Search results for: observational learning

Authors: Ranveet Saggu, Saad Bin Ahmed

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Cardiovascular Disease (CVD) is the leading cause of death worldwide. One of its main manifestations, myocardial infarction (commonly known as a heart attack), occurs about 750,000 times a year, caused by insufficient blood flow to a portion of the heart muscle. A quick and accurate diagnosis of a heart attack or heart failure is crucial in the treatment of the patient. The aim of this research project is to improve the prediction of cardiovascular diseases by automating risk assessment using binary classifiers. The methodology includes Exploratory Data Analysis (EDA), which helps to obtain information about the dataset with the help of visualizations and metrics. Additionally, Feature Engineering techniques is employed to address missing values, outliers, feature extraction, and normalizing the dataset. Subsequently, various classification machine learning algorithms are trained, and their accuracy along with other metrics are evaluated to identify the most efficient model in terms of processing time and predictive performance.

Keywords: cardiovascular disease, machine learning, deci- sion trees, logistic regression, k-nearest neighbor, xgboost, random forest, gradient boosting

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Authors: Anna-Kaisa Sjolund

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The doctoral thesis examines interaction design and sensory ethnography as tools to create immersive education environments. In recent years, there has been increasing interest and discussions among researchers and educators on immersive education like augmented reality tools, virtual glasses and the possibilities to utilize them in education at all levels. Using virtual devices as learning environments it is possible to create multisensory learning environments. Sensory ethnography in this study refers to the way of the senses consider the impact on the information dynamics in immersive learning environments. The past decade has seen the rapid development of virtual world research and virtual ethnography. Christine Hine's Virtual Ethnography offers an anthropological explanation of net behavior and communication change. Despite her groundbreaking work, time has changed the users’ communication style and brought new solutions to do ethnographical research. The virtual reality with all its new potential has come to the fore and considering all the senses. Movie and image have played an important role in cultural research for centuries, only the focus has changed in different times and in a different field of research. According to Karin Becker, the role of image in our society is information flow and she found two meanings what the research of visual culture is. The images and pictures are the artifacts of visual culture. Images can be viewed as a symbolic language that allows digital storytelling. Combining the sense of sight, but also the other senses, such as hear, touch, taste, smell, balance, the use of a virtual learning environment offers students a way to more easily absorb large amounts of information. It offers also for teachers’ different ways to produce study material. In this article using sensory ethnography as research tool approaches the core question. Sensory ethnography is used to describe information dynamics in immersive environment through interaction design. Immersive education environment is understood as three-dimensional, interactive learning environment, where the audiovisual aspects are central, but all senses can be taken into consideration. When designing learning environments or any digital service, interaction design is always needed. The question what is interaction design is justified, because there is no simple or consistent idea of what is the interaction design or how it can be used as a research method or whether it is only a description of practical actions. When discussing immersive learning environments or their construction, consideration should be given to interaction design and sensory ethnography.

Keywords: immersive education, sensory ethnography, interaction design, information dynamics

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Authors: Anass Hsissi, Hakim Allali, Abdelmajid Hajami

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This article presents the results of a doctoral research which aims to integrate metacognitive dimension in the design of human learning computing environments (ILE). We conducted a detailed study on the relationship between metacognitive processes and learning, specifically their positive impact on the performance of learners in the area of reading comprehension. Our contribution is to implement methods, using an intelligent agent based on BDI paradigm to ensure intelligent and reliable support for low readers, in order to encourage regulation and a conscious and rational use of their metacognitive abilities.

Keywords: metacognition, text comprehension EIAH, autoregulation, BDI agent

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Authors: Champa Gunawardana, Anishka Hettiarachchi

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Conducive Architecture for learning environments is an area of interest for many scholars around the world. Loss of sense of hearing leads to the assumption that deaf students are visual learners. Comprehending favorable non-hearing attributes of architecture can lead to effective, rich and friendly learning environments for hearing impaired. The objective of the current qualitative investigation is to explore the nature and parameters of a sense of place of deaf children to support optimal learning. The investigation was conducted with hearing-impaired children (age: between 8-19, Gender: 15 male and 15 female) of Yashodhara deaf and blind school at Balangoda, Sri Lanka. A sensory ethnography study was adopted to identify the nature of perception and the parameters of most preferred and least preferred spaces of the learning environment. The common perceptions behind most preferred places in the learning environment were found as being calm and quiet, sense of freedom, volumes characterized by openness and spaciousness, sense of safety, wide spaces, privacy and belongingness, less crowded, undisturbed, availability of natural light and ventilation, sense of comfort and the view of green colour in the surroundings. On the other hand, the least preferred spaces were found to be perceived as dark, gloomy, warm, crowded, lack of freedom, smells (bad), unsafe and having glare. Perception of space by deaf considering the hierarchy of sensory modalities involved was identified as; light - color perception (34 %), sight - visual perception (32%), touch - haptic perception (26%), smell - olfactory perception (7%) and sound – auditory perception (1%) respectively. Sense of freedom (32%) and sense of comfort (23%) were the predominant psychological parameters leading to an optimal sense of place perceived by hearing impaired. Privacy (16%), rhythm (14%), belonging (9%) and safety (6%) were found as secondary factors. Open and wide flowing spaces without visual barriers, transparent doors and windows or open port holes to ease their communication, comfortable volumes, naturally ventilated spaces, natural lighting or diffused artificial lighting conditions without glare, sloping walkways, wider stairways, walkways and corridors with ample distance for signing were identified as positive characteristics of the learning environment investigated.

Keywords: deaf, visual learning environment, perception, sensory ethnography

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Authors: Hamed Rahmani, Wim Groot

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The term "youth not in employment, education or training (NEET)" refers to a combination of youth unemployment and school dropout. This study investigates the variables that increase the risk of becoming NEET in Iran. A selection bias-adjusted Probit model was employed using machine learning to identify these risk factors. We used cross-sectional data obtained from the Statistical Centre of Iran and the Ministry of Cooperatives Labour and Social Welfare that was taken from the labour force survey conducted in the spring of 2021. We look at years of education, work experience, housework, the number of children under the age of six in the home, family education, birthplace, and the amount of land owned by households. Results show that hours spent performing domestic chores enhance the likelihood of youth becoming NEET, and years of education and years of potential work experience decrease the chance of being NEET. The findings also show that female youth born in cities were less likely than those born in rural regions to become NEET.

Keywords: NEET youth, probit, CART, machine learning, unemployment

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Authors: Leila Goosen, Toppie N. Mukasa-Lwanga

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The purpose of this paper relates to exploring academics’ use of distance education technologies for empowerment and equity in an Information Technology environment. Literature was studied on academics’ technology use towards effective teaching and meaningful learning in a distance education Information Technology environment. Main arguments presented center on formulating and situating significant concepts within an appropriate theoretical and conceptual framework, including those related to distance education, throughput and other measures of academic efficiency. The research design, sampling, data collection instrument and the validity and reliability thereof, as well as the data analysis method used is described. The paper discusses results related to academics’ use of technology towards effective teaching and meaningful learning in a distance education Information Technology environment. Conclusions are finally presented on the way in which this paper makes a significant and original contribution regarding academics’ use of technology towards effective teaching and meaningful learning in a distance education Information Technology environment.

Keywords: distance, education, technologies, Information Technology Environment

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Authors: Gina L. Solano

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This project endeavors to enhance learning experiences for undergraduate pre-service teachers and graduate K-12 educators by leveraging artificial intelligence (AI). Firstly, the initiative delves into integrating AI within undergraduate education courses, fostering traditional literacy skills essential for academic success and extending their applicability beyond the classroom. Education students will explore AI tools to design literacy-focused activities aligned with their curriculum. Secondly, the project investigates the utilization of AI to craft instructional materials employing gamification strategies (e.g., digital and classic games, badges, quests) to amplify student engagement and motivation in mastering course content. Lastly, it aims to create a professional repertoire that can be applied by pre-service and current teachers in P-12 classrooms, promoting seamless integration for those already in teaching positions. The project's impact extends to benefiting college students, including pre-service and graduate teachers, as they enhance literacy and digital skills through AI. It also benefits current P-12 educators who can integrate AI into their classrooms, fostering innovative teaching practices. Moreover, the project contributes to faculty development, allowing them to cultivate low-risk and engaging classroom environments, ultimately enriching the learning journey. The insights gained from this project can be shared within and beyond the discipline to advance the broader field of study.

Keywords: artificial intelligence, gamification, learning experiences, literacy skills, engagement

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Authors: Abdullah Mukhtar, Shehroz Mukhtar, Amina Mukhtar, Choudhry Shahid, Hafiz Raza Razzaq, Saif Ur Rahman

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The aim of this study is to find out the negative and positive impacts of social media platforms on the attitude of learning and educational environment of student’s community. Social Media platforms have become a source of collaboration with one another throughout the globe making it a small world. This study performs focalized investigation of the adverse and constructive factors that have a strong impact not only on the psychological adjustments but also on the academic performance of peers. This study is a quantitative research adopting random sampling method in which the participants were the students of university. Researcher distributed 1000 questionnaires among the university students from different departments and asked them to fill the data on Lickert Scale. The participants are from the age group of 18-24 years. Study applies user and gratification theory in order to examine behavior of students practicing social media in their academic and personal life. Findings of the study reveal that the use of social media platforms in Pakistani context has less positive impact as compared to negative impacts on the behavior of students towards learning. The research suggests that usage of online social media platforms should be taught to students; awareness must the created among the users of social media by the means of seminars, workshops and by media itself to overcome the negative impacts of social media leading towards sustainable education in Pakistan.

Keywords: social media, positive impact, negative impact, learning behaviour

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Authors: Abdulrahman Sumayli, Saad M. AlShahrani

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For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively

Keywords: temperature, pressure variations, machine learning, oil treatment

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Authors: Shalini Adiga, Shashikant Chetty, Jisha, Shobha Kamath

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Background: Moderate impairment of learning and memory has been observed in both type 1 and 2 diabetes mellitus in humans and experimental animals. A Change in glucose utilization and oxidative stress that occur in diabetes are considered the main reasons for cognitive dysfunction. Objective: Costus igneus (CI) which is known to possess hypoglycemic activity was evaluated in this study for its effect on learning and memory in normal and diabetic rats. Methods: Wistar rats were divided into control, CI-alcoholic extract treated normal (250 and 500mg/kg), diabetic control and CI-treated diabetic groups. CI treatment was continued for 4 weeks. For induction of diabetes, a single dose of streptozotocin was injected (30 mg/kg i.p). Entrance latency and time spent in the dark room during acquisition and at 24 and 48h after an aversive shock in a passive avoidance model was used as an index of learning and memory. Glutathione and malondialdehyde levels in brain and blood glucose were measured. Data was analysed using ANOVA. Results: During the three trials in exploration test, the diabetic control rats exhibited no significant change in entrance latency or in the total time spent in the dark compartment. During retention testing, the entrance latency of the diabetic treated groups was two times less at 24h and three times less at 48h after aversive stimulus as compared to diabetic rats. The normal drug-treated rats showed similar behaviour as the saline control. Treatment with CI significantly reduced the raised blood sugar and MDA levels of diabetic rats. Conclusion: Costus igneus prevented the cognitive dysfunction in diabetic rats which can be attributed to its antioxidant and antihyperglycemic activities.

Keywords: Costus igneous, diabetes, learning and memory, cognitive dysfunction

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Authors: Razieh Karimian, Kim Burton, Mohammad Mehdi Naghizadeh, Maryam Karimian

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Introduction: Habitual upper body postures are associated with online learning during the COVID-19 pandemic. This study explored whether adding an exercise routine to an ergonomic advice intervention improves these postures. Methods: In this randomized trial, 42 male adolescent students with a forward head posture were randomly divided into two equal groups, one allocated to ergonomic advice alone and the other to ergonomic advice plus an exercise routine. The angles of forward head, shoulder, and back postures were measured with a photogrammetric profile technique before and after the 8-week intervention period. Findings: During home quarantine, 76% of the students used their mobile phones, while 35% used a table-chair-computer for online learning. While significant reductions of the forward, shoulder, and back angles were found in both groups (P < 0.001), the effect was significantly greater in the exercise group (P < 0.001: forward head, shoulder, and back angles reduced by some 9, 6, and 5 degrees respectively, compared with 4 degrees in the forward head, and 2 degrees in the shoulder and back angles for ergonomic advice alone. Conclusion: The exercise routine produced a greater improvement in habitual upper body postures than ergonomic advice alone, a finding that may extend beyond online learning at home.

Keywords: randomized trial, online learning, adolescent, posture, exercise, ergonomic advice

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Authors: Benjamin Bobbia, Matthias Picard

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In recent years active learning methodologies based on the representativity of the data seems more promising to limit overfitting. The presented query methodology for regression using the Wasserstein distance measuring the representativity of our labelled dataset compared to the global distribution. In this work a crucial use of GroupSort Neural Networks is made therewith to draw a double advantage. The Wasserstein distance can be exactly expressed in terms of such neural networks. Moreover, one can provide explicit bounds for their size and depth together with rates of convergence. However, heterogeneity of the dataset is also considered by weighting the Wasserstein distance with the error of approximation at the previous step of active learning. Such an approach leads to a reduction of overfitting and high prediction performance after few steps of query. After having detailed the methodology and algorithm, an empirical study is presented in order to investigate the range of our hyperparameters. The performances of this method are compared, in terms of numbers of query needed, with other classical and recent query methods on several UCI datasets.

Keywords: active learning, Lipschitz regularization, neural networks, optimal transport, regression

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Authors: Dongju Kim, Youngjoo Suh, Hyojin Kim, Gyeongyeong Kim

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Acoustic Emission Testing (AET) is widely used to test the structural integrity of an operational hydrogen storage container, and clustering algorithms are frequently used in pattern recognition methods to interpret AET results. However, the interpretation of AET results can vary from user to user as the tuning of the relevant parameters relies on the user's experience and knowledge of AET. Therefore, it is necessary to use a deep learning model to identify patterns in acoustic emission (AE) signal data that can be used to classify defects instead. In this paper, a deep learning-based model for classifying the types of defects in hydrogen storage tanks, using AE sensor waveforms, is proposed. As hydrogen storage tanks are commonly constructed using carbon fiber reinforced polymer composite (CFRP), a defect classification dataset is collected through a tensile test on a specimen of CFRP with an AE sensor attached. The performance of the classification model, using one-dimensional convolutional neural network (1-D CNN) and synthetic minority oversampling technique (SMOTE) data augmentation, achieved 91.09% accuracy for each defect. It is expected that the deep learning classification model in this paper, used with AET, will help in evaluating the operational safety of hydrogen storage containers.

Keywords: acoustic emission testing, carbon fiber reinforced polymer composite, one-dimensional convolutional neural network, smote data augmentation

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Authors: Yashas Mattur, Jensen Coonradt

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Muon flux, the rate of muons reaching Earth from the atmosphere, is impacted by various factors such as air pressure, temperature, and humidity. However, the influence of concentrations of PM₂.₅ (particulate matter with diameters 2.5 mm or smaller) on muon detection rates remains unexplored. During the summer of 2023, smoke from Canadian wildfires (containing significant amounts of particulate matter) blew over regions in the Northern US, introducing huge fluctuations in PM₂.₅ concentrations, thus inspiring our experiment to investigate the correlation of PM₂.₅ concentrations and muon rates. To investigate this correlation, muon collision rates were measured and analyzed alongside PM₂.₅ concentration data over the periods of both light and heavy smoke. Other confounding variables, including temperature, humidity, and atmospheric pressure, were also considered. The results reveal a statistically significant inverse correlation between muon flux and PM₂.₅ concentrations, indicating that particulate matter has an impact on the rate of muons reaching the earth’s surface.

Keywords: Muon Flux, atmospheric effects on muons, PM₂.₅, airborne particulate matter

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Authors: Ntloyalefu Justinah, Fumane Khanare

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English learning has been found as one of the prevalent areas of difficulty for Deaf learners. However, studies conducted indicated that this challenge experienced by Deaf learners is an upsetting concern globally as is blamed and hampered by various reasons such as the way English is taught at schools, lack of teachers ' skills and knowledge, therefore, impact negatively on their academic performance. Despite any difficulty in English learning, this language is considered nowadays as the key tool to an educational and occupational career especially in Lesotho. This paper, therefore, intends to contribute to the existing literature by providing the views of Lesotho English teachers, which focuses on how effectively Universal design for learning can be implemented to enhance the academic performance of Deaf learners in context of the English language classroom. The purpose of this study sought to explore the use of universal design for learning (UDL) to support Deaf learners in Lesotho Secondary schools. The present study is informed by interpretative paradigm and situated within a qualitative research approach. Ten participating English teachers from two inclusive schools were purposefully selected and telephonically interviewed to generate data for this study. The data were thematically analysed. The findings indicated that even though UDL is identified as highly proficient and promotes flexibility in teaching methods teachers reflect limited knowledge of the UDL approach. The findings further showed that UDL ensures education for all learners, including marginalised groups, such as learners with disabilities through different teaching strategies. This means that the findings signify the effective use of UDL for the better performance of the English language by Deaf learners (DLs). This aligns with literature that shows mobilizing English teachers as assets help DLs to be engaged and have control in their communities by defining and solving problems using their resources and connections to other networks for asset and exchange. The study, therefore, concludes that teachers acknowledge that even though they assume to be knowledgeable about the definition of UDL, they have a limited practice of the approach, thus they need to be equipped with some techniques and skills to apply for supporting the performance of DLs by using UDL approach in their English teaching. The researchers recommend the awareness of UDL principles by the ministry of Education and Training and teachers training Universities, as well as teachers training colleges, for them to include it in their curricula so that teachers could be properly trained on how to apply it in their teaching effectively

Keywords: deaf learners, Lesotho, support learning, universal design for learning

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Authors: Susanna Asatryan

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The author will introduce a single module related to English teaching methodology for master course students getting specialization “A Foreign Language Teacher of High Schools And Professional Educational Institutions” of Yerevan Brusov State University of Languages and Social Sciences. The overall aim of the presentation is to introduce learning and teaching strategies within EXE Computer program for Mastery student-teachers of the University. The author will display the advantages of the use of this program. The learners interact with the teacher in the classroom as well as they are provided an opportunity for virtual domain to carry out their learning procedures in association with assessment and self-assessment. So they get integrated into blended learning. As this strategy is in its piloting stage, the author has elaborated a single module, embracing 3 main sections: -Teaching English vocabulary at high school, -Teaching English grammar at high school, and -Teaching English pronunciation at high school. The author will present the above mentioned topics with corresponding sections and subsections. The strong point is that preparing this module we have planned to display it on the blended learning landscape. So for this account working with EXE program is highly effective. As it allows the users to operate several tools for self-learning and self-testing/assessment. The author elaborated 3 single EXE files for each topic. Each file starts with the section’s subject-specific description: - Objectives and Pre-knowledge, followed by the theoretical part. The author associated and flavored her observations with appropriate samples of charts, drawings, diagrams, recordings, video-clips, photos, pictures, etc. to make learning process more effective and enjoyable. Before or after the article the author has downloaded a video clip, related to the current topic. EXE offers a wide range of tools to work out or prepare different activities and exercises for the learners: 'Interactive/non-interactive' and 'Textual/non-textual'. So with the use of these tools Multi-Select, Multi-Choice, Cloze, Drop-Down, Case Study, Gap-Filling, Matching and different other types of activities have been elaborated and submitted to the appropriate sections. The learners task is to prepare themselves for the coming module or seminar, related to teaching methodology of English vocabulary, grammar, and pronunciation. The point is that the teacher has an opportunity for face to face communication, as well as to connect with the learners through the Moodle, or as a single EXE file offer it to the learners for their self-study and self-assessment. As for the students’ feedback –EXE environment also makes it available.

Keywords: blended learning, EXE program, learning/teaching strategies, self-study/assessment, virtual domain,

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Authors: Nadia Selene Molina-Moreno, Maria Susana Avila-Garcia, Marco Bianchetti, Marcelina Pantoja-Flores

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Visual Programming Tools available are a great tool for introducing children to programming and to develop a skill set for algorithmic thinking. On the other hand, collaborative learning and pair programming within the context of programming activities, has demonstrated to have social and learning benefits. However, some of the online tools available for programming for children are not designed to allow simultaneous and equitable participation of the team members since they allow only for a single control point. In this paper, a report the work conducted with children playing a user role is presented. A preliminary study to cull ideas, insights, and design considerations for a formal programming course for children aged 8-10 using collaborative learning as a pedagogical approach was conducted. Three setups were provided: 1) lo-fi prototype, 2) PC, 3) a 46' multi-touch single display groupware limited by the application to a single touch entry. Children were interviewed at the end of the sessions in order to know their opinions about teamwork and the different setups defined. Results are mixed regarding the setup, but they agree to like teamwork.

Keywords: children, collaborative programming, visual programming, multi-touch tabletop, lo-fi prototype

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Authors: Rebecca Haythorne

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Background: Agendas continuously emphasise the need to increase work based training and opportunities for individuals with learning disabilities. However research and statistics suggest that there is still significant stigma and stereotypes as to what they can contribute, or gain from being part of the working environment. Method: To tackles some of these prejudices an Occupational Therapy based intervention was developed for learning disability service users working at a social enterprise farm. The intervention aimed to increase positive public perception around individual capabilities and encourage individuals with learning disabilities to take ownership and be proud of their individual personhood and citizenship. This was achieved by using components of the Model of Human Occupation to tailor the intervention to individual values, skills and working contributions. The final project involved making creative wall art for public viewing, focusing on 'who works there and what they do'. This was accompanied by a visitor information guide, allowing individuals to tell visitors about themselves, the work they do and why it is meaningful to them. Outcomes: The intervention has helped to increased metal well-being and confidence of learning disability service users “people will know I work here now” and “I now have something to show my family about the work I do at the farm”. The intervention has also increased positive public perception and community awareness “you can really see the effort that’s gone into doing this” and “it’s a really visual experience to see people you don’t expect to see doing this type of work”. Resources left behind have further supported individuals to take ownership in creating more wall art to be sold at the farm shop. Conclusion: the intervention developed has helped to improve mental well-being of both service users and staff and improve community awareness. Due to this, the farm has decided to roll out the intervention to other areas of the social enterprise and is considering having more Occupational Therapy involvement in the future.

Keywords: citizenship, intervention, occupational therapy, personhood

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Authors: Amedeo D'Angiulli, Kylie Schibli

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Traditionally, children’s brain development research has emphasized the limitative aspects of disability and impairment, electing as an explanatory model the classical clinical notions of brain lesion or functional deficit. In contrast, Positive Educational Neuroscience (PEN) is a new approach that emphasizes strengths and human flourishing related to the brain by exploring how learning practices have the potential to enhance neurocognitive flexibility through neuroplastic overcompensation. This mini-review provides an overview of PEN and shows how it links to the concept of neurocognitive flexibility. We provide examples of how the present concept of neurocognitive flexibility can be applied to special education by exploring examples of neuroplasticity in the learning domain, including: (1) learning to draw in congenitally totally blind children, and (2) music training in children from disadvantaged neighborhoods. PEN encourages educators to focus on children’s strengths by recognizing the brain’s capacity for positive change and to incorporate activities that support children’s individual development.

Keywords: neurocognitive development, positive educational neuroscience, sociocultural approach, special education

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Authors: Susilo Susilo

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In the process of foreign language acquisition, L2 exposure has been evidently assumed efficient for learners to help increase their proficiency. However, to get enough L2 exposure in the context of learning English as a foreign language is not as easy as that of the first language learning context. Therefore, beyond the classroom L2 exposure is helpful for EFL learners to achieve the language tasks. Alongside the rapid development of technology and media, English as a foreign language is virtually used in the social media of almost all regions, affecting the faces of Teaching English as a Foreign Language (TEFL). This different face of TEFL unavoidably intrigues teachers to treat their students differently in the classroom in order that they can put more effort in maximizing beyond-the-class learning to help improve their in-class achievements. The study aims to investigate: 1) EFL teachers’ teaching skills and beliefs about students’ out-of-class activities in different L2 exposure environments, and 2) the effect on EFL teachers’ teaching skills and beliefs about students’ out-of-class activities of different L2 exposure environments. This is a survey for 80 EFL teachers from Senior High Schools in three regions of two provinces in Indonesia. A questionnaire using a four-point Likert scale was distributed to the respondents to elicit data. The questionnaires were developed by reffering to the constructs of teaching skills (i.e. teaching preparation, teaching action, and teaching evaluation) and beliefs about out-of-class learning (i.e. setting, process and atmosphere), which have been taken from some expert definitions. The internal consistencies for those constructs were examined by using Cronbach Alpha. The data of the study were analyzed by using SPSS program, i.e. descriptive statistics and independent sample t-test. The standard for determining the significance was p < .05. The results revealed that: 1) teaching skills performed by the teachers of English as a foreign language in different exposure environments showed various focus of teaching skills, 2) the teachers showed various ways of beliefs about students’ out-of-class activities in different exposure environments, 3) there was a significant difference in the scores for NNESTs’ teaching skills in urban regions (M=34.5500, SD=4.24838) and those in rural schools (M=24.9500, SD=2.42794) conditions; t (78)=12.408, p = 0.000; and 4) there was a significant difference in the scores for NNESTs’ beliefs about students’ out-of-class activities in urban schools (M=36.9250, SD=6.17434) and those in rural regions (M=29.4250, SD=4.56793) conditions; t (78)=6.176, p = 0.000. These results suggest that different L2 exposure environments really do have effects on teachers’ teaching skills and beliefs about their students’ out-of-class learning.

Keywords: belief about EFL out-of-class learning, L2 exposure environment, teachers of English as a foreign language, teaching skills

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: Arun Sanjel, Greg Speegle

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Every second, massive amounts of data are generated, and Data Intensive Scalable Computing (DISC) frameworks have evolved into effective tools for analyzing such massive amounts of data. Since the underlying architecture of these distributed computing platforms is often new to users, building a DISC application can often be time-consuming and prone to errors. The automated conversion of a sequential program to a DISC program will consequently significantly improve productivity. However, synthesizing a user’s intended program from an input specification is complex, with several important applications, such as distributed program synthesizing and code refactoring. Existing works such as Tyro and Casper rely entirely on deductive synthesis techniques or similar program synthesis approaches. Our approach is to develop a data-driven synthesis technique to identify sequential components and translate them to equivalent distributed operations. We emphasize using reinforcement learning and unit testing as feedback mechanisms to achieve our objectives.

Keywords: program synthesis, distributed computing, reinforcement learning, unit testing, DISC

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Authors: Said Ahmed Zohairy

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Significant shifts in the theory and practice of teaching vocabulary affect teachers’ decisions about learning materials’ design. Relevant literature supports teaching specialised, authentic, and multi-word lexical items rather than focusing on single-word vocabulary lists. Corpora, collections of texts stored in a database, presents a reliable source of teaching and learning materials. Although corpus-informed studies provided guidance for teachers to identify useful language chunks and phraseological units, there is a scarcity in the literature discussing the use of corpora in teaching English for academic and specific purposes (EASP). The aim of this study is to improve teaching practices and provide a description of the pedagogical choices and procedures of an EASP tutor in an attempt to offer guidance for novice corpus users. It draws on the researcher’s experience of utilising corpus linguistic tools to design vocabulary learning activities without focusing on students’ learning outcomes. Hence, it adopts a self-study research methodology which is based on five methodological components suggested by other self-study researchers. The findings of the study noted that designing specialised and corpus-informed vocabulary learning activities could be challenging for teachers, as they require technical knowledge of how to navigate corpora and utilise corpus analysis tools. Findings also include a description of the researcher’s approach to building and analysing a specialised corpus for the benefit of novice corpus users; they should be able to start their own journey of designing corpus-based activities.

Keywords: corpora, corpus linguistics, corpus-informed, English for academic and specific purposes, agribusiness, vocabulary, phraseological units, materials design

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Authors: Francisco Ben, Tracey Price, Chad Morrison, Victoria Warren, Willy Gollan, Robyn Dunbar, Frank Davies, Mark Sorrell

Abstract:

This paper focuses particularly on the educational aspects that contribute to the overall educational satisfaction rated by Tabor School of Education students who participated in the Australasian Survey of Student Engagement (AUSSE) conducted by the Australian Council for Educational Research (ACER) in 2010, 2012 and 2013. In all three years of participation, Tabor ranked first especially in the area of overall student satisfaction. By using a single level path analysis in relation to the AUSSE datasets collected using the Student Engagement Questionnaire (SEQ) for Tabor School of Education, seven aspects that contribute to overall student satisfaction have been identified. There appears to be a direct causal link between aspects of the Supportive Learning Environment, Work Integrated Learning, Career Readiness, Academic Challenge, and overall educational satisfaction levels. A further three aspects, being Student and Staff Interactions, Active Learning, and Enriching Educational Experiences, indirectly influence overall educational satisfaction levels.

Keywords: attrition, retention, educational experience, pre-service teacher education, student satisfaction

Procedia PDF Downloads 355

Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

Abstract:

The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

Procedia PDF Downloads 150

Authors: Gabriela Steffen

Abstract:

Learning in a bilingual classroom not only implies learning in two languages or in an L2, it also means learning content subjects through the means of bilingual or plurilingual resources, which is of a qualitatively different nature than ‘monolingual’ learning. These resources form elements of a didactics of plurilingualism, aiming not only at the development of a plurilingual competence, but also at drawing on plurilingual resources for nonlinguistic subject learning. Applying a didactics of plurilingualism allows for taking account of the specificities of bilingual content subject learning in bilingual education classrooms. Bilingual education is used here as an umbrella term for different programs, such as bilingual education, immersion, CLIL, bilingual modules in which one or several non-linguistic subjects are taught partly or completely in an L2. This paper aims at discussing first results of a study on pupil group work in bilingual classrooms in several Swiss primary schools. For instance, it analyses two bilingual classes in two primary schools in a French-speaking region of Switzerland that follows a part of their school program through German in addition to French, the language of instruction in this region. More precisely, it analyses videotaped classroom interaction and in situ classroom practices of pupil group work in a mathematics lessons. The ethnographic observation of pupils’ group work and the analysis of their interaction (analytical tools of conversational analysis, discourse analysis and plurilingual interaction) enhance the description of whole-class interaction done in the same (and several other) classes. While the latter are teacher-student interactions, the former are student-student interactions giving more space to and insight into pupils’ talk. This study aims at the description of the linguistic and multimodal resources (in German L2 and/or French L1) pupils mobilize while carrying out a mathematical task. The analysis shows that the accomplishment of the mathematical task takes place in a bilingual mode, whether the whole-class interactions are conducted rather in a bilingual (German L2-French L1) or a monolingual mode in L2 (German). The pupils make plenty of use of German L2 in a setting that lends itself to use French L1 (peer groups with French as a dominant language, in absence of the teacher and a task with a mathematical aim). They switch from French to German and back ‘naturally’, which is regular for bilingual speakers. Their linguistic resources in German L2 are not sufficient to allow them to (inter-)act well enough to accomplish the task entirely in German L2, despite their efforts to do so. However, this does not stop them from carrying out the task in mathematics adequately, which is the main objective, by drawing on the bilingual resources at hand.

Keywords: bilingual content subject learning, bilingual primary education, bilingual pupil group work, bilingual teaching/learning resources, didactics of plurilingualism

Procedia PDF Downloads 164

Authors: Ji-Seok Koo, Hee‑Yong Kwon, Hui-Young Yun, Kyung-Hui Wang, Youn-Seo Koo

Abstract:

This paper presents a forecasting system for PM2.5 levels in Seoul, South Korea, leveraging a combination of chemical transport modeling and ConvLSTM-DNN machine learning technology. Exposure to PM2.5 has known detrimental impacts on public health, making its prediction crucial for establishing preventive measures. Existing forecasting models, like the Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF), are hindered by their reliance on uncertain input data, such as anthropogenic emissions and meteorological patterns, as well as certain intrinsic model limitations. The system we've developed specifically addresses these issues by integrating machine learning and using carefully selected input features that account for local and distant sources of PM2.5. In South Korea, the PM2.5 concentration is greatly influenced by both local emissions and long-range transport from China, and our model effectively captures these spatial and temporal dynamics. Our PM2.5 prediction system combines the strengths of advanced hybrid machine learning algorithms, convLSTM and DNN, to improve upon the limitations of the traditional CMAQ model. Data used in the system include forecasted information from CMAQ and WRF models, along with actual PM2.5 concentration and weather variable data from monitoring stations in China and South Korea. The system was implemented specifically for Seoul's PM2.5 forecasting.

Keywords: PM2.5 forecast, machine learning, convLSTM, DNN

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Authors: Uma Maheshwari V., Rajanikanth Aluvalu, Kumar Gautam

Abstract:

The COVID-19 disease is a highly contagious viral infection with major worldwide health implications. The global economy suffers as a result of COVID. The spread of this pandemic disease can be slowed if positive patients are found early. COVID-19 disease prediction is beneficial for identifying patients' health problems that are at risk for COVID. Deep learning and machine learning algorithms for COVID prediction using X-rays have the potential to be extremely useful in solving the scarcity of doctors and clinicians in remote places. In this paper, a convolutional neural network (CNN) with deep layers is presented for recognizing COVID-19 patients using real-world datasets. We gathered around 6000 X-ray scan images from various sources and split them into two categories: normal and COVID-impacted. Our model examines chest X-ray images to recognize such patients. Because X-rays are commonly available and affordable, our findings show that X-ray analysis is effective in COVID diagnosis. The predictions performed well, with an average accuracy of 99% on training photographs and 88% on X-ray test images.

Keywords: deep CNN, COVID–19 analysis, feature extraction, feature map, accuracy

Procedia PDF Downloads 83

Authors: Salah Eddine Ben Fadhel

Abstract:

Lev Vygotsky (1896–1934) was one of the most significant psychologists of the twentieth century despite his short life. His cultural-historical theory is still inspiring many researchers today. At the same time, we observe in many studies a lack of understanding of his thoughts. Vygotsky poses in this theory the contribution of society to individual development and learning. Thus, it suggests that human learning is largely a social and cultural process, further mentioning the influence of interactions between people and the culture in which they live. In this presentation, we highlight, on the one hand, the strong points of the theory by highlighting the major questions it raises and its contribution to developmental psychology in general. On the other hand, we will demonstrate what Vygotsky's theory brings today to the understanding of the cognitive development of children and adolescents. The major objective is to better understand the cognitive mechanisms involved in the learning process in children and adolescents and, therefore, demonstrate the complex nature of psychological development. The main contribution is to provide conceptual insight, which allows us to better understand the importance of the theory and its major pedagogical implications.

Keywords: vygotsky, society, culture, history

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Authors: Vijaya Yuvaram Singh V M, Kameshwar Rao J V

Abstract:

The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images.

Keywords: reinforcement learning, medical landmark detection, multi target detection, deep neural network

Procedia PDF Downloads 145