Search results for: EFL learning/ teaching

Authors: Veisiwe Gasa

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Many higher institutions of education that offer Open Distance Learning (ODL) and e-Learning have opened their doors to accommodate prisoners who want to further their studies. The provision of education for prisoners in South Africa emanates from a number of reasons. The alarmingly high numbers of the prison population in South Africa has called for the government to provide desperate measures. It is on these premises that the provision of higher education in prison is recommended. Higher education is recommended because of the belief that it creates employability and thereby reduces recidivism. Using targeted sampling, 5 universities were required to elaborate on their awareness strategies, how they ensure that Distance Education is accessible to the prisoners and also the ways in which they cater to the needs of incarcerated students. The research findings reveal that there is so little that has been done by these particular institutions to cater for prisoners. This raises a concern and indicates a need to raise awareness of the value of higher and distance education among prisoners. It also calls for higher education institutions to make prisons aware of their course offerings.

Keywords: e-Learning, incarcerated students, open distance learning, recidivism

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Authors: Supriya H. S., Chandrakala B. M.

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Deep learning based models have been recently successful adoption for network traffic prediction. However, training a deep learning model for various prediction tasks is considered one of the critical tasks due to various reasons. This research work develops Multi-Layer Hybrid Network (MLHN) for network traffic prediction and analysis; MLHN comprises the three distinctive networks for handling the different inputs for custom feature extraction. Furthermore, an optimized and efficient parameter-tuning algorithm is introduced to enhance parameter learning. MLHN is evaluated considering the “Big Data Challenge” dataset considering the Mean Absolute Error, Root Mean Square Error and R^2as metrics; furthermore, MLHN efficiency is proved through comparison with a state-of-art approach.

Keywords: MLHN, network traffic prediction

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Authors: Bel G Raggad

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An e-Learning setting is a distributed computing environment where information resources can be connected to any public network. Public networks are very unsecure which can compromise the reliability of an e-Learning environment. This study is only concerned with the intrusion detection aspect of e-Learning security and how incident responses are planned. The literature reported great advances in intrusion detection system (ids) but neglected to study an important ids weakness: suspected events are detected but an intrusion is not determined because it is not defined in ids databases. We propose an incident response generator (DIRG) that produces incident responses when the working ids system suspects an event that does not correspond to a known intrusion. Data involved in intrusion detection when ample uncertainty is present is often not suitable to formal statistical models including Bayesian. We instead adopt Dempster and Shafer theory to process intrusion data for the unknown event. The DIRG engine transforms data into a belief structure using incident scenarios deduced by the security administrator. Belief values associated with various incident scenarios are then derived and evaluated to choose the most appropriate scenario for which an automatic incident response is generated. This article provides a numerical example demonstrating the working of the DIRG system.

Keywords: decision support system, distributed computing, e-Learning security, incident response, intrusion detection, security risk, statefull inspection

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Authors: Silvia Benvenuti, Alessandra Cardinali

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In recent years, for instance, in relation to the Covid 19 pandemic and the evidence of climate change, it is becoming quite clear that the development of a young kid into an adult citizen requires a solid scientific background. Citizens are required to exert logical thinking and know the methods of science in order to adapt, understand, and develop as persons. Mathematics sits at the core of these required skills: learning the axiomatic method is fundamental to understand how hard sciences work and helps in consolidating logical thinking, which will be useful for the entire life of a student. At the same time, research shows that the axiomatic study of geometry is a problematic topic for students, even for those with interest in mathematics. With this in mind, the main goals of the research work we will describe are: (1) to show whether non-Euclidean geometries can be a tool to allow students to consolidate the knowledge of Euclidean geometries by developing it in a critical way; (2) to promote the understanding of the modern axiomatic method in geometry; (3) to give students a new perspective on mathematics so that they can see it as a creative activity and a widely discussed topic with a historical background. One of the main issues related to the state-of-the-art in this topic is the shortage of experimental studies with students. For this reason, our aim is to show further experimental evidence of the potential benefits of teaching non-Euclidean geometries at high school, based on data collected from a study started in 2005 in the frame of the Italian National Piano Lauree Scientifiche, continued by a teacher training organized in September 2018, perfected in a pilot study that involved 77 high school students during the school years 2018-2019 and 2019-2020. and finally implemented through an experimental study conducted in 2020-21 with 87 high school students. Our study shows that there is potential for further research to challenge current conceptions of the school mathematics curriculum and of the capabilities of high school mathematics students.

Keywords: Non-Euclidean geometries, beliefs about mathematics, questionnaires, modern axiomatic method

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Authors: Abel Olayinka Ogbungbemi, I. A. Omunagbe, O. R. King, O. H. Akingbade

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This study examined the influence of environmental factors on the academic performance of students in Lagos State Polytechnic. One hundred and thirty-eight students (138) questionnaire was randomly administered among 2,600 students in the 6 departments in the school of environmental studies, Lagos state Polytechnic. The result of the study established that the school environment affects learning. Hence, improper maintenance of fixtures led to lower than average student’s performance. Based on this, the school should endeavour to sustain the school facilities and dull colour points should not be used for painting, interactions between teachers and students should be encouraged, and teachers should relate to all the students irrespective of their age, level of study, department of study and gender.

Keywords: environment, learning, responsiveness, school effect

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Authors: Anandkumar S. Purohit

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Introduction: Amblyopia is a serious condition resulting in monocular impairment of vision. Although traditional treatment improves vision, we attempted the results of perceptual learning in this study. Methods: The prospective cohort study included all patients with amblyopia who were subjected to perceptual learning. The presenting data on vision, stereopsis, and contrast sensitivity were documented in a pretested online format, and the pre‑ and post‑treatment information was compared using descriptive, cross‑tabulation, and comparative methods on SPSS 22. Results: The cohort consisted of 47 patients (23 females and 24 males) with a mean age of 14.11 ± 7.13 years. A significant improvement was detected in visual acuity after the PL sessions, and the median follow‑up period was 17 days. Stereopsis improved significantly in all age groups. Conclusion: PL with hand-eye coordination is an effective method for managing amblyopia. This approach can improve vision in all age groups.

Keywords: amblyopia, perceptual learning, hand-eye coordination, visual acuity, stereopsis, contrast sensitivity, ophthalmology

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Authors: Chadia Nasri

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Researches in the field of foreign language learning have indicated the importance of the mastery of the four language skills; speaking, listening, writing and reading. As far as writing is concerned, recent studies have shown that this skill is unavoidable for learning a second language successfully. Writing is characterized as a complex system not easy to achieve. Writing has been proved to be affected by a variety of factors, particularly psychological ones; anxiety, intrinsic motivation, aptitude, etc. Intrinsic motivation is said to be the most influential factors in the foreign language learning process and is considered as the key factor for success. To investigate these two aspects; writing and intrinsic motivation, and the positive correlation between them, our hypothesis is designed on the basis that the degree of learners’ intrinsic motivation helps in facilitating their engagement in the writing tasks. Two questionnaires, one for teachers and the other for students, have been carried out to check the validity of the research hypothesis. As for the teachers’ questionnaire, the results have indicated their awareness of the importance of intrinsic motivation in the learning process and the role it plays in the mastery of their students’ writing skill. In addition, teachers have mentioned various procedures aiming at raising their students’ intrinsic motivation to write. The students’ questionnaire, on the other hand, has investigated students’ reasons for learning a foreign language with regard to their attitudes towards writing as an important skill that they need to master. Their answers to the questionnaire together with the marks they got in the second term test they have had in the writing module have been compared to see whether students’ writing proficiency can be determined by the degree of their intrinsic motivation. The comparison of the collected data has shown the positive correlation between both aspects.

Keywords: foreign language learning, intrinsic motivation, motivation, writing proficiency

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Authors: Zeynep Guler

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Argumentation and the production of scientific arguments are essential components that are necessary for helping students become scientifically literate through engaging them in constructing and critiquing ideas. Incorporating argumentation into science classrooms is challenging and can be a long-term process for both students and teachers. Students have difficulty in engaging tasks that require them to craft arguments, evaluate them to seek weaknesses, and revise them. Teachers also struggle with facilitating argumentation when they have underdeveloped science practices, underdeveloped pedagogical knowledge for argumentation science teaching, or underdeveloped teaching practice with argumentation (or a combination of all three). Thus, there is a need to support teachers in developing pedagogy for science teaching as argumentation, planning and implementing teaching practice for facilitating argumentation and also in becoming more agentic in this regards. Looking specifically at the experience of agency within education, it is arguable that agency is necessary for teachers’ renegotiation of professional purposes and practices in the light of changing educational practices. This study investigated how science teachers develop pedagogy for argumentation both individually and with their colleagues and also how teachers become more agentic (or not) through the active engagement of their contexts-for-action that refer to this as an ecological understanding of agency in order to positively influence or change their practice and their students' engagement with argumentation over two academic years. Through educational design study, this study conducted with three secondary science teachers (key stage 3-year 7 students aged 11-12) in the UK to find out if similar or different patterns of developing pedagogy for argumentation and of becoming more agentic emerge as they engage in planning and implementing a cycle of activities during the practice of teaching science with argumentation. Data from video and audio-recording of classroom practice and open-ended interviews with the science teachers were analysed using content analysis. The findings indicated that all the science teachers perceived strong agency in their opportunities to develop and apply pedagogical practices within the classroom. The teachers were pro-actively shaping their practices and classroom contexts in ways that were over and above the amendments to their pedagogy. They demonstrated some outcomes in developing pedagogy for argumentation and becoming more agentic in their teaching in this regards as a result of the collaboration with their colleagues and researcher; some appeared more agentic than others. The role of the collaboration between their colleagues was seen crucial for the teachers’ practice in the schools: close collaboration and support from other teachers in planning and implementing new educational innovations were seen as crucial for the development of pedagogy and becoming more agentic in practice. They needed to understand the importance of scientific argumentation but also understand how it can be planned and integrated into classroom practice. They also perceived constraint emerged from their lack of competence and knowledge in posing appropriate questions to help the students engage in argumentation, providing support for the students' construction of oral and written arguments.

Keywords: argumentation, teacher professional development, teacher agency, students' construction of argument

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Authors: Verd Shomrom, Gilat Trabelsi

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The goal of the study was to examine the effects of Computerized Cognitive Training (CCT) with and without cognitive mediation on Executive Function (EF) (planning and self- regulation) and on psychological resiliency among adolescents with Attention Deficits Hyperactive Disorder (ADHD) with or without Learning Disabilities (LD). Adolescents diagnosed with Attention Deficit Disorder and / or Learning Disabilities have multidimensional impairments that result from neurological damage. This work explored the possibility of influencing cognitive aspects in the field of Executive Functions (specifically: patterns of planning and self-regulation) among adolescents with a diagnosis of Attention Deficit Disorder and / or Learning Disabilities who study for a 10-12 grades. 46 adolescents with ADHD and/or with LD were randomly applied to experimental and control groups. All the participants were tested (BRC- research version, Resiliency quaternaries) before and after the intervention: mediated/ non-mediated Computerized Cognitive Training (MINDRI). The results indicated significant effects of cognitive modification in the experimental group, between pre and post Phases, in comparison to control group, especially in self- regulation (BRC- research version, Resiliency quaternaries), and on process analysis of Computerized Cognitive Training (MINDRI). The main conclusion was that even short- term mediation synchronized with CCT could greatly enhance the performance of executive functions demands. Theoretical implications for the positive effects of MLE in combination with CCT indicate the ability for cognitive change. The practical implication is the awareness and understanding of efficient intervention processes to enhance EF, learning awareness, resiliency and self-esteem of adolescents in their academic and daily routine.

Keywords: attention deficits hyperactive disorder, computerized cognitive training, executive function, mediated learning experience, learning disabilities

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Authors: Ruiyuan Wang, Jing Han, Bruno Di Biase, Mark Antoniou

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The purpose of this study is to explore the relationship between working memory (WM) capacity and Chinese-English consecutive interpreting (CI) performance in interpreting learners with different study abroad experience (SAE). Such relationship is not well understood. This study also examines whether Chinese interpreting learners with SAE in English-speaking countries, demonstrate a better performance in inflectional morphology and agreement, notoriously unstable in Chinese speakers of English L2, in their interpreting output than learners without SAE. Fifty Chinese university students, majoring in Chinese-English Interpreting, were recruited in Australia (n=25) and China (n=25). The two groups matched in age, language proficiency, and interpreting training period. Study abroad (SA) group has been studying in an English-speaking country (Australia) for over 12 months, and none of the students recruited in China (the no study abroad = NSA group) had ever studied or lived in an English-speaking country. Data on language proficiency and training background were collected via a questionnaire. Lexical retrieval performance and working memory (WM) capacity data were collected experimentally, and finally, interpreting data was elicited via a direct CI task. Main results of the study show that WM significantly correlated with participants' CI performance independently of learning context. Moreover, SA outperformed NSA learners in terms of subject-verb number agreement. Apart from that, WM capacity was also found to correlate significantly with their morphosyntactic accuracy. This paper sheds some light on the relationship between study abroad, WM capacity, and CI performance. Exploring the effect of study abroad on interpreting trainees and how various important factors correlate may help interpreting educators bring forward more targeted teaching paradigms for participants with different learning experiences.

Keywords: study abroad experience, consecutive interpreting, working memory, inflectional agreement

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

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Authors: Reyhan Tekin Sitrava

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Teachers should have robust and comprehensive content knowledge for effective mathematics teaching. It was explained that content knowledge includes knowing the facts, truths, and concepts; explaining the reasons behind these facts, truths and concepts, and making relationship between the concepts and other disciplines. By virtue of its importance, it will be significant to explore teachers and prospective teachers’ content knowledge related to variety of topics in mathematics. From this point of view, the purpose of this study was to investigate prospective mathematics teachers’ content knowledge. Particularly, it was aimed to reveal the prospective teachers’ knowledge regarding the definition of limit and derivate. To achieve the purpose and to get in-depth understanding, a qualitative case study method was used. The data was collected from 34 prospective mathematics teachers through a questionnaire containing 2 questions. The first question required the prospective teachers to define the limit and the second one required to define the derivative. The data was analyzed using content analysis method. Based on the analysis of the data, although half of the prospective teachers (50%) could write the definition of the limit, nine prospective teachers (26.5%) could not define limit. However, eight prospective teachers’ definition was regarded as partially correct. On the other hand, twenty-seven prospective teachers (79.5%) could define derivative, but seven of them (20.5%) defined it partially. According to the findings, most of the prospective teachers have robust content knowledge on limit and derivative. This result is important because definitions have a virtual role in learning and teaching of mathematics. More specifically, definition is starting point to understand the meaning of a concept. From this point of view, prospective teachers should know the definitions of the concepts to be able to teach them correctly to the students. In addition, they should have knowledge about the relationship between limit and derivative so that they can explain these concepts conceptually. Otherwise, students may memorize the rules of calculating the derivative and the limit. In conclusion, the present study showed that most of the prospective mathematics teachers had enough knowledge about the definition of derivative and limit. However, the rest of them should learn their definition conceptually. The examples of correct, partially correct, and incorrect definition of both concepts will be presented and discussed based on participants’ statements. This study has some implications for instructors. Instructors should be careful about whether students learn the definition of these concepts or not. In order to this, the instructors may give prospective teachers opportunities to discuss the definition of these concepts and the relationship between the concepts.

Keywords: content knowledge, derivative, limit, prospective mathematics teachers

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Authors: T. Pastraporn, J. Panida, P. Gasamapong, N. Jintana

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The study of inclusive educational environment of exceptional and normal children at the regional centre for special education aimed to establish guidelines for creating an environment for inclusive education. Buildings utilization of thirty-five elementary schools providing inclusive educational program in Bangkok were analyzed to study the following aspects: 1) The environment of exceptional and normal students’ inclusive classes at the regional centre for special education 2) The patterns of the environment suited to the exceptional and normal students’ inclusive classes 3) Environmental management policies for the inclusive classes of exceptional and normal students. Information was gathered from surveys, observations, questionnaires, document analysis, interviews, and non-experimental research. The findings showed that the usable spaces in school buildings were designated to enhance the three kinds of social learning experience: 1) Support class control 2) Help developing students’ personality consisting of physical, verbal and emotional expressions that are socially accepted 3) Recognition and learning, which are needed for the increasing of learning experience, were caused by having an interaction with the environment. Thus, the school buildings’ space designation positively affected the environmental management of exceptional and normal students’ inclusive classes.

Keywords: learning environment, inclusive education, school buildings, exceptional and normal children

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Authors: Zhan Jie How

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With the heightened awareness of workplace learning and its impact on improving organizational performance and developing employee competence, governments and corporations around the world are forced to intensify their cooperation to establish national workplace development programmes to guide these corporations in fostering engaging and collaborative workplace learning cultures. This conceptual paper aims to conduct a comparative study of existing workplace development programmes for small and medium-sized enterprises (SMEs) in Europe and Singapore, focusing primarily on the Swedish Production Leap, Finnish TEKES Liideri Programme, and Singapore SkillsFuture SME Mentors Programme. The study carries out a systematic review of the three workplace development programmes to examine the roles of external mentors or coaches in influencing the design and implementation of workplace learning strategies and practices in SMEs. Organizational, personal and external factors that promote or inhibit effective workplace mentorship are also scrutinized, culminating in a critical comparison and evaluation of the strengths and weaknesses of the aforementioned programmes. Based on the findings from the review and analyses, a heuristic conceptual framework is developed to illustrate the complex interrelationships among external workplace development programmes, internal learning and development initiatives instituted by the organization’s higher management, and employees' continuous learning activities at the workplace. The framework also includes a set of guiding principles that can be used as the basis for internal mediation between the competing perspectives of mentors and mentees (employers and employees of the organization) regarding workplace learning conditions, practices and their intended impact on the organization. The conceptual study provides a theoretical blueprint for future empirical research on organizational workplace learning and the impact of government-initiated workplace development programmes.

Keywords: employee competence, mentorship, organizational performance, workplace development programme, workplace learning culture

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Authors: Wiem Zemzem, Moncef Tagina

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In this work, we are addressing the problem of an autonomous mobile robot navigating in a large, unknown and dynamic environment using reinforcement learning abilities. This problem is principally related to the exploration/exploitation dilemma, especially the need to find a solution letting the robot detect the environmental change and also learn in order to adapt to the new environmental form without ignoring knowledge already acquired. Firstly, a new action selection strategy, called ε-greedy-MPA (the ε-greedy policy favoring the most promising actions) is proposed. Unlike existing exploration/exploitation policies (EEPs) such as ε-greedy and Boltzmann, the new EEP doesn’t only rely on the information of the actual state but also uses those of the eventual next states. Secondly, as the environment is large, an exploration favoring least recently visited states is added to the proposed EEP in order to accelerate learning. Finally, various simulations with ball-catching problem have been conducted to evaluate the ε-greedy-MPA policy. The results of simulated experiments show that combining this policy with the Qlearning method is more effective and efficient compared with the ε-greedy policy in stationary environments and the utility-based reinforcement learning approach in non stationary environments.

Keywords: autonomous mobile robot, exploration/ exploitation policy, large, dynamic environment, reinforcement learning

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Authors: Yi Ding

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With the rapid development of educational technologies, higher education institutions are facing the challenge of creating an inclusive learning environment for students from diverse backgrounds. Academic libraries, the hubs of research, instruction, and innovation at higher educational institutions, are facing the same challenge. While academic librarians worldwide have been working hard to provide services for emerging information technology such as information literacy education, online learning support, and scholarly communication advocacy, the problem of digital exclusion remains a difficult one at higher education institutions. Information services provided by academic libraries can result in the digital exclusion of students from diverse backgrounds, such as students with various digital readiness levels, students with disabilities, as well as English-as-a-Second-Language learners. This research study shows how academic librarians can design digital learning objects that are cognizant of differences in learner traits and student profiles through the lens of design thinking. By demonstrating how the design process of digital learning objects can take into consideration users’ needs, experiences, and engagement with different technologies, this research study explains design principles of accessibility, connectivity, and scalability in creating inclusive digital learning objects as shown in various case studies. Equipped with the mindset and techniques to be mindful of diverse student learning traits and profiles when designing information services, academic libraries can improve the digital inclusion and ultimately student success at higher education institutions.

Keywords: academic librarians, digital inclusion, information services, digital learning objects, student success

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Authors: Şirin Küçük, Ayşe Kök, İsmail Şahin

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Mobile devices are very popular and useful tools for assisting people in daily life. With the advancement of mobile technologies, the issue of mobile learning has been widely investigated in education. Many researches consider that it is important to integrate pedagogical and technical strengths of mobile technology into learning environments. For this reason, the purpose of this research is to examine the effect of mobile technology use in education with meta-analysis method. Meta-analysis is a statistical technique which combines the findings of independent studies in a specific subject. In this respect, the articles will be examined by searching the databases for researches which are conducted between 2005 and 2014. It is expected that the results of this research will contribute to future research related to mobile technology use in education.

Keywords: mobile learning, meta-analysis, mobile technology, education

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Authors: Andrew Nowlan

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During the Coronavirus pandemic, collaborative online international learning (COIL) emerged as an alternative to overseas sojourns. However, now that face-to-face classes have resumed and students are studying abroad, the rationale for doing COIL is not always clear amongst educators and students. Also, the logistics of COIL become increasingly complicated when participants involved in a potential collaboration have different second language (L2) learning goals. In this paper, the researcher will report on a study involving two bilingual, cross-cultural COIL courses between students at a university in Japan and those studying in North America, from April to December, 2022. The students in Japan were enrolled in an intercultural communication class in their L2 of English, while the students in Canada and the United States were studying intermediate Japanese as their L2. Based on a qualitative survey and journaling data received from 31 students in Japan, and employing a transcendental phenomenological research design, the researcher will highlight the students’ essence of experience during COIL. Essentially, students benefited from the experience through improved communicative competences and increased knowledge of the target culture, even when the L2 learning goals between institutions differed. Students also reported that the COIL experience was effective in preparation for actual study abroad, as opposed to a replacement for it, which challenges the existing literature. Both educators and administrators will be exposed to the perceptions of Japanese university students towards COIL, which could be generalized to other higher education contexts, including those in Southeast Asia. Readers will also be exposed to ideas for developing more effective pre-departure study abroad programs and domestic intercultural curriculum through COIL, even when L2 learning goals may differ between participants.

Keywords: collaborative online international learning, study abroad, phenomenology, EdTech, intercultural communication

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Authors: Catherine Omole

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Examination malpractice includes acts that temper with collecting accurate results during the conduct of an examination, thereby giving undue advantage to a student over his colleagues. Even though examination malpractice has been a lingering problem, examinations may not be easy to do away with completely as it is an important feedback tool in the learning process with several other functions e.g for the purpose of selection, placement, certification and promotion. Examination malpractice has created a lot of problems such as a relying on a weak work force based on false assessment results. The question is why is this problem still persisting, despite measures that have been taken to curb this ugly trend over the years? This opinion paper has identified modifications that could help relieve the student of the examination stress and thus increase the student’s effort towards effective learning and discourage examination malpractice in the long run.

Keywords: assessment, examination malpractice, learning, science classroom

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Authors: Mohammad Alshehri, Fahd Alfarsi

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Efficient supply chains play an essential role in delivering humanitarian supplies and directly impact the success of public aid initiatives globally. Predicting the delivery status of these essential supplies in a timely manner is crucial. Therefore, this study explores the application of various machine learning approaches to predict whether humanitarian deliveries will be made on time, using a comprehensive case-study dataset provided by one of the largest international supplying organisations. We employed several machine learning methods such as regression-based (e.g., logistics regression), tree-based (e.g., decision trees) and ensemble-based (e.g., AdaBoost, XGBoost, and Gradient Boosting) to develop our predictive model. Our findings demonstrate that ensemble algorithms achieved promising results, with F1 scores ranging from 0.90 to 0.98. These high accuracy levels indicate the robustness of ensemble-learning techniques in forecasting delivery status, potentially enabling more proactive and efficient supply chain management in global aid initiatives. The implications of this study suggest that integrating advanced predictive analytics can significantly enhance the reliability of supply chains, ensuring the timely delivery of critical commodities to those in need.

Keywords: humanitarian aids, supply chains, machine learning, delivery status

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Authors: Maria Janet, Anita David, P. Vijayasamundeeswarimaria

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Introduction: The main purpose of this study is to compare the effectiveness of web-based and blended learning on Paediatric Basic Life Support on competency among undergraduate nursing students in selected nursing colleges in Chennai. Materials and methods: A descriptive pre-test and post-test study design were used for this study. Samples of 100 Fourth year B.Sc., nursing students at Sri Ramachandra Faculty of Nursing SRIHER, Chennai, 100 Fourth year B.Sc., nursing students at Apollo College of Nursing, Chennai, were selected by purposive sampling technique. The instrument used for data collection was Knowledge Questionnaire on Paediatric Basic Life Support (PBLS). It consists of 29 questions on the general expansion of Basic Life Support and Cardiopulmonary Resuscitation, Prerequisites of Basic Life Support, and Knowledge on Paediatric Basic Life Support in which each question has four multiple choices answers, each right answer carrying one mark and no negative scoring. This questionnaire was formed with reference to AHA 2020 (American Heart Association) revised guidelines. Results: After the post-test, in the web-based learning group, 58.8% of the students had an inadequate level of objective performance score, while 41.1% of them had an adequate level of objective performance score. In the blended learning group, 26.5% of the students had an inadequate level of an objective performance score, and 73.4% of the students had an adequate level of an objective performance score. There was an association between the post-test level of knowledge and the demographic variables of undergraduate nursing students undergoing blended learning. The age was significant at a p-value of 0.01, and the performance of BLS before was significant at a p-value of 0.05. The results show that there was a significant positive correlation between knowledge and objective performance score of undergraduate nursing students undergoing web-based learning on paediatric basic life support.

Keywords: basic life support, paediatric basic life support, web-based learning, blended learning

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Authors: Szymon Rutkowski, Tomasz Korbak

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We present a reinforcement-learning-based method of training neural machine translation models without parallel corpora. The standard encoder-decoder approach to machine translation suffers from two problems we aim to address. First, it needs parallel corpora, which are scarce, especially for low-resource languages. Second, it lacks psychological plausibility of learning procedure: learning a foreign language is about learning to communicate useful information, not merely learning to transduce from one language’s 'encoding' to another. We instead pose the problem of learning to translate as learning a policy in a communication game between two agents: the translator and the classifier. The classifier is trained beforehand on a natural language inference task (determining the entailment relation between a premise and a hypothesis) in the target language. The translator produces a sequence of actions that correspond to generating translations of both the hypothesis and premise, which are then passed to the classifier. The translator is rewarded for classifier’s performance on determining entailment between sentences translated by the translator to disciple’s native language. Translator’s performance thus reflects its ability to communicate useful information to the classifier. In effect, we train a machine translation model without the need for parallel corpora altogether. While similar reinforcement learning formulations for zero-shot translation were proposed before, there is a number of improvements we introduce. While prior research aimed at grounding the translation task in the physical world by evaluating agents on an image captioning task, we found that using a linguistic task is more sample-efficient. Natural language inference (also known as recognizing textual entailment) captures semantic properties of sentence pairs that are poorly correlated with semantic similarity, thus enforcing basic understanding of the role played by compositionality. It has been shown that models trained recognizing textual entailment produce high-quality general-purpose sentence embeddings transferrable to other tasks. We use stanford natural language inference (SNLI) dataset as well as its analogous datasets for French (XNLI) and Polish (CDSCorpus). Textual entailment corpora can be obtained relatively easily for any language, which makes our approach more extensible to low-resource languages than traditional approaches based on parallel corpora. We evaluated a number of reinforcement learning algorithms (including policy gradients and actor-critic) to solve the problem of translator’s policy optimization and found that our attempts yield some promising improvements over previous approaches to reinforcement-learning based zero-shot machine translation.

Keywords: agent-based language learning, low-resource translation, natural language inference, neural machine translation, reinforcement learning

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Authors: A.Halim Sahelan, Mohd Halid Abu, Jamaluddin Hashim, Zulisman Maksom, Mohd Afif Md Nasir

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This study aims to assess the students' needs for the tour planning e-guide. The study is developing on the contribution and importance of the Educational Tour Planning Guide (ETP) is a multimedia courseware as one of the effective methods in teaching and learning of environmental science among the students in primary schools of the Ministry of Education, Malaysia. It is to provide the student with knowledge and experience about tourism, environmental science activities and process. E-guide to ETP also hopes to strengthen the student understanding toward the subject learns in the tourism environmental science. In order to assess the student's needs on the e-Guide to Educational Tour Planning in Environmental Science, the study has produced a similar e-Guide to ETP in the form as a courseware to be tested during the study. The study has involved several steps in order to be completed. It is such as the formulation of the problem, the review of the literature, the formulation of the study methodology, the production of the e-Guide to ETP, field survey and finally the analyses and discussion made on the data gathered during the study. The survey has involved 100 respondents among the students in standard six primary schools in Kluang Johor. Through the findings, the study indicates that the currently tested product is acceptable among the students in learning environmental science as a guide to plan for the tour. The findings also show a slight difference between the respondents who are using the e-Guide to ETP, and those who are not on the basis of the e-Guide to ETP results. Due the important for the study, the researcher hopes to be having a fair discussion and excellence, recommendation for the development of the product of the current study. This report is written also important to provide a written reference for the future related study.

Keywords: the tour planning e-guide, the Educational Tour Planning Guide, environmental science, multimedia course ware

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Authors: Sobhy Fathy A. Hashesh

Abstract:

The study was carried out in the Elementary Classes of Andalus Private Schools, girls section using control and experimental groups formed by Random Assignment Strategy. The study aimed at investigating the effect of Al-Andalus Fused Curriculum (AFC) model of learning and the effect of separate subjects’ approach on the development of students’ conceptual learning and skills acquiring. The society of the study composed of Al-Andalus Private Schools, elementary school students, Girls Section (N=240), while the sample of the study composed of two randomly assigned groups (N=28) with one experimental group and one control group. The study followed the quantitative and qualitative approaches in collecting and analyzing data to investigate the study hypotheses. Results of the study revealed that there were significant statistical differences between students’ conceptual learning and skills acquiring for the favor of the experimental group. The study recommended applying this model on different educational variables and on other age groups to generate more data leading to more educational results for the favor of students’ learning outcomes.

Keywords: AFC, STEAM, lego education, Al-Andalus fused curriculum, mechatronics

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Authors: Brahim El Fadil

Abstract:

The nature of science as a solution (NOS) to life problems is well established in school activities the world over. However, this study reveals the lack of representation of the NOS in science textbooks used in Quebec Province. A content analysis method was adopted to analyze the NOS in relation to optics knowledge and teaching-learning activities in Grade 9 science and technology textbooks and Grade 11 physics textbooks. The selected textbooks were approved and authorized by the Provincial Ministry of Education. Our analysis points out that most of these editions provided a poor representation of NOS. None of them indicates that scientific knowledge is subject to change, even though the history of optics reveals evolutionary and revolutionary changes. Moreover, the analysis shows that textbooks place little emphasis on the discussion of scientific laws and theories. Few of them argue that scientific inquiries are required to gain a deep understanding of scientific concepts. Moreover, they rarely present empirical evidence to support their arguments.

Keywords: nature of science, history of optics, geometrical theory of optics, wave theory of optics

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Authors: Ruchika Malhotra, Megha Khanna

Abstract:

The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics

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Authors: Karthik Mittal

Abstract:

This study performs an unsupervised machine learning analysis to find clusters of related SNPs which highlight biological pathways that are important for the biological mechanisms of breast cancer. Studying genetic variations in isolation is illogical because these genetic variations are known to modulate protein production and function; the downstream effects of these modifications on biological outcomes are highly interconnected. After extracting the SNPs and their effect on different types of breast cancer using the MRBase library, two unsupervised machine learning clustering algorithms were implemented on the genetic variants: a k-means clustering algorithm and a hierarchical clustering algorithm; furthermore, principal component analysis was executed to visually represent the data. These algorithms specifically used the SNP’s beta value on the three different types of breast cancer tested in this project (estrogen-receptor positive breast cancer, estrogen-receptor negative breast cancer, and breast cancer in general) to perform this clustering. Two significant genetic pathways validated the clustering produced by this project: the MAPK signaling pathway and the connection between the BRCA2 gene and the ESR1 gene. This study provides the first proof of concept showing the importance of unsupervised machine learning in interpreting GWAS summary statistics.

Keywords: breast cancer, computational biology, unsupervised machine learning, k-means, PCA

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Authors: Ágoston Nagy

Abstract:

This paper introduces a method of using unsupervised machine learning to visualize the feature space of a dataset in 2D, in order to find most characteristic segments in the set. After dimension reduction, users can select clusters by manual drawing. Selected clusters are recorded into a data model that is used for later predictions, based on realtime data. Predictions are made with supervised learning, using Gesture Recognition Toolkit. The paper introduces two example applications: a semantic audio organizer for analyzing incoming sounds, and a gesture database organizer where gestural data (recorded by a Leap motion) is visualized for further manipulation.

Keywords: gesture recognition, machine learning, real-time interaction, visualization

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Authors: Tesfaye Mengistu

Abstract:

This thesis aims to investigate the effectiveness of Reinforcement Learning (RL) for Autonomous Energy Management solutions. The study explores the potential of Model Free RL approaches, such as Monte Carlo RL and Q-learning, to improve energy management by autonomously adjusting energy management strategies to maximize efficiency. The research investigates the implementation of RL algorithms for optimizing energy consumption in a single-agent environment. The focus is on developing a framework for the implementation of RL algorithms, highlighting the importance of RL for enabling autonomous systems to adapt quickly to changing conditions and make decisions based on previous experiences. Moreover, the paper proposes RL as a novel energy management solution to address nations' CO2 emission goals. Reinforcement learning algorithms are well-suited to solving problems with sequential decision-making patterns and can provide accurate and immediate outputs to ease the planning and decision-making process. This research provides insights into the challenges and opportunities of using RL for energy management solutions and recommends further studies to explore its full potential. In conclusion, this study provides valuable insights into how RL can be used to improve the efficiency of energy management systems and supports the use of RL as a promising approach for developing autonomous energy management solutions in residential buildings.

Keywords: artificial intelligence, reinforcement learning, monte carlo, energy management, CO2 emission

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Authors: Murad Almadani, Khalil Abu-Hantash, Xinyu Wang, Herbert Jelinek, Kinda Khalaf

Abstract:

Gait analysis is used by healthcare professionals as a tool to gain a better understanding of the movement impairment and track progress. In most circumstances, monitoring patients in their real-life environments with low-cost equipment such as cameras and wearable sensors is more important. Inertial sensors, on the other hand, cannot provide enough information on angular dynamics. This research offers a method for tracking 2D joint coordinates using cutting-edge vision algorithms and a single RGB camera. We provide an end-to-end comprehensive deep learning pipeline for marker-less gait parameter estimation, which, to our knowledge, has never been done before. To make our pipeline function in real-time for real-world applications, we leverage the AlphaPose human posture prediction model and a deep learning transformer. We tested our approach on the well-known GPJATK dataset, which produces promising results.

Keywords: gait analysis, human pose estimation, deep learning, real time gait estimation, AlphaPose, transformer

Procedia PDF Downloads 112