Search results for: opposition based learning

Authors: Duangkamol Thitivesa

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This paper explores the use of project work in a content-based instruction in a Rajabhat University, Thailand. The use of project is to promote kinds of learning expected of student teachers as stated by Thailand Quality Framework: TQF. The kinds of learning are grouped into five domains: Ethical and moral development, knowledge, cognitive skill, interpersonal skills and responsibility, and analytical and communication skills. The content taught in class is used to lead the student teachers to relate their previously-acquired linguistic knowledge to meaningful realizations of the language system in passages of immediate relevance to their professional interests, teaching methods in particular. Two research questions are formulate to guide this study: 1) To what degree are the five domains of learning expected of student teachers after the use of project in a content class?, and 2) What is the academic achievement of the students’ writing skills, as part of the learning domains stated by TQF, against the 70% attainment target after the use of project to enhance the skill? The sample of the study comprised of 38 fourth-year English major students. The data was collected by means of a summative achievement test, student writing works, an observation checklist, and project diary. The scores in the summative achievement test were analyzed by mean score, standard deviation, and t-test. Project diary serves as students’ record of the language acquired during the project. List of structures and vocabulary noted in the diary has shown students’ ability to attend to, recognize, and focus on meaningful patterns of language forms.

Keywords: Thailand quality framework, project Work, writing skill, summative

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Authors: Parsa Mahdavi, M. Amin Hariri-Ardebili

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The development of accurate machine learning and deep learning models traditionally demands hands-on expertise and a solid background to fine-tune hyperparameters. With the continuous expansion of datasets in various scientific and engineering domains, researchers increasingly turn to machine learning methods to unveil hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of the resultant meta-models and, consequently, the interpretation of the findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. AutoML encompasses crucial stages such as data preparation, feature engineering, hyperparameter optimization, and neural architecture search. This paper provides a comprehensive overview of the principles underpinning AutoML, surveying several widely-used AutoML platforms. Additionally, the paper offers a glimpse into the application of AutoML on various engineering datasets. By comparing these results with those obtained through classical machine learning methods, the paper quantifies the uncertainties inherent in the application of a single ML model versus the holistic approach provided by AutoML. These examples showcase the efficacy of AutoML in extracting meaningful patterns and insights, emphasizing its potential to revolutionize the way we approach and analyze complex datasets.

Keywords: automated machine learning, uncertainty, engineering dataset, regression

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Authors: Hong Zhang

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The main purpose of this study is to show that machine translation (MT) post-editing (PE) training can help our Chinese students learn Spanish as a second language. Our hypothesis is that they might make better use of it by learning PE skills specific for foreign language learning. We have developed PE training materials based on the data collected in a previous study. Training material included the special error types of the output of MT and the error types that our Chinese students studying Spanish could not detect in the experiment last year. This year we performed a pilot study in order to evaluate the PE training materials effectiveness and to what extent PE training helps Chinese students who study the Spanish language. We used screen recording to record these moments and made note of every action done by the students. Participants were speakers of Chinese with intermediate knowledge of Spanish. They were divided into two groups: Group A performed PE training and Group B did not. We prepared a Chinese text for both groups, and participants translated it by themselves (human translation), and then used Google Translate to translate the text and asked them to post-edit the raw MT output. Comparing the results of PE test, Group A could identify and correct the errors faster than Group B students, Group A did especially better in omission, word order, part of speech, terminology, mistranslation, official names, and formal register. From the results of this study, we can see that PE training can help Chinese students learn Spanish as a second language. In the future, we could focus on the students’ struggles during their Spanish studies and complete the PE training materials to teach Chinese students learning Spanish with machine translation.

Keywords: machine translation, post-editing, post-editing training, Chinese, Spanish, foreign language learning

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Authors: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi

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In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.

Keywords: drug synergy, clustering, prediction, machine learning., deep learning

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Authors: S. Probine

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The visual arts have been proven to be a central means through which young children can communicate their ideas, reflect on experience, and construct new knowledge. Despite this, perceptions of, and the degree to which the visual arts are valued within education, vary widely within political, educational, community and family contexts. These differing perceptions informed my doctoral research project, which explored the contextual factors that affect how young children come to value and use the visual arts in their lives and learning. The qualitative methodology of narrative inquiry with inclusion of arts-based methods was most appropriate for this inquiry. Using a sociocultural framework, the stories collected were analysed through the sociocultural theories of Lev Vygotsky as well as the work of Urie Bronfenbrenner, together with postmodern theories about identity formation. The use of arts-based methods such as teacher’s reflective art journals and the collection of images by child participants and their parent/caregivers allowed the research participants to have a significant role in the research. Three early childhood settings at which the visual arts were deeply valued as a meaning-making device in children’s learning, were purposively selected to be involved in the research. At each setting, the study found a unique and complex web of influences and interconnections, which shaped how children utilised the visual arts to mediate their thinking. Although the teachers' practices at all three centres were influenced by sociocultural theories, each settings' interpretations of these theories were unique and resulted in innovative interpretations of the role of the teacher in supporting visual arts learning. These practices had a significant impact on children’s experiences of the visual arts. For many of the children involved in this study, visual art was the primary means through which they learned. The children in this study used visual art to represent their experiences, relationships, to explore working theories, their interests (including those related to popular culture), to make sense of their own and other cultures, and to enrich their imaginative play. This research demonstrates that teachers have fundamental roles in fostering and disseminating the importance of the visual arts within their educational communities.

Keywords: arts-based methods, early childhood education, teacher's visual arts pedagogies, visual arts

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Authors: Bryony Pound

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This study investigated how views of green or urban space affect learning performance. It provides evidence of the value of visual access to greenspace in work and learning environments, and builds on the extensive research into the cognitive and learning-related benefits of access to green and natural spaces, particularly in learning environments. It demonstrates that benefits of visual access to natural spaces whilst learning can produce statistically significant faster responses than those facing urban views after only 5 minutes. The primary hypothesis of this research was that a greenspace view would improve short-term learning. Participants were randomly assigned to either a view of parkland or of urban buildings from the same room. They completed a psychological test of two stages. The first stage consisted of a presentation of words from eight different categories (four manmade and four natural). Following this a 2.5 minute break was given; participants were not prompted to look out of the window, but all were observed doing so. The second stage of the test involved a word recognition/false memory test of three types. Type 1 was presented words from each category; Type 2 was non-presented words from those same categories; and Type 3 was non-presented words from different categories. Participants were asked to respond with whether they thought they had seen the words before or not. Accuracy of responses and reaction times were recorded. The key finding was that reaction times for Type 2 words (highest difficulty) were significantly different between urban and green view conditions. Those with an urban view had slower reaction times for these words, so a view of greenspace resulted in better information retrieval for word and false memory recognition. Importantly, this difference was found after only 5 minutes of exposure to either view, during winter, and with a sample size of only 26. Greenspace views improve performance in a learning task. This provides a case for better visual access to greenspace in work and learning environments.

Keywords: benefits, greenspace, learning, restoration

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Authors: Nahla Khatib

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This study investigated several learning difficulties that affected the classroom learning experience of preservice students who are studying general science and methods of teaching science students at Faculty of Educational Studies at the Arab Open University (AOU) in Amman, Jordan. The focus questions for this study were to find answers for the following: 1. What are the main areas of learning difficulty among preservice students towards science education? 2. What are the main aspects of reducing obstacles towards success in science education? To achieve this goal, the researcher prepared a questionnaire which included 30 items to point out the learning difficulties among preservice students towards science education. The questionnaire was distributed among students enrolled in the general science courses 1&2 and methods of teaching science courses at the beginning of the spring semester of year (2013-2014). After collecting the filled questionnaire a descriptive statistical analysis was carried out (means and standard deviation) for the items of the questionnaire. After analyzing the data statistically our findings showed that student control–factors as well as course controlled factor, factors related to the nature of science, and factors related to the role of instructor affected student success toward science education. The study was concluded with a number of recommendations.

Keywords: nature of science, preservice teachers, science education, learning difficulties

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Authors: Anne-Lena Kampen, Øivind Kure

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Machine learning (ML) can be implemented in Wireless Sensor Networks (WSNs) as a central solution or distributed solution where the ML is embedded in the nodes. Embedding improves privacy and may reduce prediction delay. In addition, the number of transmissions is reduced. However, quality factors such as prediction accuracy, fault detection efficiency and coordinated control of the overall system suffer. Here, we discuss and highlight the trade-offs that should be considered when choosing between embedding and centralized ML, especially for multihop networks. In addition, we present estimations that demonstrate the energy trade-offs between embedded and centralized ML. Although the total network energy consumption is lower with central prediction, it makes the network more prone for partitioning due to the high forwarding load on the one-hop nodes. Moreover, the continuous improvements in the number of operations per joule for embedded devices will move the energy balance toward embedded prediction.

Keywords: central machine learning, embedded machine learning, energy consumption, local machine learning, wireless sensor networks, WSN

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Authors: Jennifer Gallup, Beverly Ray, Esther Ntuli

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Many students such as students from linguistically or culturally diverse backgrounds and those with a disability remain chronically underrepresented in higher level science and mathematics disciplines as well as many hands-on-lab-based activities due to the need for remedial reading and mathematics instruction. Makerspace labs can be a conduit for supporting inclusive learning for these students through hands-on active learning strategies that support equitable access to STEM disciplines. Makerspace is a physical space where individuals gather to create, invent, innovate, and learn while using hands-on materials such as 2D and 3D printers, software programs, electronics, and other tools and supplies. Makerspaces are emerging across many P-12 settings; however, many teachers enter the field not prepared to harness the power inherent in a makerspace, especially for those with disabilities and differing needs. This paper offers suggestions on teaching pre-service teachers and practicing teachers how to incorporate a makerspace into their professional practice through guided instruction and hands-on practice. Recommendations for interested stakeholders are included as well.

Keywords: STEM learning, technology, autism, students with disabilities, makerspace

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Authors: Hahido Samaras

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In many cases, especially with the pandemic playing a major role in fast-tracking the growth of the digital industry, online learning has become a necessity or even a standard educational model nowadays, reliably overcoming barriers such as location, time and cost and frequently combined with a face-to-face format (e.g., in blended learning). This being the case, it is evident that students in many parts of the world, as well as their parents, will increasingly need to become aware of the pros and cons of online versus traditional courses. This fast-growing mode of learning, accelerated during the years of the pandemic, presents an abundance of exciting options especially matched for a large number of secondary school students in remote places of the world where access to stimulating educational settings and opportunities for a variety of learning alternatives are scarce, adding advantages such as flexibility, affordability, engagement, flow and personalization of the learning experience. However, online learning can also present several challenges, such as a lack of student motivation and social interactions in natural settings, digital literacy, and technical issues, to name a few. Therefore, educational researchers will need to conduct further studies focusing on the benefits and weaknesses of online learning vs. traditional learning, while instructional designers propose ways of enhancing student motivation and engagement in virtual environments. Similarly, teachers will be required to become more and more technology-capable, at the same time developing their knowledge about their students’ particular characteristics and needs so as to match them with the affordances the technology offers. And, of course, schools, education programs, and policymakers will have to invest in powerful tools and advanced courses for online instruction. By developing digital courses that incorporate intentional opportunities for community-building and interaction in the learning environment, as well as taking care to include built-in design principles and strategies that align learning outcomes with learning assignments, activities, and assessment practices, rewarding academic experiences can derive for all students. This paper raises various issues regarding the effectiveness of online learning on students by reviewing a large number of research studies related to the usefulness and impact of online learning following the COVID-19-induced digital education shift. It also discusses what students, teachers, decision-makers, and parents have reported about this mode of learning to date. Best practices are proposed for parties involved in the development of online learning materials, particularly for secondary school students, as there is a need for educators and developers to be increasingly concerned about the impact of virtual learning environments on student learning and wellbeing.

Keywords: blended learning, online learning, secondary schools, virtual environments

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Authors: Rosaline Govender

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This research study is an exploration of the self-directed professional development of teachers who teach in public schools in an era of democracy and educational change in South Africa. Amidst an ever-changing educational system, the teachers in this study position themselves as self-directed teacher-learners where they adopt particular learning practices which enable change within the broader discourses of public schooling. Life-story interviews were used to enter into the private and public spaces of five teachers which offer glimpses of how particular systems shaped their identities, and how the meanings of self-directed teacher-learner shaped their learning practices. Through the Multidimensional framework of analysis and interpretation the teachers’ stories were analysed through three lenses: restorying the field texts - the self through story; the teacher-learner in relation to social contexts, and practices of self-directed learning.This study shows that as teacher-learners learn for change through self-directed learning practices, they develop their agency as transformative intellectuals, which is necessary for the reworking of South African public schools.

Keywords: professional development, professionality, professionalism, self-directed learning

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Authors: Jonathan J. Foo, Keng Hao Chew

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Interest in virtual reality (VR) technologies as virtual learning environments have been on the rise in recent years. With thanks to the aggressively competitive consumer electronics environment, VR technology has been made affordable and accessible to the average person with developments like Google Cardboard and Oculus Go. While the promise of virtual access to unique virtual learning environments with the benefits of experiential learning sounds extremely attractive, there are still concerns over user comfort in the psychomotor, cognitive, and affective domains. Reports of motion sickness and short durations create doubt and have stunted its growth. In this paper, a multidimensional framework is proposed for the evaluation of VR learning environments within the three dimensions: tactual quality, didactic quality, and autodidactic quality. This paper further proposes a mixed-methods experimental research plan that sets out to evaluate a virtual reality training simulator in the context of amateur sports fencing. The study will investigate if an immersive VR learning environment can effectively simulate an authentic learning environment suitable for instruction, practice, and assessment while providing the user comfort in the tactual, didactic, and autodidactic dimensions. The models and recommendations developed for this study are designed in the context of fencing, but the potential impact is a guide for the future design and evaluation of all VR developments across sports and technical classroom education.

Keywords: autodidactic quality, didactic quality, tactual quality, virtual reality

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Authors: Sara M. Ismael, Ali H. Al-Badi

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The rapid development and growth of technology has changed the method of obtaining information for educators and learners. Technology has created a new world of collaboration and communication among people. Incorporating new technology into the teaching process can enhance learning outcomes. Billions of individuals across the world are now connected together, and are cooperating and contributing their knowledge and intelligence. Time is no longer wasted in waiting until the teacher is ready to share information as learners can go online and get it immediately. The objectives of this paper are to understand the reasons why changes in teaching and learning methods are necessary, to find ways of improving them, and to investigate the challenges that present themselves in the adoption of new ICT tools in higher education institutes. To achieve these objectives two primary research methods were used: questionnaires, which were distributed among students at higher educational institutes and multiple interviews with faculty members (teachers) from different colleges and universities, which were conducted to find out why teaching and learning methodology should change. The findings show that both learners and educators agree that educational technology plays a significant role in enhancing instructors’ teaching style and students’ overall learning experience; however, time constraints, privacy issues, and not being provided with enough up-to-date technology do create some challenges.

Keywords: e-books, educational technology, educators, e-learning, learners, social media, Web 2.0, LMS

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Authors: Julian Pigott

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This paper introduces the Language Learning as a Means of Drive Engagement (LLMDE) theory, derived from a grounded theory analysis of interviews with Japanese university students. According to LLMDE theory, language learning can be understood as a means of engaging one or more of four self-fulfillment drives: the drive to expand one’s horizons (perspective drive); the drive to make a success of oneself (status drive); the drive to engage in interaction with others (communication drive); and the drive to obtain intellectual and affective stimulation (entertainment drive). While many theories of learner psychology focus on conscious agency, LLMDE theory addresses the role of the unconscious. In addition, supplementary thematic analysis of the data revealed the role of context in mediating drive engagement. Unexpected memorable events, for example, play a key role in instigating and, indirectly, in regulating learning, as do institutional and cultural contexts. Given the apparent importance of such factors beyond the immediate control of the learner, and given the pervasive role of habit and drives, it is argued that the concept of motivation merits theoretical reappraisal. Rather than an underlying force determining language learning success or failure, it can be understood to emerge sporadically in consciousness to promote behavioral change, or to protect habitual behavior from disruption.

Keywords: drives, grounded theory, motivation, significant events

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Authors: Sofia Hartati

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This research is aimed at describing training in early childhood education program empirically, describing teachers ability to plan lessons empirically, and acquiring empirical data as well as analyzing the influence of guided and independent training toward teachers competence in planning early childhood learning program. The method used is an experiment. It collected data with a population of 76 early childhood educators in Tunjung Teja Sub District area through random sampling technique and grouped into two namely 38 people in an experiment class and 38 people in a controlled class. The technique used for data collections is a test. The result of the research shows that there is a significant influence between training for guided educators toward Teachers Ability toward Planning Early Childhood Learning Program. Guided training has been proven to improve the ability to comprehend planning a learning program. The ability to comprehend planning a learning program owned by teachers of early childhood program comprises of 1) determining the characteristics and competence of students prior to learning; 2) formulating the objective of the learning; 3) selecting materials and its sequences; 4) selecting teaching methods; 5) determining the means or learning media; 6) selecting evaluation strategy as a part of teachers pedagogic competence. The result of this research describes a difference in the competence level of teachers who have joined guided training which is relatively higher than the teachers who joined the independent training. Guided training is one of an effective way to improve the knowledge and competence of early childhood educators.

Keywords: competence, planning, teachers, training

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Authors: Ann Dashwood

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Using standard Australian English with confidence is a cultural expectation of parents of primary school aged children who want to engage effectively with their children’s teachers and school administration. That confidence in support of their children’s learning at school is seldom experienced by parents whose first language is not English. Sharing language with competence in an intercultural environment is the common denominator for meaningful communication and engagement to occur in a school community. Experience in relevant interactive sessions is known to enhance engagement and participation. The purpose of this paper is to identify interactional settings for which parents who are isolated from the daily use of functional Australian cultural language learned to engage more effectively in their children’s learning at school. The outcomes measured parents’ intercultural engagement with classroom teachers and attention to the school’s administrative procedures. The study used quantitative and qualitative methods. The principles of communicative task-based language learning combined with intercultural communication principles provided the theoretical base for intensive English task-based learning and engagement. The quantitative analysis examined data samples collected by classroom teachers and administrators and parents’ writing samples. Interviews and observations qualitatively informed the study. Currently significant numbers of projects are active in community centres and schools to enhance English language knowledge of parents from Language Backgrounds Other Than English (LBOTE). The study was significant to explore the effects of conducting intensive English with parents of varied English language backgrounds by targeting language use for social interactions in the community, specific engagement in school activities, cultural interaction with teachers and responsiveness to complying with school procedures.

Keywords: engagement, intercultural communication, LBOTE, school community

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Authors: Moet Moet Myint lay

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Professional learning communities provide ongoing professional development for teachers, where they become learning leaders and actively participate in school improvement. The development of professional knowledge requires a significant focus on professional competence in the work of teachers, and a solid foundation of professional knowledge and skills is necessary for members of society to become intelligent members. Continuing professional development (CPD) plays a vital role in improving educational outcomes, as its importance has been proven over the years. This article explores the need for CPD for teachers in Myanmar and the utility of professional learning communities in improving teacher quality. This study aims to explore a comprehensive understanding of professional learning communities to support the continuing professional development of teacher educators in improving the quality of education. The research questions are: (1) How do teacher educators in Myanmar understand the concept of professional learning communities for continuing professional development? (2) What CPD training is required for all teachers in teachers' colleges? Quantitative research methods were used in this study. Survey data were collected from 50 participants (teacher trainers) from five educational institutions. The analysis shows that professional learning communities when done well, can have a lasting impact on teacher quality. Furthermore, the creation of professional learning communities is the best indicator of professional development in existing education systems. Some research suggests that teacher professional development is closely related to teacher professional skills and school improvement. As a result of the collective learning process, teachers gain a deeper understanding of the subject matter, increase their knowledge, and develop their professional teaching skills. This will help improve student performance and school quality in the future. The lack of clear understanding and knowledge about PLC among school leaders and leads teachers to believe that PLC activities are not beneficial. Lack of time, teacher accountability, leadership skills, and negative attitudes of participating teachers were the most frequently cited challenges in implementing PLCs. As a result of these findings, educators and stakeholders can use them to implement professional learning communities.

Keywords: professional learning communities, continuing professional development, teacher education, competence, school improvement

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

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

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

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Authors: C. W. Kan

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Fabrics can be treated to equip them with certain functional properties in which water repellency is one of the important functional effects. In this study, commercial water repellent agent was used under different application conditions to cotton fabric. Finally, the water repellent effect was evaluated by standard testing method. Thus, the aim of this study is to illustrate the proper application of water repellent finishing to cotton fabric and the results could provide guidance note to the students in learning this topic. Acknowledgment: Authors would like to thank the financial support from the Hong Kong Polytechnic University for this work.

Keywords: learning materials, water repellent, textiles, cotton

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Anne-Marie Tuomala

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Education for sustainable development (ESD) aims to provide students with the attitudes, values, and behaviors necessary for the contribution to sustainability. The research was implemented as a part of the Horizons Europe research project, where each partner organization had at least one pilot project locally. The pilot in question was an online course about the basics of the natural stone industry in Finland and its sustainability and circular economy aspects. The course was open to all students of applied universities in Finland, and it was implemented twice during the research. The Stone from Finland association participated in the course design, and it was also an expert in the local context and real-life provider. The multiple case-study method was chosen, as it enables purposeful sampling of cases that are tailored to the specific study. It was also assumed that it predicts quite comparable results of two different course implementations of the course with the same topic and content. The Curtin University of Technology’s Attitudes Towards Thinking and Learning Survey was adapted. The results show the importance of the trans-disciplinary nature of sustainability education. In addition, the new industry areas with the general - but also industry-specific sustainability issues - must be introduced to students and encourage them to do critically reflective learning. Surveys that guide them to analyze their own attitudes to thinking and learning may expose students to their weaknesses but also result in forms of more active sustainability interaction.

Keywords: education for sustainable development, learning attitudes, learning of circular economy, virtual learning

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Authors: Amadou Wurry Jallow, Adama N. S. Bah, Karamo Bah, Shih-Ye Wang, Kuo-Chung Chu, Chien-Yeh Hsu

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Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predicting and preventing complications of chronic kidney disease (CKD). This study aimed to develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and effective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportional hazard regression analyses were performed to determine the variables with high prognostic values for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laboratory, laboratory, and metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, body mass index (BMI), and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have demonstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The machine learning models are simple to use and flexible because they work even with incomplete data and can be applied in any clinical setting, including settings where laboratory data is difficult to obtain.

Keywords: chronic kidney disease, glomerular filtration rate, creatinine, novel metabolic indices, machine learning, risk prediction

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Authors: Boy Subirosa Sabarguna, Aria Kekalih, Irzan Nurman

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Introduction: Software in the form of Clinical Decision Support System could help students in understanding the mind set of decision-making in diagnosis and treatment at the stage of general practitioners. This could accelerate and ease the learning process which previously took place by using books and experience. Method: Gather 1000 members of the National Medical Multimedia Digital Community (NM2DC) who use the “flowchart of diagnosis and treatment” software, and analyse factors related to: display, speed in learning, convenience in learning, helpfulness and usefulness in the learning process, by using the Likert Scale through online questionnaire which will further be processed using percentage. Results and Discussions: Out of the 1000 members of NM2DC, apparently: 97.0% of the members use the software and 87.5% of them are students. In terms of the analysed factors related to: display, speed in learning, convenience in learning, helpfulness and usefulness of the software’s usage, the results indicate a 90.7% of fairly good performance. Therefore, the “Flowchart of Diagnosis and Treatment” software has helped students in understanding the decision-making of diagnosis and treatment. Conclusion: the use of “Flowchart of Diagnosis and Treatment” software indicates a positive role in helping students understand decision-making of diagnosis and treatment.

Keywords: usage, software, diagnosis and treatment, medical education

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Authors: Feng Yang

Abstract:

Machine learning (ML), especially deep learning (DL), has been extensively applied to many applications in recently years and gained great success in solving different problems, including scientific problems. However, conventional ML/DL methodologies are purely data-driven which have the limitations, such as need of ample amount of labelled training data, lack of consistency to physical principles, and lack of generalizability to new problems/domains. Recently, there is a growing consensus that ML models need to further take advantage of prior knowledge to deal with these limitations. Physics-informed machine learning, aiming at integration of physics/domain knowledge into ML, has been recognized as an emerging area of research, especially in the recent 2 to 3 years. In this work, physics-informed ML, specifically physics-informed neural network (NN), is employed and implemented to estimate the displacements at x, y, z directions in a solid mechanics problem that is controlled by equilibrium equations with boundary conditions. By incorporating the physics (i.e. the equilibrium equations) into the learning process of NN, it is showed that the NN can be trained very efficiently with a small set of labelled training data. Experiments with different settings of the NN model and the amount of labelled training data were conducted, and the results show that very high accuracy can be achieved in fulfilling the equilibrium equations as well as in predicting the displacements, e.g. in setting the overall displacement of 0.1, a root mean square error (RMSE) of 2.09 × 10−4 was achieved.

Keywords: deep learning, neural network, physics-informed machine learning, solid mechanics

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Authors: Rochelle Botha-Marais

Abstract:

When lecturing future educators in South Africa, praxis is an indispensable aspect that is often neglected. Without properly understanding how the theory taught in lecture halls relates to their future position as educators, we can not expect these students to be fully equipped future teachers. To enable education students at the Vaal Campus of the North West University - who have the Afrikaans language as major - to discover the link between theory and practice, the author created an assignment on phonetics in which the use of technology was incorporated. In the past, students had to submit an assignment or worksheet and they did not get the opportunity to apply their newly found knowledge in a practical manner. For potential future teachers, this application is essential. This paper will demonstrate how technology is used in the second year Afrikaans education module to promote student engagement and self-directed learning. Students were introduced to innovative new technologies alongside more familiar applications to shape a 21st century learning environment where students can think, communicate, solve problems, collaborate and take responsibility for their own teaching and learning. The paper will also reflect on student feedback pertaining the use and efficiency of technology in the Afrikaans module and the possible impact thereof on their own teaching and learning landscape. The aim of this paper is to showcase how technology can be used to maximize the students learning experience and equip future education students with the tools and knowledge to introduce technology-enhanced learning in their own teaching practice.

Keywords: education students, theory and practice, self-directed learning, student engagement, technology

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Authors: Almutasim Billa A. Alanazi, Hal S. Tharp

Abstract:

Safe performance and efficient energy consumption are essential factors for designing a control system. This paper presents a reinforcement learning (RL) model that can be applied to control applications to improve safety and reduce energy consumption. As hardware constraints and environmental disturbances are imprecise and unpredictable, conventional control methods may not always be effective in optimizing control designs. However, RL has demonstrated its value in several artificial intelligence (AI) applications, especially in the field of control systems. The proposed model intelligently monitors a system's success by observing the rewards from the environment, with positive rewards counting as a success when the controlled reference is within the desired operating zone. Thus, the model can determine whether the system is safe to continue operating based on the designer/user specifications, which can be adjusted as needed. Additionally, the controller keeps track of energy consumption to improve energy efficiency by enabling the idle mode when the controlled reference is within the desired operating zone, thus reducing the system energy consumption during the controlling operation. Water temperature control for a hydroponic system is taken as a case study for the RL model, adjusting the variance of disturbances to show the model’s robustness and efficiency. On average, the model showed safety improvement by up to 15% and energy efficiency improvements by 35%- 40% compared to a traditional RL model.

Keywords: control system, hydroponics, machine learning, reinforcement learning

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Authors: N. Ayob, N. C. Nkosi, R. P. Burger, S. J. Piketh, F. Letlaila, O. Maphosa

Abstract:

South African higher tertiary institution have been faced with high dropout rates. About 50 to 60% of first years drop out to due to various reasons one being inadequate academic support. Geography 111 (GEOG 111) module is historically known for having below 50% pass rate, high dropout rate and identified as a first year risk module. For the first time GEOG 111 (2021) on the Mahikeng Campus admitted 150 students pursuing more than 6 different qualifications (BA and BSc) from the Humanities Faculty and FNAS. First year students had difficulties transitioning from secondary to tertiary institutions as we shifted to remote learning while we navigate through the Covid-19 pandemic. The traditional method of teaching does not encourage students to help each other. With remote learning we do not have control over what the students share and perhaps this can be a learning opportunity to embrace peer learning and change the manner in which we assess the students. The purpose of this pilot study was to assist GEOG111 students with academic challenges whilst improving their University experience. This was a qualitative study open to all GEOG111, repeaters, students who are not confident in their Geographical knowledge and never did Geography at high school level. The selected 9 Golden Key International Honour Society Geography mentors attended an academic mentor training program with module lecturers. About 17.6% of the mentees did not have a geography background however, 94% of the mentees passed, 1 mentee had a mark of 38%. 11 of the participants had a mark >60% with one student that excelled 70%. It is evident that mentorship helped students reach their academic potential. Peer learning and mentoring are associated with improved academic performance and allows the students to take charge of their learning and academic experience. Thus an important element as we transform pedagogies at higher learning institutions.

Keywords: geography, risk module, peer mentoring, peer learning

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Authors: C. W. Kan

Abstract:

Cotton is the most commonly used material for apparel purpose because of its durability, good perspiration absorption characteristics, comfort during wear and dyeability. However, proneness to creasing and wrinkling give cotton garments a poor rating during actual wear. Resin finishing is a process to bring out crease or wrinkle free/resistant effect to cotton fabric. Thus, the aim of this study is to illustrate the proper application of resin finishing to cotton fabric, and the results could provide guidance note to the students in learning this topic. Acknowledgment: Authors would like to thank the financial support from the Hong Kong Polytechnic University for this work.

Keywords: learning materials, resin, textiles, wrinkle

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Authors: Zyra Cheska Hidalgo, Joehiza Mae Renon, Kzarina Buen, Girlie Mitrado

Abstract:

ABSTRACT: The global coronavirus disease (COVID-19) has dramatically impacted the lives of many, including education and our economy. Thus, it presents a massive challenge for medical education as instructors are mandated to deliver their lectures virtually to ensure the continuity of the medical education process and ensure students' safety. The purpose of this research paper is to determine the lived experiences of paramedical students who are engaged in virtual hands-on learning and to determine the different coping strategies they used to deal with virtual hands-on learning. The researchers used the survey method of descriptive research design to determine the lived experiences and coping strategies of twenty (20) paramedical students from Lorma Colleges (particularly the College of Medicine Department). The data were collected through online questionnaires, particularly with the use of google forms. This study shows technical issues, difficulty in adapting styles, distractions and time management issues, mental and physical health issues, and lack of interest and motivation are the most common problems and challenges experienced by paramedical students. On the other hand, the coping strategies used by paramedical students to deal with those challenges include time management, engagement in leisure activities, acceptance of responsibilities, studying, and adapting. With the data gathered, the researchers concluded that virtual hands-on learning effectively increases the knowledge of paramedical students. However, teaching and learning barriers must have to be considered to implement virtual hands-on learning successfully.

Keywords: virtual hands-on learning, E-learning, paramedical students, medical education

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Authors: Jingwen Shan

Abstract:

In order to explore the key factors affecting the robot program learning intention of school students, this study takes the technology acceptance model as the theoretical basis and invites 167 students from Jiading District of Shanghai as the research subjects. In the robot course, the model of school students on their learning behavior is constructed. By verifying the causal path relationship between variables, it is concluded that teachers can enhance students’ perceptual usefulness to robotics courses by enhancing subjective norms, entertainment perception, and reducing technical anxiety, such as focusing on the gradual progress of programming and analyzing learner characteristics. Students can improve perceived ease of use by enhancing self-efficacy. At the same time, robot hardware designers can optimize in terms of entertainment and interactivity, which will directly or indirectly increase the learning intention of the robot course. By changing these factors, the learning behavior of primary and secondary school students can be more sustainable.

Keywords: TAM, learning behavior intentions, robot courses, primary and secondary school students

Procedia PDF Downloads 137