Search results for: Jones learning center

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: Ahmad Jawad Fardin

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Over 60% of patients with breast cancer in Afghanistan present late with advanced stage III and IV, a major cause for the poor survival rate. The objectives of this study were to identify the contributing factors for the diagnosis and treatment delay and its outcome. This cross-sectional study was conducted on 318 patients with histologically confirmed breast cancer in the oncology department of Jamhoriat hospital, which is the first and only national cancer center in Afghanistan; data were collected from medical records and interviews conducted with women diagnosed with breast cancer, linear regression and logistic regression were used for analysis. Patient delay was defined as the time from first recognition of symptoms until first medical consultation and doctor form first consultation with a health care provider until histological confirmation of breast cancer. The mean age of patients was 49.2+_ 11.5years. The average time for the final diagnosis of breast cancer was 8.5 months; most patients had ductal carcinoma 260.7 (82%). Factors associated with delay were low education level 76% poor socioeconomic and cultural conditions 81% lack of cancer center 73% lack of screening 19%. The stage distribution was as follows stage IV 4 22% stage III 44.4% stage II 29.3% stage I 4.3%. Complex associated factors were identified to delayed the diagnosis of breast cancer and increased adverse outcomes consequently. Raising awareness and education in women, the establishment of cancer centers and providing accessible diagnosis service and screening, training of general practitioners; required to promote early detection, diagnosis and treatment.

Keywords: delayed diagnosis and poor outcome, breast cancer in Afghanistan, poor outcome of delayed breast cancer treatment, breast cancer delayed diagnosis and treatment in Afghanistan

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Authors: Laura Mendez

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Tribunals for the prosecution of crimes against humanity have been implemented in recent history via international intervention or imposed by one side of the conflict, as in the cases of Rwanda, Iraq, Argentina, and Chile. However, the creation of a criminal tribunal as the result of a peace agreement between formerly warring parties has been unique to the Colombian peace process. As such, the Colombian Jurisdiction for Peace (SJP), or JEP for its Spanish acronym, is viewed as a site of social contestation where actors shape its design and implementation. This study contributes to the literature of transitional justice by analyzing how the framing of the creation of the Colombian tribunal reveals the parties' interests. The analysis frames the interests of the power-brokers, i.e., the government and the Revolutionary Armed Forces of Colombia (FARC), and the victims in light of the tribunal’s functions. The purpose of this analysis is to understand how the interests of the parties are embedded in the designing of the SJP. This paper argues that the creation of the SJP rests on restorative justice, for which the victim, not the perpetrator, is at the center of prosecution. The SJP’s approach to justice moves from prosecution as punishment to prosecution as sanctions. SJP’s alternative sanctions focused on truth, reparation, and restoration are designed to humanize both the victim and the perpetrator in order to achieve reconciliation. The findings also show that requiring the perpetrator to perform labor to repair the victim as an alternative form of sanction aims to foster relations of reintegration and social learning between victims and perpetrators.

Keywords: transitional justice mechanisms, criminal tribunals, Colombia, Colombian Jurisdiction for Peace, JEP

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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: Masoud Mahdavi, Mojtaba Farzaneh Moghadam

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The seismic forces caused by the waves created in the depths of the earth during the earthquake hit the structure and cause the building to vibrate. Creating large seismic forces will cause low-strength sections in the structure to suffer extensive surface damage. The use of new steel shear walls in steel structures has caused the strength of the building and its main members (columns) to increase due to the reduction and depreciation of seismic forces during earthquakes. In the present study, an attempt was made to evaluate a type of steel shear wall that has regular holes in the inner sheet by modeling the finite element model with Abacus software. The shear wall of the steel plate, measuring 6000 × 3000 mm (one floor) and 3 mm thickness, was modeled with four different pores with a cross-sectional area. The shear wall was dynamically subjected to a time history of 5 seconds by three accelerators, El Centro, Imperial Valley and Kobe. The results showed that increasing the distance between the geometric center of the hole and the geometric center of the inner plate in the steel shear wall (increasing the R_CS index) caused the total maximum acceleration to be transferred from the perimeter of the hole to horizontal and vertical beams. The results also show that there is no direct relationship between R_CS index and total acceleration in steel shear wall and R_CS index is separate from the peak ground acceleration value of earthquake.

Keywords: hollow steel plate shear wall, time history analysis, finite element method, abaqus software

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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: Shan-Yu Hsu, Hsin-Shu Huang

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The main purpose of this teaching research is to design an interdisciplinary team-assisted teaching method for trainees and interns and review the effectiveness of this teaching method on trainees' understanding of peritoneal dialysis. The teaching research object is the fifth and sixth-grade trainees in a medical center's medical school. The teaching methods include media teaching, demonstration of technical operation, face-to-face communication with patients, special case discussions, and field visits to the peritoneal dialysis room. Evaluate learning effectiveness before, after, and verbally. Statistical analysis was performed using the SPSS paired-sample t-test to analyze whether there is a difference in peritoneal dialysis professional cognition before and after teaching intervention. Descriptive statistics show that the average score of the previous test is 74.44, the standard deviation is 9.34, the average score of the post-test is 95.56, and the standard deviation is 5.06. The results of the t-test of the paired samples are shown as p-value = 0.006, showing the peritoneal dialysis professional cognitive test. Significant differences were observed before and after. The interdisciplinary team-assisted teaching method helps trainees and interns to improve their professional awareness of peritoneal dialysis. At the same time, trainee physicians have positive feedback on the inter-professional team-assisted teaching method. This teaching research finds that the clinical ability development education of trainees and interns can provide cross-professional team-assisted teaching methods to assist clinical teaching guidance.

Keywords: monitor quality, patient safety, health promotion objective, cross-professional team-assisted teaching methods

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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

Procedia PDF Downloads 241

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: Nuzhat Sultana M. B.

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Children are high-risk population in terms of food born illnesses. Food safety and security are the most important aspect of the success of midday meal programmes. Improper holding temperatures, cross-contamination and poor personal hygiene of food handlers are the main causes for the prevalence of pathogenic microbes in the food servicing areas. Two hundred and fifty preschool children in the age of 3 to 6 years from urban and rural anganwadies (pre school center) of Beed district were selected. Nutritional status of preschool children were assessed by anthropometrical and clinical measurement. The study assessed the food safety and security with the help of personal hygiene and other safety measures maintained by the food personnel working for midday meal programme, supplying mid meals to children in govt. anganwadies (pre school center). The hygiene level, sanitary condition and microbial quality of food and water, pathological health examination of food handlers were assessed with the help of checklist. A questionnaire was designed to evaluate knowledge, attitude, and practices of food handlers. Results of the study show that the nutritional and health status of rural and urban preschool children was very poor. Many of the food handlers were not aware of general knowledge and hygiene practices to be followed during food preparation areas. An intervention programme of education and importing training at workplaces has shown a positive impact on the outcome of safety and security practices and safe, hygienic practices of food handlers at workplace.

Keywords: food, health, preschool children, safety, security

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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

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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

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

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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

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Authors: Gabriela Steffen

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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

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

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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

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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

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Authors: Salah Eddine Ben Fadhel

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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

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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

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Authors: Jyh Sheen, Yang-Hung Cheng

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This paper presents a design of a microstrip bandpass filter with a compact size and wide stopband by using 0.15-μm GaAs pHEMT process. The wide stop band is achieved by suppressing the first and second harmonic resonance frequencies. The slow-wave coupling stepped impedance resonator with cross coupled structure is adopted to design the bandpass filter. A two-resonator filter was fabricated with 13.5GHz center frequency and 11% bandwidth was achieved. The devices are simulated using the ADS design software. This device has shown a compact size and very low insertion loss of 2.6 dB. Microstrip planar bandpass filters have been widely adopted in various communication applications due to the attractive features of compact size and ease of fabricating. Various planar resonator structures have been suggested. In order to reach a wide stopband to reduce the interference outside the passing band, various designs of planar resonators have also been submitted to suppress the higher order harmonic frequencies of the designed center frequency. Various modifications to the traditional hairpin structure have been introduced to reduce large design area of hairpin designs. The stepped-impedance, slow-wave open-loop, and cross-coupled resonator structures have been studied to miniaturize the hairpin resonators. In this study, to suppress the spurious harmonic bands and further reduce the filter size, a modified hairpin-line bandpass filter with cross coupled structure is suggested by introducing the stepped impedance resonator design as well as the slow-wave open-loop resonator structure. In this way, very compact circuit size as well as very wide upper stopband can be achieved and realized in a Roger 4003C substrate. On the other hand, filters constructed with integrated circuit technology become more attractive for enabling the integration of the microwave system on a single chip (SOC). To examine the performance of this design structure at the integrated circuit, the filter is fabricated by the 0.15 μm pHEMT GaAs integrated circuit process. This pHEMT process can also provide a much better circuit performance for high frequency designs than those made on a PCB board. The design example was implemented in GaAs with center frequency at 13.5 GHz to examine the performance in higher frequency in detail. The occupied area is only about 1.09×0.97 mm2. The ADS software is used to design those modified filters to suppress the first and second harmonics.

Keywords: microstrip resonator, bandpass filter, harmonic suppression, GaAs

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Authors: Hossein Havaeji, Tony Wong, Thien-My Dao

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1. Research Problems and Research Objectives: Blockchain Technology-enabled Supply Chain System (BT-enabled SCS) is the system using BT to drive SCS transparency, security, durability, and process integrity as SCS data is not always visible, available, or trusted. The costs of operating BT in the SCS are a common problem in several organizations. The costs must be estimated as they can impact existing cost control strategies. To account for system and deployment costs, it is necessary to overcome the following hurdle. The problem is that the costs of developing and running a BT in SCS are not yet clear in most cases. Many industries aiming to use BT have special attention to the importance of BT installation cost which has a direct impact on the total costs of SCS. Predicting BT installation cost in SCS may help managers decide whether BT is to be an economic advantage. The purpose of the research is to identify some main BT installation cost components in SCS needed for deeper cost analysis. We then identify and categorize the main groups of cost components in more detail to utilize them in the prediction process. The second objective is to determine the suitable Supervised Learning technique in order to predict the costs of developing and running BT in SCS in a particular case study. The last aim is to investigate how the running BT cost can be involved in the total cost of SCS. 2. Work Performed: Applied successfully in various fields, Supervised Learning is a method to set the data frame, treat the data, and train/practice the method sort. It is a learning model directed to make predictions of an outcome measurement based on a set of unforeseen input data. The following steps must be conducted to search for the objectives of our subject. The first step is to make a literature review to identify the different cost components of BT installation in SCS. Based on the literature review, we should choose some Supervised Learning methods which are suitable for BT installation cost prediction in SCS. According to the literature review, some Supervised Learning algorithms which provide us with a powerful tool to classify BT installation components and predict BT installation cost are the Support Vector Regression (SVR) algorithm, Back Propagation (BP) neural network, and Artificial Neural Network (ANN). Choosing a case study to feed data into the models comes into the third step. Finally, we will propose the best predictive performance to find the minimum BT installation costs in SCS. 3. Expected Results and Conclusion: This study tends to propose a cost prediction of BT installation in SCS with the help of Supervised Learning algorithms. At first attempt, we will select a case study in the field of BT-enabled SCS, and then use some Supervised Learning algorithms to predict BT installation cost in SCS. We continue to find the best predictive performance for developing and running BT in SCS. Finally, the paper will be presented at the conference.

Keywords: blockchain technology, blockchain technology-enabled supply chain system, installation cost, supervised learning

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Authors: Karl Baker-Green, Ian Woolsey

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Historically, a variety of methods have been used to access the student voice within higher education, including module evaluations and informal classroom feedback. However, currently, the views articulated in student-staff-committee meetings bear the most weight and can therefore have the most significant impact on departmental policy. Arguably, these forums are exclusionary as several students, including those who experience severe anxiety, might feel unable to participate in this face-to-face (large) group activities. Similarly, students who declare a disability, but are not in possession of a learning contract, are more likely to withdraw from their studies than those whose additional needs have been formally recognised. It is also worth noting that whilst the number of disabled students in Higher Education has increased in recent years, the percentage of those who have been issued a learning contract has decreased. These issues foreground the need to explore the educational experiences of students with or without a learning contract in order to identify their respective aspirations and needs and therefore help shape education policy. This is in keeping with the ‘Nothing about us without us’, agenda, which recognises that disabled individuals are best placed to understand their own requirements and the most effective strategies to meet these.

Keywords: education, student voice, student experience, student retention

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Authors: Patricia Jiménez, Rafael Corchuelo

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Nowadays the World Wide Web is the most popular source of information that relies on billions of on-line documents. Web mining is used to crawl through these documents, collect the information of interest and process it by applying data mining tools in order to use the gathered information in the best interest of a business, what enables companies to promote theirs. Unfortunately, it is not easy to extract the information a web site provides automatically when it lacks an API that allows to transform the user-friendly data provided in web documents into a structured format that is machine-readable. Rule-based information extractors are the tools intended to extract the information of interest automatically and offer it in a structured format that allow mining tools to process it. However, the performance of an information extractor strongly depends on the search heuristic employed since bad choices regarding how to learn a rule may easily result in loss of effectiveness and/or efficiency. Improving search heuristics regarding efficiency is of uttermost importance in the field of Web Information Extraction since typical datasets are very large. In this paper, we employ an information extractor based on a classical top-down algorithm that uses the so-called Information Gain heuristic introduced by Quinlan and Cameron-Jones. Unfortunately, the Information Gain relies on some well-known problems so we analyse an intuitive alternative, Termini, that is clearly more efficient; we also analyse other proposals in the literature and conclude that none of them outperforms the previous alternative.

Keywords: information extraction, search heuristics, semi-structured documents, web mining.

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Authors: Mei-Hui Liu

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This paper reports the progression of language teachers’ learning to assess students’ speech act performance via online videos in a cross-tier professional growth community. This yearlong research project collected multiple data sources from several stakeholders, including 12 preservice and 4 inservice English as a foreign language (EFL) teachers, 4 English professionals, and 82 high school students. Data sources included surveys, (focus group) interviews, online reflection journals, online video-based assessment items/scores, and artifacts related to teacher professional learning. The major findings depicted the effectiveness of this proposed learning module on language teacher development in pragmatic assessment as well as its impact on student learning experience. All these teachers appreciated this professional learning experience which enhanced their knowledge in assessing students’ pragmalinguistic and sociopragmatic performance in an English speech act (i.e., making refusals). They learned how to design online video-based assessment items by attending to specific linguistic structures, semantic formula, and sociocultural issues. They further became aware of how to sharpen pragmatic instructional skills in the near future after putting theories into online assessment and related classroom practices. Additionally, data analysis revealed students’ achievement in and satisfaction with the designed online assessment. Yet, during the professional learning process most participating teachers encountered challenges in reaching a consensus on selecting appropriate video clips from available sources to present the sociocultural values in English-speaking refusal contexts. Also included was to construct test items which could testify the influence of interlanguage transfer on students’ pragmatic performance in various conversational scenarios. With pedagogical implications and research suggestions, this study adds to the increasing amount of research into integrating preservice and inservice EFL teacher education in pragmatic assessment and relevant instruction. Acknowledgment: This research project is sponsored by the Ministry of Science and Technology in the Republic of China under the grant number of MOST 106-2410-H-029-038.

Keywords: cross-tier professional development, inservice EFL teachers, pragmatic assessment, preservice EFL teachers, student learning experience

Procedia PDF Downloads 259