assessment of the FRAC pavements.

Keywords: fiber reinforced asphalt concrete, machine learning, cracking performance tests, prediction model

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Authors: Jocelyne Kenne Kenne

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Previous works in the field of sociolinguistics have explored the various outcomes of linguistic pluralism. One of these outcomes is the creation of new languages. The presentation will focus on one of such languages, Camfranglais, a hybrid language spoken by Cameroonians. It appeared in the 1970s in the francophone area in Cameroon and developed as a result of interactions between French, English, Cameroonian Pidgin English and local Cameroonian languages, all languages spoken in Cameroon. With the migration of Cameroonians to Europe, researches have been conducted to analyze the sociolinguistic profile of Cameroonians in their new environment. The emphasis on this presentation will be on two recent studies that have been conducted to analyze the peculiarity of Camfranglais in two European countries: Germany and Italy. The research involved 59 Cameroonians living in Italy and 49 Cameroonians residing in Germany. The respondents were composed of participants from different linguistic background, students and workers, married and single. A combination of quantitative and qualitative research methods was employed. The field study was divided into three parts. The first part was focused on observing the Cameroonians interact in different places such as in canteens, in the university halls of residence, lecture theatres, at homes, at various Cameroonian meetings. Those observations were accompanied by audio-recordings of the various interactions. The aim was to study communication between Cameroonians to see whether they use Camfranglais or not; if yes, in which domains and what were the speakers’ linguistic profiles. Additionally, questionnaires of different lengths were used to collect biographical information concerning the participants and their sociolinguistic profile and finally, in-depth interviews with Cameroonians were conducted to inquire about the use, the functions and the importance of this language in the migratory context. The results of the research demonstrate how a widespread use of Camfranglais by Cameroonians in Germany and Italy reveal a longing for home on the one hand and a sign of belonging on the other. It also shows the differences that exist between the profiles of Camfranglais speakers in Europe and the speakers in Cameroon notably in terms of age and social class. Finally, it points out some differences in the use, the structure and the functions of this hybrid language in the migratory setting. This study is a contribution to existing research in the field of contact languages and can serve as a comparison for other situations of multilingualism and the creation of mixed languages. Furthermore, with globalization, the study of migrant languages and the contact of these languages with new languages are topics that might be productive for further research in the field of sociolinguistics.

Keywords: interaction, migrants language, multilingualism, mixed languages

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Authors: Jade V. Rushby, Tony Loughland, Tracy L. Durksen, Hoa Nguyen, Robert M. Klassen

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This paper reports a study investigating the development and implementation of an online multi-session ‘scenario-based learning’ (SBL) program administered to preservice teachers in Australia. The transition from initial teacher education to the teaching profession can present numerous cognitive and psychological challenges for early career teachers. Therefore, the identification of additional supports, such as scenario-based learning, that can supplement existing teacher education programs may help preservice teachers to feel more confident and prepared for the realities and complexities of teaching. Scenario-based learning is grounded in situated learning theory which holds that learning is most powerful when it is embedded within its authentic context. SBL exposes participants to complex and realistic workplace situations in a supportive environment and has been used extensively to help prepare students in other professions, such as legal and medical education. However, comparatively limited attention has been paid to investigating the effects of SBL in teacher education. In the present study, the SBL intervention provided participants with the opportunity to virtually engage with school-based scenarios, reflect on how they might respond to a series of plausible response options, and receive real-time feedback from experienced educators. The development process involved several stages, including collaboration with experienced educators to determine the scenario content based on ‘critical incidents’ they had encountered during their teaching careers, the establishment of the scoring key, the development of the expert feedback, and an extensive review process to refine the program content. The 4-part SBL program focused on areas that can be challenging in the beginning stages of a teaching career, including managing student behaviour and workload, differentiating the curriculum, and building relationships with colleagues, parents, and the community. Results from prior studies implemented by the research group using a similar 4-part format have shown a statistically significant increase in preservice teachers’ self-efficacy and classroom readiness from the pre-test to the final post-test. In the current research, professional teaching identity - incorporating self-efficacy, motivation, self-image, satisfaction, and commitment to teaching - was measured over six weeks at multiple time points: before, during, and after the 4-part scenario-based learning program. Analyses included latent growth curve modelling to assess the trajectory of change in the outcome variables throughout the intervention. The paper outlines (1) the theoretical underpinnings of SBL, (2) the development of the SBL program and methodology, and (3) the results from the study, including the impact of the SBL program on aspects of participating preservice teachers’ professional identity. The study shows how SBL interventions can be implemented alongside the initial teacher education curriculum to help prepare preservice teachers for the transition from student to teacher.

Keywords: classroom simulations, e-learning, initial teacher education, preservice teachers, professional learning, professional teaching identity, scenario-based learning, teacher development

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Authors: Rochelle Elva

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Faculty in Higher Education today are faced with the challenge of convincing their students of the importance of learning and mastery of skills. This is because most students often have a single motivation -to get high grades. If it appears that this goal will not be met, they lose their motivation, and their academic efforts wane. This is true even for students in the competitive fields of STEM, including Computer Science majors. As educators, we have to understand our students and leverage what motivates them to achieve our learning outcomes. This paper presents a case study that utilizes cognitive psychology’s Expectancy Value Theory and Motivation Theory to investigate the effect of sustained expectancy for success on students’ learning outcomes. In our case study, we explore how students’ motivation and persistence in their academic efforts are impacted by providing them with an unexpected possible path to success that continues to the end of the semester. The approach was tested in an undergraduate computer science course with n = 56. The results of the study indicate that when presented with the real possibility of success, despite existing low grades, both low and high-scoring students persisted in their efforts to improve their performance. Their final grades were, on average, one place higher on the +/-letter grade scale, with some students scoring as high as three places above their predicted grade.

Keywords: expectancy for success and persistence, motivation and performance, computer science education, motivation and performance in computer science

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Authors: Georgi I. Petkov, Ivan I. Vankov, Yolina A. Petrova

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A structure-based model of category learning and categorization at different levels of abstraction is presented. The model compares different structures and expresses their similarity implicitly in the forms of mappings. Based on this similarity, the model can categorize different targets either as members of categories that it already has or creates new categories. The model is novel using two threshold parameters to evaluate the structural correspondence. If the similarity between two structures exceeds the higher threshold, a new sub-ordinate category is created. Vice versa, if the similarity does not exceed the higher threshold but does the lower one, the model creates a new category on higher level of abstraction.

Keywords: analogy-making, categorization, learning of categories, abstraction, hierarchical structure

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Authors: Jinwooung Kim, Jae-Hwan Jung, Seong-Jun Kim, Sung-Ah Kim

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Because the building envelope has a significant impact on the operation and maintenance stage of the building, designing the facade considering the performance can improve the performance of the building and lower the maintenance cost of the building. In general, however, optimizing two or more performance factors confronts the limits of time and computational tools. The optimization phase typically repeats infinitely until a series of processes that generate alternatives and analyze the generated alternatives achieve the desired performance. In particular, as complex geometry or precision increases, computational resources and time are prohibitive to find the required performance, so an optimization methodology is needed to deal with this. Instead of directly analyzing all the alternatives in the optimization process, applying experimental techniques (heuristic method) learned through experimentation and experience can reduce resource waste. This study proposes and verifies a method to optimize the double envelope of a building composed of a perforated panel using machine learning to the design geometry and quantitative performance. The proposed method is to achieve the required performance with fewer resources by supplementing the existing method which cannot calculate the complex shape of the perforated panel.

Keywords: building envelope, machine learning, perforated metal, multi-factor optimization, façade

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Authors: Tejush Badal, Sanaa Alwidian

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Class diagrams are useful tools within the Unified Modelling Language (UML) to model and visualize the relationships between, and properties of objects within a system. As a system evolves over time and space (e.g., products), a series of models with several commonalities and variabilities create what is known as a model family. In circumstances where there are several versions of a model, examining each model individually, becomes expensive in terms of computation resources. To avoid performing redundant operations, this paper proposes an approach for representing a family of class diagrams into Union Models to represent model families using a single generic model. The paper aims to analyze and reason about a family of class diagrams using union models as opposed to individual analysis of each member model in the family. The union algorithm provides a holistic view of the model family, where the latter cannot be otherwise obtained from an individual analysis approach, this in turn, enhances the analysis performed in terms of speeding up the time needed to analyze a family of models together as opposed to analyzing individual models, one model at a time.

Keywords: analysis, class diagram, model family, unified modeling language, union model

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Authors: Sushree Sangita Mohanty

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The pedagogic system, which is used to work with indigenous groups, who have equally different socio-economic, socio-cultural & multi-lingual conditions with differing cognitive capabilities, makes the education situation complex. As a result, educating the indigenous people became just the dissemination of facts and information, but advancement in knowledge and possibilities somewhere hides. This gap arises complexities due to the language barrier and the teachers from a conventional background of teaching practices are unable to understand or connect with the students in the schools. This paper presents the research work of the Mother Tongue Based Multilingual Education (MTB-MLE) project that has developed a creative pedagogic endeavor for the students of Kalinga Institute of Social Sciences (KISS) for facilitating Multilingual Education (MLE) teaching. KISS is a home for 25,000 indigenous children. The students enrolled here are from 62 different indigenous communities who speak around 24 different languages with geographical articulation. The book contents include concept, understanding languages, similitudes among languages, the need of mother tongue in teaching and learning, skill development (Listening-Speaking-Reading-Writing), teachers activities for teaching in multilingual schools, the process of teaching, training format of multilingual teaching and procedures for basic data collection regarding multilingual schools and classroom handle.

Keywords: indigenous, multi-lingual, pedagogic, teachers, teaching practices

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Authors: Lulu Liao, Huijing Chen, Yinan Zhao, Hongting Ning, Hui Feng

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Urinary tract infection rate is an important index of care quality in nursing homes. The aim of the study is to understand the nursing assistant's current knowledge and attitudes of urinary incontinence and to explore related stakeholders' viewpoint about urinary incontinence training. This explanatory sequential study used Knowledge, Practice, and Attitude Model (KAP) and Adult Learning Theories, as the conceptual framework. The researchers collected data from 509 nursing assistants in sixteen nursing homes in Hunan province in China. The questionnaire survey was to assess the knowledge and attitude of urinary incontinence of nursing assistants. On the basis of quantitative research and combined with focus group, training demands were identified, which nurse managers should adopt to improve nursing assistants’ professional practice ability in urinary incontinence. Most nursing assistants held the poor knowledge (14.0 ± 4.18) but had positive attitudes (35.5 ± 3.19) toward urinary incontinence. There was a significant positive correlation between urinary incontinence knowledge and nursing assistants' year of work and educational level, urinary incontinence attitude, and education level (p < 0.001). Despite a general awareness of the importance of prevention of urinary tract infections, not all nurse managers fully valued the training in urinary incontinence compared with daily care training. And the nursing assistants required simple education resources to equip them with skills to address problem about urinary incontinence. The variety of learning methods also highlighted the need for educational materials, and nursing assistants had shown a strong interest in online learning. Related education material should be developed to meet the learning need of nurse assistants and provide suitable training method for planned quality improvement in urinary incontinence.

Keywords: mixed methods, nursing assistants, nursing homes, urinary incontinence

Procedia PDF Downloads 142

Authors: Abdal-Hafeez Alhussein

Abstract:

Artificial Intelligence (AI) has become a transformative force in the field of information technologies, reshaping how data is processed, analyzed, and utilized across various domains. This paper explores the multifaceted applications of AI within information technology, focusing on three key areas: automation, scalability, and data-driven decision-making. We delve into how AI-powered automation is optimizing operational efficiency in IT infrastructures, from automated network management to self-healing systems that reduce downtime and enhance performance. Scalability, another critical aspect, is addressed through AI’s role in cloud computing and distributed systems, enabling the seamless handling of increasing data loads and user demands. Additionally, the paper highlights the use of AI in cybersecurity, where real-time threat detection and adaptive response mechanisms significantly improve resilience against sophisticated cyberattacks. In the realm of data analytics, AI models—especially machine learning and natural language processing—are driving innovation by enabling more precise predictions, automated insights extraction, and enhanced user experiences. The paper concludes with a discussion on the ethical implications of AI in information technologies, underscoring the importance of transparency, fairness, and responsible AI use. It also offers insights into future trends, emphasizing the potential of AI to further revolutionize the IT landscape by integrating with emerging technologies like quantum computing and IoT.

Keywords: artificial intelligence, information technology, automation, scalability

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Authors: Waleed Afandi

Abstract:

Artificial intelligence (AI) has rapidly transformed various sectors, including education. This paper explores the integration of AI in education, emphasizing its potential to revolutionize learning processes, enhance teaching methodologies, and personalize education. We examine the historical context of AI in education, current applications, and the potential challenges and ethical considerations associated with its implementation. By reviewing a wide range of literature, this study aims to provide a comprehensive understanding of how AI can be leveraged to improve educational outcomes and the future directions of AI-driven educational innovations. Additionally, the paper discusses the impact of AI on student engagement, teacher support, and administrative efficiency. Case studies highlighting successful AI applications in diverse educational settings are presented, showcasing the practical benefits and real-world implications. The analysis also addresses potential disparities in access to AI technologies and suggests strategies to ensure equitable implementation. Through a balanced examination of the promises and pitfalls of AI in education, this study seeks to inform educators, policymakers, and technologists about the optimal pathways for integrating AI to foster an inclusive, effective, and innovative educational environment.

Keywords: artificial intelligence, education, personalized learning, teaching methodologies, educational outcomes, AI applications, student engagement, teacher support, administrative efficiency, equity in education

Procedia PDF Downloads 41

Authors: Wen-Wei Chiang

Abstract:

In this study, the researcher developed a scale for use in measuring the degree to which high school students experience a state of flow. The researcher then verified its reliability and validity in an actual classroom setting. The ultimate objective was to identify feasible methods by which to promote the experience of a flow state among high school students engaged in the study of science. The nine indices identified in this study to assess the engagement of high school students focus primarily on the study of science-related topics; however, the principles on which they are based are applicable to a wide range of learning situations. Teachers must outline the goals of each lesson clearly and provide unambiguous feedback. They must also look for ways to make the lessons more fun and appealing.

Keywords: flow experience, positive psychology, questionnaire, science learning

Procedia PDF Downloads 122

Authors: Rohit Mittal, Bright Keswani, Amit Mithal

Abstract:

This paper provides a sense about the application of computational intelligence techniques used to develop computer games, especially car racing. For the deep sense and knowledge of artificial intelligence, this paper is divided into various sections that is optimization, imitation, innovation and combining approach of optimization and imitation. This paper is mainly concerned with combining approach which tells different aspects of using fitness measures and supervised learning techniques used to imitate aspects of behavior. The main achievement of this paper is based on modelling player behaviour and evolving new game content such as racing tracks as single car racing on single track.

Keywords: evolution algorithm, genetic, optimization, imitation, racing, innovation, gaming

Procedia PDF Downloads 650

Authors: Thrivikraman Aswathi, S. Advaith

Abstract:

As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

Procedia PDF Downloads 135

Authors: A. D. Tayal

Abstract:

The energy industry is undergoing significant disruption. This research outlines that, whilst challenging; this disruption is also an emerging opportunity for electricity utilities. One such opportunity is leveraging the developments in data analytics and machine learning. As the uptake of renewable energy technologies and complimentary control systems increases, electricity grids will likely transform towards dense microgrids with high penetration of renewable generation sources, rich in network and customer data, and linked through intelligent, wireless communications. Data digitisation and analytics have already impacted numerous industries, and its influence on the energy sector is growing, as computational capabilities increase to manage big data, and as machines develop algorithms to solve the energy challenges of the future. The objective of this paper is to address how far the uptake of renewable technologies can go given the constraints of existing grid infrastructure and provides a qualitative assessment of how higher levels of renewable energy penetration can be facilitated by incorporating even broader technological advances in the fields of data analytics and machine learning. Western Australia is used as a contextualised case study, given its abundance and diverse renewable resources (solar, wind, biomass, and wave) and isolated networks, making a high penetration of renewables a feasible target for policy makers over coming decades.

Keywords: data, innovation, renewable, solar

Procedia PDF Downloads 369

Authors: Aragorn Joaquin Pineda Dela Cruz

Abstract:

Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

Procedia PDF Downloads 105