Search results for: problem-based learning approach

Authors: Phalaunnnaphat Siriwongs

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The classroom of the 21st century is an ever changing forum for new and innovative thoughts and ideas. With increasing technology and opportunity, students have rapid access to information that only decades ago would have taken weeks to obtain. Unfortunately, new techniques and technology are not the cure for the fundamental problems that have plagued the classroom ever since education was established. Class size has been an issue long debated in academia. While it is difficult to pin point an exact number, it is clear that in this case more does not mean better. By looking into the success and pitfalls of classroom size the true advantages of smaller classes will become clear. Previously, one class was comprised of 50 students. Being seventeen and eighteen- year- old students, sometimes it was quite difficult for them to stay focused. To help them understand and gain much knowledge, a researcher introduced “The Theory of Multiple Intelligence” and this, in fact, enabled students to learn according to their own learning preferences no matter how they were being taught. In this lesson, the researcher designed a cycle of learning activities involving all intelligences so that everyone had equal opportunities to learn.

Keywords: multiple intelligences, role play, performance assessment, formative assessment

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Authors: Mai Nguyen-Phuong-Mai

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This paper incorporates insight from various models, studies and disciplines to construct a framework called the Inverted Pyramid Model. It is argued that such a framework has several advantages: (1) it reduces the shortcomings of the problem-focused approach that dominates the mainstream theories of cross-cultural management. With contributing insight from neuroscience, it suggests that training in business cross-cultural awareness should start with potential synergy emerged from differences instead of the traditional approach that focuses on the liability of foreigners and negative consequences of cultural distance. (2) The framework supports a dynamic and holistic way of analyzing cultural diversity by analyzing four major cultural units (global, national, organizational and group culture). (3) The framework emphasizes the role of individuals –an aspect of culture that is often ignored or regarded as a non-issue in the traditional approach. It is based on the notion that people don’t do business with a country, but work (in)directly with a unique person. And it is at this individual level that culture is made, personally, dynamically, and contextually. Insight from neuroscience provides significant evidence that a person can develop a multicultural mind, confirm and contradict, follow and reshape a culture, even when (s)he was previously an outsider to this culture. With this insight, the paper proposes a revision of the old adage (Think global – Act local) and change it into Think global – Plan local – Act individual.

Keywords: static–dynamic paradigm, cultural diversity, multicultural mind, neuroscience

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Authors: Syauqi Asfiya R.

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This study examines the constraints faced by Islamic boarding school (Pesantren) in Indonesia in effectively addressing physical violence within their educational institutions. The vulnerability to violence in the education sector remains pervasive, including in Pesantren, primarily due to the residential nature of the boarding school system, which necessitates round-the-clock interaction among students from diverse backgrounds. Additionally, environmental factors, parenting styles, individual characteristics, and media influences further complicate the conditions within Pesantren. Numerous cases of physical violence have been reported, underscoring the need to identify the constraints of violence prevention strategies implemented by Pesantren. Adopting a case study approach, this research focuses on a Modern Pesantren in Tangerang and utilizes interviews conducted with 20 victims of violence to explore the aspects of Pesantren's violence prevention strategies that may have been overlooked. The findings indicate that many students face a dilemma when reporting the violence they experience, as the imposed sanctions often prove excessively severe and carry the risk of exacerbating the violence perpetrated by the offenders. Consequently, numerous victims choose to remain silent, thereby enabling the perpetuation of violence. Moreover, senior students (mudabbir) are prohibited from giving punishment, but there are still many who punish other students based on their personal moods. Furthermore, violence is also perpetrated by religious teachers (ustadz), despite their responsibility for addressing such issues. The evaluation process often follows a unidirectional approach wherein the santri have limited freedom compared to the Mudabbir or ustadz when it comes to providing feedback. Additionally, sentiment within specific student generations is reinforced due to the segregation of dormitories based on cohorts. Lastly, the absence of psychologists to address the trauma experienced by victims further exacerbates the situation. This research sheds light on the constraints faced by Pesantren in effectively preventing physical violence and emphasizes the importance of implementing comprehensive measures to create safer and nurturing learning environments within these institutions.

Keywords: physical violence, islam, boarding school, constraint

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Authors: Cristina-Maria Păcurar

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The paper presents an original Python-based application that outlines the advantages of combining some elementary notions of mathematics with the study of a programming language. The application support refers to some of the first lessons of analytic geometry, meaning conics and quadrics and their reduction to a standard form, as well as some related notions. The chosen programming language is Python, not only for its closer to an everyday language syntax – and therefore, enhanced readability – but also for its highly reusable code, which is of utmost importance for a mathematician that is accustomed to exploit already known and used problems to solve new ones. The purpose of this paper is, on one hand, to support the idea that one of the most appropriate means to initiate one into programming is throughout mathematics, and reciprocal, one of the most facile and handy ways to assimilate some basic knowledge in the study of mathematics is to apply them in a personal project. On the other hand, besides being a mean of learning both programming and analytic geometry, the application subject to this paper is itself a useful tool for it can be seen as an independent original Python package for analytic geometry.

Keywords: analytic geometry, conics, python, quadrics

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Authors: Ana Paula Soares, Alexandrina Lages, Helena Oliveira, Francisco-Javier Gutiérrez-Domínguez, Marisa Lousada

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From an early age, exposure to a spoken language allows us to implicitly capture the structure underlying the succession of the speech sounds in that language and to segment it into meaningful units (words). Statistical learning (SL), i.e., the ability to pick up patterns in the sensory environment even without intention or consciousness of doing it, is thus assumed to play a central role in the acquisition of the rule-governed aspects of language and possibly to lie behind the language difficulties exhibited by children with development language disorder (DLD). The research conducted so far has, however, led to inconsistent results, which might stem from the behavioral tasks used to test SL. In a classic SL experiment, participants are first exposed to a continuous stream (e.g., syllables) in which, unbeknownst to the participants, stimuli are grouped into triplets that always appear together in the stream (e.g., ‘tokibu’, ‘tipolu’), with no pauses between each other (e.g., ‘tokibutipolugopilatokibu’) and without any information regarding the task or the stimuli. Following exposure, SL is assessed by asking participants to discriminate between triplets previously presented (‘tokibu’) from new sequences never presented together during exposure (‘kipopi’), i.e., to perform a two-alternative-forced-choice (2-AFC) task. Despite the widespread use of the 2-AFC to test SL, it has come under increasing criticism as it is an offline post-learning task that only assesses the result of the learning that had occurred during the previous exposure phase and that might be affected by other factors beyond the computation of regularities embedded in the input, typically the likelihood two syllables occurring together, a statistic known as transitional probability (TP). One solution to overcome these limitations is to assess SL as exposure to the stream unfolds using online techniques such as event-related potentials (ERP) that is highly sensitive to the time-course of the learning in the brain. Here we collected ERPs to examine the neurofunctional correlates of SL in preschool children with DLD, and chronological-age typical language development (TLD) controls who were exposed to an auditory stream in which eight three-syllable nonsense words, four of which presenting high-TPs and the other four low-TPs, to further analyze whether the ability of DLD and TLD children to extract-word-like units from the steam was modulated by words’ predictability. Moreover, to ascertain if the previous knowledge of the to-be-learned-regularities affected the neural responses to high- and low-TP words, children performed the auditory SL task, firstly, under implicit, and, subsequently, under explicit conditions. Although behavioral evidence of SL was not obtained in either group, the neural responses elicited during the exposure phases of the SL tasks differentiated children with DLD from children with TLD. Specifically, the results indicated that only children from the TDL group showed neural evidence of SL, particularly in the SL task performed under explicit conditions, firstly, for the low-TP, and, subsequently, for the high-TP ‘words’. Taken together, these findings support the view that children with DLD showed deficits in the extraction of the regularities embedded in the auditory input which might underlie the language difficulties.

Keywords: development language disorder, statistical learning, transitional probabilities, word segmentation

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Authors: Bhekisipho Twala, Mantepu Masetshaba, Ramapulana Nkoana

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Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfill requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.

Keywords: single classifier, ensemble learning, multi-target tracking, multiple classifiers

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Authors: Hyun-Woo Cho

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Detection of incipient abnormal events in production processes is important to improve safety and reliability of manufacturing operations and reduce losses caused by failures. The construction of calibration models for predicting faulty conditions is quite essential in making decisions on when to perform preventive maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of process measurement data. The calibration model is used to predict faulty conditions from historical reference data. This approach utilizes variable selection techniques, and the predictive performance of several prediction methods are evaluated using real data. The results shows that the calibration model based on supervised probabilistic model yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: calibration model, monitoring, quality improvement, feature selection

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Authors: Jia-Lien Hsu, Chiung-Wen Chang

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Research articles may follow a simple and succinct structure of organizational patterns, called move. For example, considering extended abstracts, we observe that an extended abstract usually consists of five moves, including Background, Aim, Method, Results, and Conclusion. As another example, when publishing articles in PubMed, authors are encouraged to provide a structured abstract, which is an abstract with distinct and labeled sections (e.g., Introduction, Methods, Results, Discussions) for rapid comprehension. This paper introduces a method for computational analysis of move structures (i.e., Background-Purpose-Method-Result-Conclusion) in abstracts and introductions of research documents, instead of manually time-consuming and labor-intensive analysis process. In our approach, sentences in a given abstract and introduction are automatically analyzed and labeled with a specific move (i.e., B-P-M-R-C in this paper) to reveal various rhetorical status. As a result, it is expected that the automatic analytical tool for move structures will facilitate non-native speakers or novice writers to be aware of appropriate move structures and internalize relevant knowledge to improve their writing. In this paper, we propose a Bayesian approach to determine move tags for research articles. The approach consists of two phases, training phase and testing phase. In the training phase, we build a Bayesian model based on a couple of given initial patterns and the corpus, a subset of CiteSeerX. In the beginning, the priori probability of Bayesian model solely relies on initial patterns. Subsequently, with respect to the corpus, we process each document one by one: extract features, determine tags, and update the Bayesian model iteratively. In the testing phase, we compare our results with tags which are manually assigned by the experts. In our experiments, the promising accuracy of the proposed approach reaches 56%.

Keywords: academic English writing, assisted writing, move tag analysis, Bayesian approach

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Authors: Weichao Yu, Kai Wen, Weihe Huang, Yang Yang, Jing Gong

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Metal-loss corrosion is a major threat to the safety and integrity of gas pipelines as it may result in the burst failures which can cause severe consequences that may include enormous economic losses as well as the personnel casualties. Therefore, it is important to ensure the corroding pipeline integrity and efficiency, considering the value of wall thickness, which plays an important role in the failure probability of corroding pipeline. Actually, the wall thickness is controlled during pipe purchase stage. For example, the API_SPEC_5L standard regulates the allowable tolerance of the wall thickness from the specified value during the pipe purchase. The allowable wall thickness tolerance will be used to determine the wall thickness distribution characteristic such as the mean value, standard deviation and distribution. Taking the uncertainties of the input variables in the burst limit-state function into account, the reliability approach rather than the deterministic approach will be used to evaluate the failure probability. Moreover, the cost of pipe purchase will be influenced by the allowable wall thickness tolerance. More strict control of the wall thickness usually corresponds to a higher pipe purchase cost. Therefore changing the wall thickness tolerance will vary both the probability of a burst failure and the cost of the pipe. This paper describes an approach to optimize the wall thickness tolerance considering both the safety and economy of corroding pipelines. In this paper, the corrosion burst limit-state function in Annex O of CSAZ662-7 is employed to evaluate the failure probability using the Monte Carlo simulation technique. By changing the allowable wall thickness tolerance, the parameters of the wall thickness distribution in the limit-state function will be changed. Using the reliability approach, the corresponding variations in the burst failure probability will be shown. On the other hand, changing the wall thickness tolerance will lead to a change in cost in pipe purchase. Using the variation of the failure probability and pipe cost caused by changing wall thickness tolerance specification, the optimal allowable tolerance can be obtained, and used to define pipe purchase specifications.

Keywords: allowable tolerance, corroding pipeline segment, operation cost, production cost, reliability approach

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Authors: Asimina Paparrigopoulou, John Pavlopoulos, Maria Konstantinidou

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Dating papyri accurately is crucial not only to editing their texts but also for our understanding of palaeography and the history of writing, ancient scholarship, material culture, networks in antiquity, etc. Most ancient manuscripts offer little evidence regarding the time of their production, forcing papyrologists to date them on palaeographical grounds, a method often criticized for its subjectivity. By experimenting with data obtained from the Collaborative Database of Dateable Greek Bookhands and the PapPal online collections of objectively dated Greek papyri, this study shows that deep learning dating models, pre-trained on generic images, can achieve accurate chronological estimates for a test subset (67,97% accuracy for book hands and 55,25% for documents). To compare the estimates of these models with those of humans, experts were asked to complete a questionnaire with samples of literary and documentary hands that had to be sorted chronologically by century. The same samples were dated by the models in question. The results are presented and analysed.

Keywords: image classification, papyri images, dating

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Authors: Nirmani Wijenayake, Louise Lutze-Mann, Lucy Jo, John Wilson, Vivian Yeung, Dean Lovett, Kim Snepvangers

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Sessional academic staff at universities have the most influence and impact on student learning, engagement, and experience as they have the most direct contact with undergraduate students. A blended technology-enhanced program was created for the development and support of sessional staff to ensure adequate training is provided to deliver quality educational outcomes for the students. This program combines innovative mixed media educational modules, a peer-driven support forum, and face-to-face workshops to provide a comprehensive training and support package for staff. Additionally, the program encourages the development of learning communities and peer mentoring among the sessional staff to enhance their support system. In 2018, the program was piloted on 100 sessional staff in the School of Biotechnology and Biomolecular Sciences to evaluate the effectiveness of this model. As part of the program, rotoscope animations were developed to showcase ‘typical’ interactions between staff and students. These were designed around communication, confidence building, consistency in grading, feedback, diversity awareness, and mental health and wellbeing. When surveyed, 86% of sessional staff found these animations to be helpful in their teaching. An online platform (Moodle) was set up to disseminate educational resources and teaching tips, to host a discussion forum for peer-to-peer communication and to increase critical thinking and problem-solving skills through scenario-based lessons. The learning analytics from these lessons were essential in identifying difficulties faced by sessional staff to further develop supporting workshops to improve outcomes related to teaching. The face-to-face professional development workshops were run by expert guest speakers on topics such as cultural diversity, stress and anxiety, LGBTIQ and student engagement. All the attendees of the workshops found them to be useful and 88% said they felt these workshops increase interaction with their peers and built a sense of community. The final component of the program was to use an adaptive e-learning platform to gather feedback from the students on sessional staff teaching twice during the semester. The initial feedback provides sessional staff with enough time to reflect on their teaching and adjust their performance if necessary, to improve the student experience. The feedback from students and the sessional staff on this model has been extremely positive. The training equips the sessional staff with knowledge and insights which can provide students with an exceptional learning environment. This program is designed in a flexible and scalable manner so that other faculties or institutions could adapt components for their own training. It is anticipated that the training and support would help to build the next generation of educators who will directly impact the educational experience of students.

Keywords: designing effective instruction, enhancing student learning, implementing effective strategies, professional development

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Authors: Yasser F. Hassan

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Social networking involves use of an online platform or website that enables people to communicate, usually for a social purpose, through a variety of services, most of which are web-based and offer opportunities for people to interact over the internet, e.g. via e-mail and ‘instant messaging’, by analyzing the voting behavior and ratings of judges in a popular comments in social networks. While most of the party literature omits the electorate, this paper presents a model where elites and parties are emergent consequences of the behavior and preferences of voters. The research in artificial intelligence and psychology has provided powerful illustrations of the way in which the emergence of intelligent behavior depends on the development of representational structure. As opposed to the classical voting system (one person – one decision – one vote) a new voting system is designed where agents with opposed preferences are endowed with a given number of votes to freely distribute them among some issues. The paper uses ideas from machine learning, artificial intelligence and soft computing to provide a model of the development of voting system response in a simulated agent. The modeled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structure. We employ agent-based computer simulation to demonstrate the formation and interaction of coalitions that arise from individual voter preferences. We are interested in coordinating the local behavior of individual agents to provide an appropriate system-level behavior.

Keywords: voting system, rough sets, multi-agent, social networks, emergence, power indices

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Authors: Amal M. Alrayes, Hayat M. Ali

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Since the beginning of distance education with the rapid evolution of technology, the social network plays a vital role in the educational process to enforce the interaction been the learners and teachers. There are many Web 2.0 technologies, services and tools designed for educational purposes. This research aims to investigate instructors’ acceptance towards web-based learning systems in higher educational institutes in Kingdom of Bahrain. Questionnaire is used to investigate the instructors’ usage of Web 2.0 and the factors affecting their acceptance. The results confirm that instructors had high accessibility to such technologies. However, patterns of use were complex. Whilst most expressed interest in using online technologies to support learning activities, learners seemed cautious about other values associated with web-based system, such as the shared construction of knowledge in a public format. The research concludes that there are main factors that affect instructors’ adoption which are security, performance expectation, perceived benefits, subjective norm, and perceived usefulness.

Keywords: Web 2.0, higher education, acceptance, students' perception

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Authors: Asma Bettahar, Imed Eddine Nezli, Sameh Habes

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Groundwater resources in the Oued Righ valley are represented like the parts of the eastern basin of the Algerian Sahara, superposed by two major aquifers: the Intercalary Continental (IC) and the Terminal Complex (TC). From a qualitative point of view, various studies have highlighted that the waters of this region showed excessive mineralization, including the waters of the terminal complex (EC Avg equal 5854.61 S/cm) .The present article is a statistical approach by two multi methods various complementary (ACP, CAH), applied to the analytical data of multilayered aquifer waters Terminal Complex of the Oued Righ valley. The approach is to establish a correlation between the chemical composition of water and the lithological nature of different aquifer levels formations, and predict possible connection between groundwater’s layers. The results show that the mineralization of water is from geological origin. They concern the composition of the layers that make up the complex terminal.

Keywords: complex terminal, mineralization, oued righ, statistical approach

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Authors: Sarita Agarwal, Deepika Delsa Dean

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Like the task of transferring photo images to artistic images, image-to-image translation aims to translate the data to the imitated data which belongs to the target domain. Neural Style Transfer and CycleGAN are two well-known deep learning architectures used for photo image-to-art image transfer. However, studies involving these two models concentrate on one-to-one domain translation, not one-to-multi domains translation. Our study tries to investigate deep learning architectures, which can be controlled to yield multiple artistic style translation only by adding a conditional vector. We have expanded CycleGAN and constructed Conditional CycleGAN for 5 kinds of categories translation. Our study found that the architecture inserting conditional vector into the middle layer of the Generator could output multiple artistic images.

Keywords: genetic counseling, FMR1 gene, fragile x-associated primary ovarian insufficiency, premutation

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Authors: Rima Al-Awadhi, Abdul Jaleel Tharayil

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On a regular basis, KOC engages into various types of Projects. However, due to very nature and complexity involved, each project experience generates a lot of ‘learnings’ that need to be factored into while drafting a new contract and thus avoid repeating the same mistakes. But, many a time these learnings are localized and remain as tacit leading to scope re-work, larger cycle time, schedule overrun, adjustment orders and claims. Also, these experiences are not readily available to new employees leading to steep learning curve and longer time to competency. This is to share our experience in designing and implementing a process driven architecture for the ‘lessons learnt’ knowledge sharing framework in KOC. It high-lights the ‘lessons learnt’ sharing process adopted, integration with the organizational processes, governance framework, the challenges faced and learning from our experience in implementing a ‘lessons learnt’ framework.

Keywords: lessons learnt, knowledge transfer, knowledge sharing, successful practices, Lessons Learnt Workshop, governance framework

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Authors: Kristina Pflug, Markus Busch

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Being able to predict polymer properties and processing behavior based on the applied operating reaction conditions in one of the key challenges in modern polymer reaction engineering. Especially, for cost-intensive processes such as the high-pressure polymerization of low-density polyethylene (LDPE) with high safety-requirements, the need for simulation-based process optimization and product design is high. A multi-scale modelling approach was set-up and validated via a series of high-pressure mini-plant autoclave reactor experiments. The approach starts with the numerical modelling of the complex reaction network of the LDPE polymerization taking into consideration the actual reaction conditions. While this gives average product properties, the complex polymeric microstructure including random short- and long-chain branching is calculated via a hybrid Monte Carlo-approach. Finally, the processing behavior of LDPE -its melt flow behavior- is determined in dependence of the previously determined polymeric microstructure using the branch on branch algorithm for randomly branched polymer systems. All three steps of the multi-scale modelling approach can be independently validated against analytical data. A triple-detector GPC containing an IR, viscosimetry and multi-angle light scattering detector is applied. It serves to determine molecular weight distributions as well as chain-length dependent short- and long-chain branching frequencies. 13C-NMR measurements give average branching frequencies, and rheological measurements in shear and extension serve to characterize the polymeric flow behavior. The accordance of experimental and modelled results was found to be extraordinary, especially taking into consideration that the applied multi-scale modelling approach does not contain parameter fitting of the data. This validates the suggested approach and proves its universality at the same time. In the next step, the modelling approach can be applied to other reactor types, such as tubular reactors or industrial scale. Moreover, sensitivity analysis for systematically varying process conditions is easily feasible. The developed multi-scale modelling approach finally gives the opportunity to predict and design LDPE processing behavior simply based on process conditions such as feed streams and inlet temperatures and pressures.

Keywords: low-density polyethylene, multi-scale modelling, polymer properties, reaction engineering, rheology

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Authors: Anna Sanczyk

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Teacher agency plays a crucial role in effective teaching, supporting diverse students, and providing an enriching learning environment; therefore, it is significant to gain a deeper understanding of language teachers’ sense of agency in teaching linguistically and culturally diverse students. This paper presents an overview of qualitative research on how language teachers exercise their agency in diverse classrooms. The analysis of the literature reveals that language teachers strive for addressing students’ needs and challenging educational inequalities, but experience educational constraints in enacting their agency. The examination of the research on language teacher agency identifies four major areas where language teachers experience challenges in enacting their agency: (1) implementing curriculum; (2) adopting school reforms and policies; (3) engaging in professional learning; (4) and negotiating various identities as professionals. The practical contribution of this literature review is that it provides a much-needed compilation of the studies on how language teachers exercise agency amid educational constraints. The discussion of the overview points to the importance of teacher identity, learner advocacy, and continuous professional learning and the critical need of promoting empowerment, activism, and transformation in language teacher education. The findings of the overview indicate that language teacher education programs should prepare teachers to be active advocates for English language learners and guide teachers to become more conscious of complexities of teaching in constrained educational settings so that they can become agentic professionals. This literature overview illustrates agency work in English language teaching contexts and contributes to understanding of the important link between experiencing educational constraints and development of teacher agency.

Keywords: advocacy, educational constraints, language teacher agency, language teacher education

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Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

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The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

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Authors: Yoon-Cheol Park

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This research focuses on examining the Chinese character-Korean language translation of the Vajrachedika-prajna-paramita sutra by a pragmatic approach. The background of this research is that there were no previous researches which looked into the Vajrachedika-prajna-paramita translation by pragmatic approach until now. Even though it is composed of conversational structures between Buddha and his disciple unlike other Buddhist sutras, most of its translation could find the traces to have pursued literal translation and still has now overlooked pragmatic elements in it. Accordingly, it is meaningful to examine the messages through speaker and hearer relation and between speaker intention and utterance meaning. Practically, the Vajrachedika-prajna-paramita sutra includes pragmatic elements, such as speech acts, presupposition, conversational implicature, the cooperative principle and politeness. First, speech acts in its sutra text show the translation to reveal obvious performance meanings of language to the target text. And presupposition in their dialogues is conveyed by paraphrasing or substituting abstruse language with easy expressions. Conversational implicature in utterances makes it possible to understand the meanings of holy words by relying on utterance contexts. In particular, relevance results in an increase of readability in the translation owing to previous utterance contexts. Finally, politeness in the target text is conveyed with natural stylistics through the honorific system of the Korean language. These elements mean that the pragmatic approach can function as a useful device in conveying holy words in a specific, practical and direct way depending on utterance contexts. Therefore, we expect that taking a pragmatic approach in translating the Vajrachedika-prajna-paramita sutra will provide a theoretical foundation for seeking better translation methods than the literal translations of the past. And it implies that the translation of Buddhist sutra needs to convey messages by translation methods which take into account the characteristic of sutra text like the Vajrachedika-prajna-paramita.

Keywords: buddhist sutra, Chinese character-Korean language translation, pragmatic approach, utterance context

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Authors: Odoh Dickson Akpegi

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The paper is about the need for an indigenous language in order to meaningfully harness both our human and material resources for the nation’s integration. It then examines the notty issue of the national language question and advocates a piece meal approach in solving the problem. This approach allows for the development and use of local languages in minority areas, especially in Benue State, as a way of preparing them for consideration as possible replacement for English language as Nigeria’s national or official language. Finally, an arrangement to follow to prepare the languages for such competition at the national level is presented.

Keywords: indigenous language, English language, official language, National integration

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Authors: Harun Bozna

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MOOC (massive open online course) is a groundbreaking education platform and a current buzzword in higher education. Although MOOCs offer many appreciated learning experiences to learners from various universities and institutions, they have considerably higher dropout rates than traditional education. Only about 10% of the learners who enroll in MOOCs actually complete the course. In this case, perceptions of participants and a comprehensive analysis of MOOCs have become an essential part of the research in this area. This study aims to explore the MOOCs in detail for better understanding its content, purpose and primarily drop out issues. The researcher conducted an online questionnaire to get perceptions of graduate students on their learning experiences in MOOCs and arranged a semi- structured oral interview with some participants. The participants are Turkish graduate level students doing their MA and Ph.D. in various programs. The findings show that participants are more likely to drop out courses due to lack of time and lack of pressure.

Keywords: distance education, MOOCs, drop out, perception of graduate students

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Authors: Min Chen, Rubi Gupta

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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

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Authors: Jin Woo Hwang, Byung Kwan Oh, Yousok Kim, Hyo Seon Park

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As greenhouse effect has been recognized as serious environmental problem of the world, interests in carbon dioxide (CO₂) emission which comprises major part of greenhouse gas (GHG) emissions have been increased recently. Since construction industry takes a relatively large portion of total CO₂ emissions of the world, extensive studies about reducing CO₂ emissions in construction and operation of building have been carried out after the 2000s. Also, performance based design (PBD) methodology based on nonlinear analysis has been robustly developed after Northridge Earthquake in 1994 to assure and assess seismic performance of building more exactly because structural engineers recognized that prescriptive code based design approach cannot address inelastic earthquake responses directly and assure performance of building exactly. Although CO₂ emissions and PBD approach are recent rising issues on construction industry and structural engineering, there were few or no researches considering these two issues simultaneously. Thus, the objective of this study is to minimize the CO₂ emissions and cost of building designed by PBD approach in structural design stage considering structural materials. 4 story and 4 span reinforced concrete building optimally designed to minimize CO₂ emissions and cost of building and to satisfy specific seismic performance (collapse prevention in maximum considered earthquake) of building satisfying prescriptive code regulations using non-dominated sorting genetic algorithm-II (NSGA-II). Optimized design result showed that minimized CO₂ emissions and cost of building were acquired satisfying specific seismic performance. Therefore, the methodology proposed in this paper can be used to reduce both CO₂ emissions and cost of building designed by PBD approach.

Keywords: CO2 emissions, performance based design, optimization, sustainable design

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Authors: Maged Kamal

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Previous research papers have suggested that enhancing the environmental performance in textiles and apparel industry would affect positively on the overall enterprise competitiveness. However, there is a gap in the literature regarding simplifying the available theory to get it practically implemented with more confidence of the expected results, especially for small and medium enterprises. The aim of this paper is to simplify and best use of the concerned international norms to produce a systematic approach that could be used as a guideline for practical application of the main sustainable principles in medium size textile business. The increasing in efficiency which has been resulted from the implementation of the suggested approach/model originated from reduction in raw materials usage, energy, and water savings, in addition to the risk reduction for the people and the environment. The practical case study has been implemented in a textile factory producing knitted fabrics, readymade garments, dyed and printed fabrics. The results were analyzed to examine the effect of the suggested change on the enterprise profitability.

Keywords: apparel industry, environmental management, sustainability, textiles

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

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

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

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Authors: Tolulope Aremu

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The key process steps to produce liquid detergent products will introduce potential defects, such as formulation, mixing, filling, and packaging, which might compromise product quality, consumer safety, and operational efficiency. Real-time identification and characterization of such defects are of prime importance for maintaining high standards and reducing waste and costs. Usually, defect detection is performed by human inspection or rule-based systems, which is very time-consuming, inconsistent, and error-prone. The present study overcomes these limitations in dealing with optimization in defect characterization within the process for making liquid detergents using Machine Learning algorithms. Performance testing of various machine learning models was carried out: Support Vector Machine, Decision Trees, Random Forest, and Convolutional Neural Network on defect detection and classification of those defects like wrong viscosity, color deviations, improper filling of a bottle, packaging anomalies. These algorithms have significantly benefited from a variety of optimization techniques, including hyperparameter tuning and ensemble learning, in order to greatly improve detection accuracy while minimizing false positives. Equipped with a rich dataset of defect types and production parameters consisting of more than 100,000 samples, our study further includes information from real-time sensor data, imaging technologies, and historic production records. The results are that optimized machine learning models significantly improve defect detection compared to traditional methods. Take, for instance, the CNNs, which run at 98% and 96% accuracy in detecting packaging anomaly detection and bottle filling inconsistency, respectively, by fine-tuning the model with real-time imaging data, through which there was a reduction in false positives of about 30%. The optimized SVM model on detecting formulation defects gave 94% in viscosity variation detection and color variation. These values of performance metrics correspond to a giant leap in defect detection accuracy compared to the usual 80% level achieved up to now by rule-based systems. Moreover, this optimization with models can hasten defect characterization, allowing for detection time to be below 15 seconds from an average of 3 minutes using manual inspections with real-time processing of data. With this, the reduction in time will be combined with a 25% reduction in production downtime because of proactive defect identification, which can save millions annually in recall and rework costs. Integrating real-time machine learning-driven monitoring drives predictive maintenance and corrective measures for a 20% improvement in overall production efficiency. Therefore, the optimization of machine learning algorithms in defect characterization optimum scalability and efficiency for liquid detergent companies gives improved operational performance to higher levels of product quality. In general, this method could be conducted in several industries within the Fast moving consumer Goods industry, which would lead to an improved quality control process.

Keywords: liquid detergent manufacturing, defect detection, machine learning, support vector machines, convolutional neural networks, defect characterization, predictive maintenance, quality control, fast-moving consumer goods

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Authors: Paul Nadasdy

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In 2020, oral communication classes at many universities in Japan switched to online and hybrid lessons because of the coronavirus pandemic. Teachers had to adapt their practices immediately and deal with the challenges of the online environment. Even for experienced teachers, this still presented a problem as many had not conducted online classes before. Simultaneously, for many students, this type of learning was completely alien to them, and they had to adapt to the challenges faced by communicating in English online. This study collected data from 418 first grade students in the first semester of English communication classes at a technical university in Tokyo, Japan. Zoom software was used throughout the learning period. Though there were many challenges in the setting up and implementation of Zoom classes at the university, the results indicated that the students enjoyed the format and made the most of the circumstances. This proved the robustness of the course that was taught in regular lessons and the adaptability of teachers and students to challenges in a very short timeframe.

Keywords: zoom, hybrid lessons, communicative english, online teaching

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Authors: Brogan Guest, Amy Donaldson-Perrott

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The Master’s of Physician Associate Studies (MPAS) programme at St George’s, University of London (SGUL), is an intensive two-year course that trains students to become physician associates (PAs). PAs are generalized healthcare providers who work in primary and secondary care across the UK. PA programmes face the difficult task of preparing students to become safe medical providers in two short years. Our goal is to teach students to develop clinical reasoning early on in their studies and historically, this has been done predominantly though problem-based learning (PBL). We have had an increase concern about student engagement in PBL and difficulty recruiting facilitators to maintain the low student to facilitator ratio required in PBL. To address this issue, we created ‘Clinical Application of Anatomy and Physiology (CAAP)’. These peer-led, interactive, problem-based, small group sessions were designed to facilitate students’ clinical reasoning skills. The sessions were designed using the concept of Team-Based Learning (TBL). Students were divided into small groups and each completed a pre-session quiz consisting of difficult questions devised to assess students’ application of medical knowledge. The quiz was completed in small groups and they were not permitted access of external resources. After the quiz, students worked through a series of openended, clinical tasks using all available resources. They worked at their own pace and the session was peer-led, rather than facilitator-driven. For a group of 35 students, there were two facilitators who observed the sessions. The sessions utilised an infinite space whiteboard software. Each group member was encouraged to actively participate and work together to complete the 15-20 tasks. The session ran for 2 hours and concluded with a post-session quiz, identical to the pre-session quiz. We obtained subjective feedback from students on their experience with CAAP and evaluated the objective benefit of the sessions through the quiz results. Qualitative feedback from students was generally positive with students feeling the sessions increased engagement, clinical understanding, and confidence. They found the small group aspect beneficial and the technology easy to use and intuitive. They also liked the benefit of building a resource for their future revision, something unique to CAAP compared to PBL, which out students participate in weekly. Preliminary quiz results showed improvement from pre- and post- session; however, further statistical analysis will occur once all sessions are complete (final session to run December 2022) to determine significance. As a post-graduate healthcare professional programme, we have a strong focus on self-directed learning. Whilst PBL has been a mainstay in our curriculum since its inception, there are limitations and concerns about its future in view of student engagement and facilitator availability. Whilst CAAP is not TBL, it draws on the benefits of peer-led, small group work with pre- and post- team-based quizzes. The pilot of these sessions has shown that students are engaged by CAAP, and they can make significant progress in clinical reasoning in a short amount of time. This can be achieved with a high student to facilitator ratio.

Keywords: problem based learning, team based learning, active learning, peer-to-peer teaching, engagement

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Authors: A. Pedro, T. Goldschmidt

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Socio-emotional development is considered to be an essential prerequisite for school readiness. To our best knowledge, research on socio-emotional development specifically from the views of teachers in the South African context is limited. This study explored preschool teachers’ understanding of the role that socio-emotional development plays in preparing the child for school. Using the social learning theory, a qualitative approach with an exploratory design was used for the study. A total of 12 preschool teachers from both community-based and school-based preschools were purposively recruited. Upon receiving ethics clearance from the University of the Western Cape and the Western Cape Education Department, semi-structured interviews were conducted and analysed by utilizing Braun and Clarke’s (2006) six phases of thematic analysis. Participants’ rights, anonymity, and confidentiality were upheld throughout the research process. Findings reveal that preschool teachers emphasise the importance of holistic development for school readiness. Teachers deemed socio-emotional development as absolutely crucial for preparing children for school as it eases the transition to formal schooling and adaptation to the classroom environment.

Keywords: early childhood, preschool teachers, school readiness, socio-emotional development

Procedia PDF Downloads 145