Search results for: adaptive educational digital learning environments

Authors: Nilgun Karatas, Bihter Sakin

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In this study, the relationships between the use of language learning strategies and English language exit exam success were investigated in the university EFL learners’ context. The study was conducted at Fatih University Prep School. To collect data 3 classes from the A1 module in English language classes completed a questionnaire known as the English Language Learning Strategy Inventory or ELLSI. The data for the present study were collected from the preparatory class students who are studying English as a second language at the School of Foreign Languages. The students were placed into four different levels of English, namely A1, A2, B1, and B2 level of English competency according to European Union Language Proficiency Standard, by means of their English placement test results. The Placement test was conveyed at the beginning of the spring semester in 2014-2015.The ELLSI consists of 30 strategy items which students are asked to rate from 1 (low frequency) to 5 (high frequency) according to how often they use them. The questionnaire and exit exam results were entered onto SPSS and analyzed for mean frequencies and statistical differences. Spearman and Pearson correlation were used in a detailed way. There were no statistically significant results between the frequency of strategy use and exit exam results. However, most questions correlate at a significant level with some of the questions.

Keywords: individual differences, language learning strategies, Fatih University, English language

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Authors: Greta Rizzi, Ashley Gallitto, Federica Vacca

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Looking at design artifacts as bearers and disseminators of material knowledge and intangible socio-cultural meanings, the significance of archival jewelry was investigated following digital cultural heritage research streams. The application of the reverse engineering concept guided the research path: starting with the study of Gianfranco Ferré's archival jewelry and analyzing its technical heritage and symbolic value, the digitalization, dematerialization, and rematerialization of the artifact were carried out. According to that, the proposed paper results from research conducted within the residency program between the Gianfranco Ferré Research Center (GFRC) and Massachusetts Institute of Technology (MIT), involving both the Design and Mechanical Engineering Departments of Politecnico di Milano. The paper will discuss the analysis of traditional design manufacturing techniques, re-imagined through 3D scanning, 3D modeling, and 3D printing technical knowledge while emphasizing the significance of the designer's role as an explorer of socio-cultural meanings and technological mediators in the analog-digital-analog transition.

Keywords: Archival jewelry, cultural heritage, rematerialization, reverse engineering.

Procedia PDF Downloads 45

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

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

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

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Authors: Xi Liu, Fan Yuan

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In the evolving landscape of physics-based machine learning (PBML), particularly within fluid dynamics and its applications in electromechanical engineering, robot vision, and robot learning, achieving precision and alignment with researchers' specific needs presents a formidable challenge. In response, this work proposes a methodology that integrates neural transformation with a modified smoothed particle hydrodynamics model for generating transformed 3D fluid simulations. This approach is useful for nanoscale science, where the unique and complex behaviors of viscoelastic medium demand accurate neurally-transformed simulations for materials understanding and manipulation. In electromechanical engineering, the method enhances the design and functionality of fluid-operated systems, particularly microfluidic devices, contributing to advancements in nanomaterial design, drug delivery systems, and more. The proposed approach also aligns with the principles of PBML, offering advantages such as multi-fluid stylization and consistent particle attribute transfer. This capability is valuable in various fields where the interaction of multiple fluid components is significant. Moreover, the application of neurally-transformed hydrodynamical models extends to manufacturing processes, such as the production of microelectromechanical systems, enhancing efficiency and cost-effectiveness. The system's ability to perform neural transfer on 3D fluid scenes using a deep learning algorithm alongside physical models further adds a layer of flexibility, allowing researchers to tailor simulations to specific needs across scientific and engineering disciplines.

Keywords: physics-based machine learning, robot vision, robot learning, hydrodynamics

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Authors: Shreeyam, Ranjan Kumar Sah, Shivangi

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Recognizing and solving handwritten mathematical expressions can be a challenging task, particularly when certain characters are segmented and classified. This project proposes a solution that uses Convolutional Neural Network (CNN) and image processing techniques to accurately solve various types of equations, including arithmetic, quadratic, and trigonometric equations, as well as logical operations like logical AND, OR, NOT, NAND, XOR, and NOR. The proposed solution also provides a graphical solution, allowing users to visualize equations and their solutions. In addition to equation solving, the platform, called CNNCalc, offers a comprehensive learning experience for students. It provides educational content, a quiz platform, and a coding platform for practicing programming skills in different languages like C, Python, and Java. This all-in-one solution makes the learning process engaging and enjoyable for students. The proposed methodology includes horizontal compact projection analysis and survey for segmentation and binarization, as well as connected component analysis and integrated connected component analysis for character classification. The compact projection algorithm compresses the horizontal projections to remove noise and obtain a clearer image, contributing to the accuracy of character segmentation. Experimental results demonstrate the effectiveness of the proposed solution in solving a wide range of mathematical equations. CNNCalc provides a powerful and user-friendly platform for solving equations, learning, and practicing programming skills. With its comprehensive features and accurate results, CNNCalc is poised to revolutionize the way students learn and solve mathematical equations. The platform utilizes a custom-designed Convolutional Neural Network (CNN) with image processing techniques to accurately recognize and classify symbols within handwritten equations. The compact projection algorithm effectively removes noise from horizontal projections, leading to clearer images and improved character segmentation. Experimental results demonstrate the accuracy and effectiveness of the proposed solution in solving a wide range of equations, including arithmetic, quadratic, trigonometric, and logical operations. CNNCalc features a user-friendly interface with a graphical representation of equations being solved, making it an interactive and engaging learning experience for users. The platform also includes tutorials, testing capabilities, and programming features in languages such as C, Python, and Java. Users can track their progress and work towards improving their skills. CNNCalc is poised to revolutionize the way students learn and solve mathematical equations with its comprehensive features and accurate results.

Keywords: AL, ML, hand written equation solver, maths, computer, CNNCalc, convolutional neural networks

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Authors: Sameh A. S. Thabit Alariqi

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Our previous studies aimed to investigate the biodegradation of γ-sterilized polyolefins in composting and microbial culture environments at different doses and γ-dose rates. It was concluded from the previous studies that the pretreatment of γ-irradiation can accelerate the biodegradation of neat polymer matrix in biotic conditions significantly. A similar work was carried out to study the stabilization of γ-sterilized polyolefins using different mixtures of stabilizers which are approved for food-contact applications. Ethylene-propylene (EP) copolymer has been melt-mixed with hindered amine stabilizers (HAS), phenolic antioxidants and hydroperoxide decomposers. Results were discussed by comparing the stabilizing efficiency, combination and consumption of stabilizers and the synergistic and antagonistic effects was explained through the interaction between the stabilizers. In this attempt, we have aimed to study the influence of the synergistic and antagonistic mixtures of oligomeric stabilizers on the biodegradation of the γ-irradiated polyolefins in composting and microbial culture. Neat and stabilized films of EP copolymer irradiated under γ-radiation and incubated in compost and fungal culture environments. The changes in functional groups, surface morphology, mechanical properties and intrinsic viscosity in polymer chains were characterized by FT-IR spectroscopy, SEM, instron, and viscometric measurements respectively. Results were discussed by comparing the effect of different stabilizers, stabilizers mixtures on the biodegradation of the γ-irradiated polyolefins. It was found that the biodegradation significantly depends on the components of stabilization system, mobility, interaction, and consumption of stabilizers.

Keywords: biodegradation, γ-irradiation, polyolefins, stabilization

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Authors: Zahra Alikhani Koopaei

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In the fifth lesson of the third-grade mathematics book, students are introduced to the concept of multiplication. However, some students showed a lack of interest in learning this topic. To address this, a simple electronic multiplication table was designed with the aim of making the concept of multiplication entertaining and engaging for students. It provides them with moments of excitement during the learning process. To achieve this goal, a device was created that produced a bell sound when two wire ends were connected. Each wire end was connected to a specific number in the multiplication table, and the other end was linked to the corresponding answer. Consequently, if the answer is correct, the bell will ring. This study employs interactive and engaging methods to teach mathematics, particularly to students who have previously shown little interest in the subject. By integrating game-based learning and critical thinking, we observed an increase in understanding and interest in learning multiplication compared to before using this method. This further motivated the students. As a result, the intelligent multiplication table was successfully designed. Students, under the instructor's supervision, could easily construct the device during the lesson. Through the implementation of these operations, the concept of multiplication was firmly established in the students' minds. Engaging multiple intelligences in each student enhances a more stable and improved understanding of the concept of multiplication.

Keywords: intelligent multiplication table, design, construction, education, increased interest, students

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Authors: J. Mano Ranjini

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Each and every child is special, born with a unique talent to explore this world. The word Dyslexia is derived from the Greek language in which “dys” meaning poor or inadequate and “lexis” meaning words or language. Dyslexia describes about a different kind of mind, which is often gifted and productive, that learns the concept differently. The main aim of the study is to bring the positive outcome of the reading level by examining the effectiveness of Visual Auditory Kinesthetic Tactile technique on Reading Level among Dyslexic Children at Helikx Open School and Learning Centre. A Quasi experimental one group pretest post test design was adopted for this study. The Reading Level was assessed by using the Schonell Graded Word Reading Test. Thirty subjects were drawn by using purposive sampling technique and the intervention Visual Auditory Kinesthetic Tactile technique was implemented to the Dyslexic Children for 30 consecutive days followed by the post Reading Level assessment revealed the improvement in the mean score value of reading level by 12%. Multi-sensory (VAKT) teaching uses all learning pathways in the brain (visual, auditory, kinesthetic-tactile) in order to enhance memory and learning and the ability in uplifting emotional, physical and societal dimensions. VAKT is an effective method to improve the reading skill of the Dyslexic Children that ensures the enormous significance of learning thereby influencing the wholesome of the child’s life.

Keywords: visual auditory kinesthetic tactile technique, reading level, dyslexic children, Helikx Open School

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Authors: Suraj Gururaj, Sumantha Udupa U.

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Browsing the Web has emerged as the de facto activity performed on the Internet. Although browsing gets tracked, the manageability aspect of Web browsing history is very poor. In this paper, we have a workable solution implemented by using machine learning and natural language processing techniques for efficient manageability of user’s browsing history. The significance of adding such a capability to a Web browser is that it ensures efficient and quick information retrieval from browsing history, which currently is very challenging. Our solution guarantees that any important websites visited in the past can be easily accessible because of the intelligent and automatic classification. In a nutshell, our solution-based paper provides an implementation as a browser extension by intelligently classifying the browsing history into most relevant category automatically without any user’s intervention. This guarantees no information is lost and increases productivity by saving time spent revisiting websites that were of much importance.

Keywords: adhoc retrieval, Chrome extension, supervised learning, tile, Web personalization

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Authors: A. Alshabeb

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Vocabulary is one of the elements that links the four skills of reading, writing, speaking, and listening and is very critical in learning a foreign language. This study aims to determine how Saudi Arabian EFL students learn English vocabulary via Twitter. The study adopts a mixed sequential research design in collecting and analysing data. The results of the study provide several recommendations for vocabulary learning. Moreover, the study can help teachers to consider the possibilities of using Twitter further, and perhaps to develop new approaches to vocabulary teaching and to support students in their use of social media.

Keywords: social media, twitter, vocabulary, web 2

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Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

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The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

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Authors: Priyajeet Kaur Kaleka

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Introduction: A dentist is often the first responder in the battle for a patient’s healthy body and maybe the first health professional to observe signs of child abuse, be it physical, emotional, and/or sexual mistreatment. Therefore, it is an ethical responsibility for the dental clinician to detect and report suspected cases of child abuse and neglect (CAN). The main reasons for not reporting suspected cases of CAN, with special emphasis on the third: 1) Uncertainty of the diagnosis, 2) Lack of knowledge of the reporting procedure, and 3) Child abuse and neglect somewhat remained the subject of ignorance among dental professionals because of a lack of advance clinical training. Given these epidemic proportions, there is a scope of further research about dental school curriculum design. Purpose: This study aimed to assess the knowledge and attitude of dentists in Canada regarding signs and symptoms of child abuse and neglect (CAN), reporting procedures, and whether educational strategies followed by dental schools address this sensitive issue. In pursuit of that aim, this abstract summarizes the evidence related to this question. Materials and Methods: Data was collected through a specially designed questionnaire adapted and modified from the author’s previous cross-sectional study on (CAN), which was conducted in Pune, India, in 2016 and is available on the database of PubMed. Design: A random sample was drawn from the targeted population of registered dentists and dental students in Canada regarding their knowledge, professional responsibilities, and behavior concerning child abuse. Questionnaire data were distributed to 200 members. Out of which, a total number of 157 subjects were in the final sample for statistical analysis, yielding response of 78.5%. Results: Despite having theoretical information on signs and symptoms, 55% of the participants indicated they are not confident to detect child physical abuse cases. 90% of respondents believed that recognition and handling the CAN cases should be a part of undergraduate training. Only 4.5% of the participants have correctly identified all signs of abuse due to inadequate formal training in dental schools and workplaces. Although nearly 96.3% agreed that it is a dentist’s legal responsibility to report CAN, only a small percentage of the participants reported an abuse case in the past. While 72% stated that the most common factor that might prevent a dentist from reporting a case was doubt over the diagnosis. Conclusion: The goal is to motivate dental schools to deal with this critical issue and provide their students with consummate training to strengthen their capability to care for and protect children. The educational institutions should make efforts to spread awareness among dental students regarding the management and tackling of CAN. Clinical Significance: There should be modifications in the dental school curriculum focusing on problem-based learning models to assist graduates to fulfill their legal and professional responsibilities. CAN literacy should be incorporated into the dental curriculum, which will eventually benefit future dentists to break this intergenerational cycle of violence.

Keywords: abuse, child abuse and neglect, dentist knowledge, dental school curriculum, problem-based learning

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Authors: Walter M. Millanzi, Stephen M. Kibusi

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Background: Currently, there has been a progressive shortage not only to the number but also the quality of medical practitioners for the most of nursing. Despite that, those who are present exhibit unethical and illegal practices, under standard care and malpractices. The concern is raised in the ways they are prepared, or there might be something missing in nursing curricula or how it is delivered. There is a need for transforming or testing new teaching modalities to enhance competent health workforces. Objective: to investigate the Effect of Facilitation in a Problem-based Environment (FPBE) on metacognition, self-directed learning and learning motivation to undergraduate nurse student in Tanzanian higher learning institutions. Methods: quasi-experimental study (quantitative research approach). A purposive sampling technique was employed to select institutions and achieving a sample size of 401 participants (interventional = 134 and control = 267). Self-administered semi-structured questionnaire; was the main data collection methods and the Statistical Package for Service Solution (v. 20) software program was used for data entry, data analysis, and presentations. Results: The pre-post test results between groups indicated noticeably significant change on metacognition in an intervention (M = 1.52, SD = 0.501) against the control (M = 1.40, SD = 0.490), t (399) = 2.398, p < 0.05). SDL in an intervention (M = 1.52, SD = 0.501) against the control (M = 1.40, SD = 0.490), t (399) = 2.398, p < 0.05. Motivation to learn in an intervention (M = 62.67, SD = 14.14) and the control (n = 267, M = 57.75), t (399) = 2.907, p < 0.01). A FPBE teaching pedagogy, was observed to be effective on the metacognition (AOR = 1.603, p < 0.05), SDL (OR = 1.729, p < 0.05) and Intrinsic motivation in learning (AOR = 1.720, p < 0.05) against conventional teaching pedagogy. Needless, was less likely to enhance Extrinsic motivation (AOR = 0.676, p > 0.05) and Amotivation (AOR = 0.538, p > 0.05). Conclusion and recommendation: FPBE teaching pedagogy, can improve student’s metacognition, self-directed learning and intrinsic motivation to learn among nurse students. Nursing curricula developers should incorporate it to produce 21st century competent and qualified nurses.

Keywords: facilitation, metacognition, motivation, self-directed

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Authors: Angela Meyer

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The operation and maintenance cost for wind parks make up a major fraction of the park’s overall lifetime cost. To minimize the cost and risk involved, an optimal operation and maintenance strategy requires continuous monitoring and analysis. In order to facilitate this, we present a decision support system that automatically scans the stream of telemetry sensor data generated from the turbines. By learning decision boundaries and normal reference operating states using machine learning algorithms, the decision support system can detect anomalous operating behavior in individual wind turbines and diagnose the involved turbine sub-systems. Operating personal can be alerted if a normal operating state boundary is exceeded. The presented decision support system and method are applicable for any turbine type and manufacturer providing telemetry data of the turbine operating state. We demonstrate the successful detection and diagnosis of anomalous operating states in a case study at a German onshore wind park comprised of Vestas V112 turbines.

Keywords: anomaly detection, decision support, machine learning, monitoring, performance optimization, wind turbines

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Authors: Bruno Pinto Vieira, Marco Antonio Calijorne Soares, Armando Sérgio de Aguiar Filho

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Nowadays, a massive amount of information has been produced by different data sources, including mobile devices and transactional systems. In this scenario, concerns arise on how to maintain or establish data quality, which is now treated as a product to be defined, measured, analyzed, and improved to meet consumers' needs, which is the one who uses these data in decision making and companies strategies. Information that reaches low levels of quality can lead to issues that can consume time and money, such as missed business opportunities, inadequate decisions, and bad risk management actions. The step of selecting, identifying, evaluating, and selecting data sources with significant quality according to the need has become a costly task for users since the sources do not provide information about their quality. Traditional data quality control methods are based on user experience or business rules limiting performance and slowing down the process with less than desirable accuracy. Using advanced machine learning algorithms, it is possible to take advantage of computational resources to overcome challenges and add value to companies and users. In this study, machine learning is applied to data quality analysis on different datasets, seeking to compare the performance of the techniques according to the dimensions of quality assessment. As a result, we could create a ranking of approaches used, besides a system that is able to carry out automatically, data quality assessment.

Keywords: machine learning, data quality, quality dimension, quality assessment

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Authors: Mohammed Kafaji

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This paper aims to investigate the influence of quality of education and quality of research, provided by local educational institutions, on the adoption of Information and Communication Technology (ICT) in managing business operations for companies in Saudi market. A model was developed and tested using data collected from 138 CEO’s of foreign companies in diverse business sectors. The data is analysed and managed using multivariate approaches through standard statistical packages. The results showed that educational quality has little contribution to the ICT adoption while research quality seems to play a more prominent role. These results are analysed in terms of business environment and market constraints and further extended to the perceived effectiveness of applied pedagogical approaches in schools and universities.

Keywords: quality of education, quality of research, mediation, domestic competition, ICT adoption

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Authors: Murat Demir Aydin, Elanur Celebi

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Adhesively bonded joints are used as an alternative to traditional joining methods due to the important advantages they provide. The most important consideration in the use of adhesively bonded joints is that these joints have appropriate requirements for their use in terms of safety. In order to ensure control of this condition, damage analysis of the adhesively bonded joints should be performed by determining the mechanical properties of the adhesives. When the literature is investigated; it is generally seen that the mechanical properties of adhesives are determined by traditional measurement methods. In this study, to determine the mechanical properties of adhesives, the Digital Image Correlation (DIC) method, which can be an alternative to traditional measurement methods, has been used. The DIC method is a new optical measurement method which is used to determine the parameters of displacement and strain in an appropriate and correct way. In this study, tensile tests of Thick Adherent Shear Test (TAST) samples formed using DP410 liquid structural adhesive and steel materials and bulk tensile specimens formed using and DP410 liquid structural adhesive was performed. The displacement and strain values of the samples were determined by DIC method and the shear stress-strain curves of the adhesive for TAST specimens and the tensile strain curves of the bulk adhesive specimens were obtained. Various methods such as numerical methods are required as conventional measurement methods (strain gauge, mechanic extensometer, etc.) are not sufficient in determining the strain and displacement values of the very thin adhesive layer such as TAST samples. As a result, the DIC method removes these requirements and easily achieves displacement measurements with sufficient accuracy.

Keywords: structural adhesive, adhesively bonded joints, digital image correlation, thick adhered shear test (TAST)

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Authors: Htay Htay Yi, Zin May Aye

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Network security is role of the ICT environment because malicious users are continually growing that realm of education, business, and then related with ICT. The network security contravention is typically described and examined centrally based on a security event management system. The firewalls, Intrusion Detection System (IDS), and Intrusion Prevention System are becoming essential to monitor or prevent of potential violations, incidents attack, and imminent threats. In this system, the firewall rules are set only for where the system policies are needed. Dataset deployed in this system are derived from the testbed environment. The traffic as in DoS and PortScan traffics are applied in the testbed with firewall and IDS implementation. The network traffics are classified as normal or attacks in the existing testbed environment based on six machine learning classification methods applied in the system. It is required to be tested to get datasets and applied for DoS and PortScan. The dataset is based on CICIDS2017 and some features have been added. This system tested 26 features from the applied dataset. The system is to reduce false positive rates and to improve accuracy in the implemented testbed design. The system also proves good performance by selecting important features and comparing existing a dataset by machine learning classifiers.

Keywords: false negative rate, intrusion detection system, machine learning methods, performance

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Authors: Cory A. Logston

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It is imperative researchers continue to explore every transformative strategy to increase empathy and awareness of racial bias. Racism is a social and political concept that uses stereotypical ideology to highlight racial inequities. Everyone has biases they may not be aware of toward disparate out-groups. There is some form of racism in every profession; doctors, lawyers, and teachers are not immune. There have been numerous successful and unsuccessful strategies to motivate and transform an individual’s unconscious biased attitudes. One method designed to induce a transformative experience and identify implicit bias is virtual reality (VR). VR is a technology designed to transport the user to a three-dimensional environment. In a virtual reality simulation, the viewer is immersed in a realistic interactive video taking on the perspective of a Black man. The viewer as the character experiences discrimination in various life circumstances growing up as a child into adulthood. For instance, the prejudice felt in school, as an adolescent encountering the police and false accusations in the workplace. Current research suggests that an immersive VR simulation can enhance self-awareness and become a transformative learning experience. This study uses virtual reality immersion and transformative learning theory to create empathy and identify any unintentional racial bias. Participants, White teachers, will experience a VR immersion to create awareness and identify implicit biases regarding Black students. The desired outcome provides a springboard to reconceptualize their own implicit bias. Virtual reality is gaining traction in the research world and promises to be an effective tool in the transformative learning process.

Keywords: empathy, implicit bias, transformative learning, virtual reality

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Authors: Kim Bower

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Evaluating and researching motivation in language learning is a complex and multi-faceted activity. Various models for investigating learner motivation have been proposed in the literature, but no one model supplies a complex and coherent model for investigating a range of motivational characteristics. Here, such a methodological framework, which includes exemplification of sources of evidence and potential methods of investigation, is proposed. The process model for the investigation of motivation within language learning settings proposed is based on a complex dynamic systems perspective that takes account of cognition and affects. It focuses on three overarching aspects of motivation: the learning environment, learner engagement and learner identities. Within these categories subsets are defined: the learning environment incorporates teacher, course and group specific aspects of motivation; learner engagement addresses the principal characteristics of learners' perceived value of activities, their attitudes towards language learning, their perceptions of their learning and engagement in learning tasks; and within learner identities, principal characteristics of self-concept and mastery of the language are explored. Exemplifications of potential sources of evidence in the model reflect the multiple influences within and between learner and environmental factors and the possible changes in both that may emerge over time. The model was initially developed as a framework for investigating different models of Content and Language Integrated Learning (CLIL) in contrasting contexts in secondary schools in England. The study, from which examples are drawn to exemplify the model, aimed to address the following three research questions: (1) in what ways does CLIL impact on learner motivation? (2) what are the main elements of CLIL that enhance motivation? and (3) to what extent might these be transferable to other contexts? This new model has been tried and tested in three locations in England and reported as case studies. Following an initial visit to each institution to discuss the qualitative research, instruments were developed according to the proposed model. A questionnaire was drawn up and completed by one group prior to a 3-day data collection visit to each institution, during which interviews were held with academic leaders, the head of the department, the CLIL teacher(s), and two learner focus groups of six-eight learners. Interviews were recorded and transcribed verbatim. 2-4 naturalistic observations of lessons were undertaken in each setting, as appropriate to the context, to provide colour and thereby a richer picture. Findings were subjected to an interpretive analysis by the themes derived from the process model and are reported elsewhere. The model proved to be an effective and coherent framework for planning the research, instrument design, data collection and interpretive analysis of data in these three contrasting settings, in which different models of language learning were in place. It is hoped that the proposed model, reported here together with exemplification and commentary, will enable teachers and researchers in a wide range of language learning contexts to investigate learner motivation in a systematic and in-depth manner.

Keywords: investigate, language-learning, learner motivation model, dynamic systems perspective

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Authors: Faride Hashemiannejad, Shima Oloomi

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Imagination is one of the important elements of creative thinking which as a skill needs attention by the educational system. Although most students learn reading, writing, and arithmetic skills well, they lack high level thinking skills like creative thinking. Therefore, in the information age and in the beginning of entry to knowledge-based society, the educational system needs to think over its goals and mission, and concentrate on creativity-based curriculum. From among curriculum elements-goals, content, method and evaluation “method” is a major domain whose reform can pave the way for fostering imagination and creativity. The purpose of this study was examining the relationship between creativity development and curriculum. Research questions were: (1) is there a relationship between the cognitive-emotional structure of the classroom and creativity development? (2) Is there a relationship between the environmental-social structure of the classroom and creativity development? (3) Is there a relationship between the thinking structure of the classroom and creativity development? (4) Is there a relationship between the physical structure of the classroom and creativity development? (5) Is there a relationship between the instructional structure of the classroom and creativity development? Method: This research is a applied research and the research method is Correlational research. Participants: The total number of participants in this study included 894 students from High school through 11th grade from seven schools of seven zones in Mashad city. Sampling Plan: Sampling was selected based on Random Multi State. Measurement: The dependent measure in this study was: (a) the Test of Creative Thinking, (b) The researcher-made questionnaire includes five fragments, cognitive, emotional structure, environmental social structure, thinking structure, physical structure, and instructional structure. The Results Show: There was significant relationship between the cognitive-emotional structure of the classroom and student’s creativity development (sig=0.139). There was significant relationship between the environmental-social structure of the classroom and student’s creativity development (sig=0.006). There was significant relationship between the thinking structure of the classroom and student’s creativity development (sig=0.004). There was not significant relationship between the physical structure of the classroom and student’s creativity development (sig=0.215). There was significant relationship between the instructional structure of the classroom and student’s creativity development (sig=0.003). These findings denote if students feel secure, calm and confident, they can experience creative learning. Also the quality of coping with students’ questions, imaginations and risks can influence on their creativity development.

Keywords: imagination, creativity, curriculum, bioinformatics, biomedicine

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Authors: Kayode E. Oyetade, Seraphin D. Eyono Obono

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Age ratings are very helpful in providing parents with relevant information for the purchase and use of digital technologies by the children; this is why the non-definition of age ratings for the use of ICT's by children in schools is a major concern; and this problem serves as a motivation for this study whose aim is to examine the factors affecting the perceptions of educators on the learners’ youngest age for the introduction of ICT's in schools. This aim is achieved through two types of research objectives: the identification and design of theories and models on age ratings, and the empirical testing of such theories and models in a survey of educators from the Camperdown district of the South African KwaZulu-Natal province. A questionnaire is used for the collection of the data of this survey whose validity and reliability is checked in SPSS prior to its descriptive and correlative quantitative analysis. The main hypothesis supporting this research is the association between the demographics of educators, their personality, and their perceptions on the learners’ youngest age for the introduction of ICT's in schools; as claimed by existing research; except that the present study looks at personality from three dimensions: self-actualized personalities, fully functioning personalities, and healthy personalities. This hypothesis was fully confirmed by the empirical study conducted by this research except for the demographic factor where only the educators’ grade or class was found to be associated with the personality of educators.

Keywords: age ratings, educators, e-learning, personality theories

Procedia PDF Downloads 229

Authors: Farzaneh Badinlou, Reza Kormi-Nouri, Monika Knopf, Kamal Kharazi

Abstract:

Research into long-term memory has demonstrated that the richness of the knowledge base cues in memory tasks improves retrieval process, which in turn influences learning and memory performance. The present research investigated the idea that adding cues connected to knowledge can affect memory performance in the context of action memory in children. In action memory studies, participants are instructed to learn a series of verb–object phrases as verbal learning and experience-based learning (learning by doing and learning by observation). It is well established that executing action phrases is a more memorable way to learn than verbally repeating the phrases, a finding called enactment effect. In the present study, a total of 410 students from four grade groups—2nd, 4th, 6th, and 8th—participated in this study. During the study, participants listened to verbal action phrases (VTs), performed the phrases (SPTs: subject-performed tasks), and observed the experimenter perform the phrases (EPTs: experimenter-performed tasks). During the test phase, cued recall test was administered. Semantic relational cues (i.e., well-integrated vs. poorly integrated items) were manipulated in the present study. In that, the participants were presented two lists of action phrases with high semantic integration between verb and noun, e.g., “write with the pen” and with low semantic integration between verb and noun, e.g., “pick up the glass”. Results revealed that experience-based learning had a better results than verbal learning for both well-integrated and poorly integrated items, though manipulations of semantic relational cues can moderate the enactment effect. In addition, children of different grade groups outperformed for well- than poorly integrated items, in flavour of older children. The results were discussed in relation to the effect of knowledge-based information in facilitating retrieval process in children.

Keywords: action memory, enactment effect, knowledge-based cues, school-aged children, semantic relational cues

Procedia PDF Downloads 272

Authors: Wen Chen

Abstract:

To investigate the interleaving effect of subject matter (mathematics vs. history) and perceptual modality (visual vs. auditory materials) on student’s attention and learning outcomes, the present study collected self-reported data on subjective cognitive load (SCL) and attention level, EEG data, and learning outcomes from micro-lectures. Eighty-one 7th grade students were randomly assigned to four learning conditions: blocked (by subject matter) micro-lectures with auditory textual information (B-A condition), blocked (by subject matter) micro-lectures with visual textual information (B-V condition), interleaved (by subject matter) micro-lectures with auditory textual information (I-A condition), and interleaved micro-lectures by both perceptual modality and subject matter (I-all condition). The results showed that although interleaved conditions may show advantages in certain indices, the I-all condition showed the best overall outcomes (best performance, low SCL, and high attention). This study suggests that interleaving by both subject matter and perceptual modality should be preferred in scheduling and planning classes.

Keywords: cognitive load, interleaving effect, micro-lectures, sustained attention

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Authors: Furkan Ozkan, M. Selcuk Arslan, Hatice Mercan

Abstract:

Electric vehicles are becoming increasingly popular asa sustainable alternative to traditional combustion engine vehicles. However, to fully realize the potential of EVs in reducing environmental impact and energy consumption, efficient control strategies are essential. This study explores the application of green wave control using model predictive control for electric vehicles, coupled with energy consumption modeling using neural networks. The use of MPC allows for real-time optimization of the vehicles’ energy consumption while considering dynamic traffic conditions. By leveraging neural networks for energy consumption modeling, the EV's performance can be further enhanced through accurate predictions and adaptive control. The integration of these advanced control and modeling techniques aims to maximize energy efficiency and range while navigating urban traffic scenarios. The findings of this research offer valuable insights into the potential of green wave control for electric vehicles and demonstrate the significance of integrating MPC and neural network modeling for optimizing energy consumption. This work contributes to the advancement of sustainable transportation systems and the widespread adoption of electric vehicles. To evaluate the effectiveness of the green wave control strategy in real-world urban environments, extensive simulations were conducted using a high-fidelity vehicle model and realistic traffic scenarios. The results indicate that the integration of model predictive control and energy consumption modeling with neural networks had a significant impact on the energy efficiency and range of electric vehicles. Through the use of MPC, the electric vehicle was able to adapt its speed and acceleration profile in realtime to optimize energy consumption while maintaining travel time objectives. The neural network-based energy consumption modeling provided accurate predictions, enabling the vehicle to anticipate and respond to variations in traffic flow, further enhancing energy efficiency and range. Furthermore, the study revealed that the green wave control strategy not only reduced energy consumption but also improved the overall driving experience by minimizing abrupt acceleration and deceleration, leading to a smoother and more comfortable ride for passengers. These results demonstrate the potential for green wave control to revolutionize urban transportation by enhancing the performance of electric vehicles and contributing to a more sustainable and efficient mobility ecosystem.

Keywords: electric vehicles, energy efficiency, green wave control, model predictive control, neural networks

Procedia PDF Downloads 50

Authors: Stepan Grebeniuk, Ersi Hodo, Henri Ruotsalainen, Paul Tavolato

Abstract:

Substation security plays an important role in the power delivery system. During the past years, there has been an increase in number of attacks on automation networks of the substations. In spite of that, there hasn’t been enough focus dedicated to the protection of such networks. Aiming to design a specialized anomaly detection system based on machine learning, in this paper we will discuss the IEC 60870-5-104 protocol that is used for communication between substation and control station and focus on the simulation of the substation traffic. Firstly, we will simulate the communication between substation slave and server. Secondly, we will compare the system's normal behavior and its behavior under the attack, in order to extract the right features which will be needed for building an anomaly detection system. Lastly, based on the features we will suggest the anomaly detection system for the asynchronous protocol IEC 60870-5-104.

Keywords: Anomaly detection, IEC-60870-5-104, Machine learning, Man-in-the-Middle attacks, Substation security

Procedia PDF Downloads 360

Authors: Guebli Abdelkader, Reguieg Madani, Belkadi Adel, Sbaa Bouabdellah

Abstract:

The identification of mechanical variables in the motor performance trajectory has a prominent role in improving skill performance, error-exceeding, it contributes seriously to solving some problems of learning and training. The study aims to highlight the indicators of motor performance for Paralympic athletes during the practicing sports between modelling and between excellence in motor performance, this by taking into account the distinction of athlete practicing with special behavioral skills for the Paralympic athletes. In the study, we relied on the analysis of some previous research of biomechanical performance indicators during some of the events sports (shooting activities in the Paralympic athletics, shooting skill in the wheelchair basketball). The results of the study highlight the distinction of disabled practitioners of sporting events identified in motor performance during practice, by overcoming some physics indicators in human movement, as a lower center of body weight, increase in offset distance, such resistance which requires them to redouble their efforts. However, the results of the study highlighted the strength of the correlation between biomechanical variables of motor performance and the digital level achievement similar to the other practitioners normal.

Keywords: sports, the disabled, motor performance, Paralympic

Procedia PDF Downloads 273

Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

Abstract:

This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

Procedia PDF Downloads 475

Authors: David Chapola Nggada

Abstract:

Although the concept of terrorism is not a new phenomenon to Nigeria, the protracted terrorist activities experiencing in the north-eastern part of the country since 2009, had left an indelible mark on virtually every aspects of lives whether directly or indirectly, particularly the educational sector. Indeed, since the abduction of over 200 schoolgirls at Chibok in 2014 by the insurgence, education had witnessed a setback as most school remains closed for sometimes. The aftermath of this development on education and its future multiplier effect on national development is a source of concern. Consequently, this paper is designed to examine the consequences of terrorism on educational attainment and national development among the Chibok community of Borno State. The technique employed involves a mixture of both qualitative and quantitative research work on a sample size of 79 secondary school students currently displaced from Chibok, Damboa and Askira-Uba, now residing as internally displaced persons(IDPs) in Biu, Gombe, Maiduguri. A random sample technique is used. Structured and semi-unstructured questionnaire were administered. The result shows that, a significant number of students over these years, lacked access to education and this posed a great danger to national development. Recommendations towards reinvigorating education as a panacea to social, economic cum political vices were articulated. Concerted effort should be made to create confidence in the community.

Keywords: education, effect, terrorism, national, development

Procedia PDF Downloads 257