Search results for: learning theory

Authors: Jianlin Wang, Jiajia Zhao

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This paper applies the theory and empirical method to examine the relationship between electricity price and coal price, as well as electricity and industry output, for China during Jan 1999-Dec 2012. Our results indicate that there is no any causality between coal price and electricity price under other factors are controlled. However, we found a bi-directional causality between electricity consumption and industry output. Overall, the electricity price set by China’s NDRC is inefficient, which lead to the electricity supply shortage after 2004. It is time to reform electricity price system for China’s reformers.

Keywords: electricity price, coal price, power supply, China

Procedia PDF Downloads 465

Authors: Ruchika Malhotra, Megha Khanna

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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics

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Authors: Karthik Mittal

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This study performs an unsupervised machine learning analysis to find clusters of related SNPs which highlight biological pathways that are important for the biological mechanisms of breast cancer. Studying genetic variations in isolation is illogical because these genetic variations are known to modulate protein production and function; the downstream effects of these modifications on biological outcomes are highly interconnected. After extracting the SNPs and their effect on different types of breast cancer using the MRBase library, two unsupervised machine learning clustering algorithms were implemented on the genetic variants: a k-means clustering algorithm and a hierarchical clustering algorithm; furthermore, principal component analysis was executed to visually represent the data. These algorithms specifically used the SNP’s beta value on the three different types of breast cancer tested in this project (estrogen-receptor positive breast cancer, estrogen-receptor negative breast cancer, and breast cancer in general) to perform this clustering. Two significant genetic pathways validated the clustering produced by this project: the MAPK signaling pathway and the connection between the BRCA2 gene and the ESR1 gene. This study provides the first proof of concept showing the importance of unsupervised machine learning in interpreting GWAS summary statistics.

Keywords: breast cancer, computational biology, unsupervised machine learning, k-means, PCA

Procedia PDF Downloads 141

Authors: Tory Erickson

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The "Bidirectional Spacetime with Spatial Covariance and Wave-Particle Duality in Spacetime Flow" (BST-SCWPDF) Model introduces a framework aimed at unifying general relativity (GR) and quantum mechanics (QM). By proposing a concept of bidirectional spacetime, this model suggests that time can flow in more than one direction, thus offering a perspective on temporal dynamics. Integrated with spatial covariance and wave-particle duality in spacetime flow, the BST-SCWPDF Model resolves long-standing discrepancies between GR and QM. This unified theory has profound implications for quantum gravity, potentially offering insights into quantum entanglement, the collapse of the wave function, and the fabric of spacetime itself. The Bidirectional Spacetime with Spatial Covariance and Wave-Particle Duality in Spacetime Flow" (BST-SCWPDF) Model offers researchers a framework for a better understanding of theoretical physics.

Keywords: astrophysics, quantum mechanics, general relativity, unification theory, theoretical physics

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Authors: Soufiane Zerraf, Malika Tridane, Said Belaaouad

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CuHPO₃.2H₂O was synthesized by the direct method. CuHPO₃.2H₂O crystallizes in the orthorhombic system, space group P2₁2₁2₁, a = 6.7036 (2) Å, b = 7.3671 (4) Å, c = 8.9749 (4) Å, Z = 4, V = 443.24 (4) ų. The crystal structure was refined to R₁= 0.0154, R₂= 0.0380 for 19018 reflections satisfying criterion I ≥ 2σ (I). The structural resolution shows the existence of chains of ions HPO₃- linked together by hydrogen bonds. The crystalline structure is formed by chains consisting of Cu[O₃(H₂O)₃] deformed octahedral, which are connected to the vertices. The chains extend parallel to b and are mutually linked by PO₃ groups. The structure is closely related to that of CuSeO₃.2H₂O and CuTeO₃.2H₂O. The experimental studies of the infrared and Raman spectra were used to confirm the presence of the phosphate ion and were compared in the (0-4000) cm-1 region with the theoretical results calculated by the density functional theory (DFT) method to provide reliable assignments of all observed bands in the experimental spectra.

Keywords: crystal structure, X-ray diffraction, vibration study, thermal behavior, density functional theory

Procedia PDF Downloads 111

Authors: Anam Gopi

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The undesired torque and flux ripple may occur in conventional direct torque control (DTC) induction motor drive. DTC can improve the system performance at low speeds by continuously tuning the regulator by adjusting the Kp, Ki values. In this Teaching Learning Based Optimization (TLBO) is proposed to adjust the parameters (Kp, Ki) of the speed controller in order to minimize torque ripple, flux ripple, and stator current distortion. The TLBO based PI controller has resulted is maintaining a constant speed of the motor irrespective of the load torque fluctuations.

Keywords: teaching learning based optimization, direct torque control, PI controller

Procedia PDF Downloads 583

Authors: Agis Andriani

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To create an effective teaching condition, the teacher requests the students as the instruction to guide the them interactively in the learning activities in the classroom. This study involves 160 Indonesian students who study English in the university, as participants in the discourse completion test, and ten of them are interviewed. The result shows that when the students response the teacher’s request, it realizes assertives, directives, commisives, expressives, and declaratives. These indicate that the students are active, motivated, and responsive in the learning process, although in the certain condition these responses are to prevent their faces from the shyness of their silence in interaction. Therefore, it needs the teacher’s creativity to give the conducive atmosphere in order to support the students’ participation in learning English.

Keywords: discourse completion test, effective teaching, request, teacher’s creativity

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Authors: Ágoston Nagy

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This paper introduces a method of using unsupervised machine learning to visualize the feature space of a dataset in 2D, in order to find most characteristic segments in the set. After dimension reduction, users can select clusters by manual drawing. Selected clusters are recorded into a data model that is used for later predictions, based on realtime data. Predictions are made with supervised learning, using Gesture Recognition Toolkit. The paper introduces two example applications: a semantic audio organizer for analyzing incoming sounds, and a gesture database organizer where gestural data (recorded by a Leap motion) is visualized for further manipulation.

Keywords: gesture recognition, machine learning, real-time interaction, visualization

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Authors: Tesfaye Mengistu

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This thesis aims to investigate the effectiveness of Reinforcement Learning (RL) for Autonomous Energy Management solutions. The study explores the potential of Model Free RL approaches, such as Monte Carlo RL and Q-learning, to improve energy management by autonomously adjusting energy management strategies to maximize efficiency. The research investigates the implementation of RL algorithms for optimizing energy consumption in a single-agent environment. The focus is on developing a framework for the implementation of RL algorithms, highlighting the importance of RL for enabling autonomous systems to adapt quickly to changing conditions and make decisions based on previous experiences. Moreover, the paper proposes RL as a novel energy management solution to address nations' CO2 emission goals. Reinforcement learning algorithms are well-suited to solving problems with sequential decision-making patterns and can provide accurate and immediate outputs to ease the planning and decision-making process. This research provides insights into the challenges and opportunities of using RL for energy management solutions and recommends further studies to explore its full potential. In conclusion, this study provides valuable insights into how RL can be used to improve the efficiency of energy management systems and supports the use of RL as a promising approach for developing autonomous energy management solutions in residential buildings.

Keywords: artificial intelligence, reinforcement learning, monte carlo, energy management, CO2 emission

Procedia PDF Downloads 79

Authors: Akram Hashemi

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This study provided an empirical evidence for learners of elementary level of language proficiency to investigate the potential role of contextualization in vocabulary learning. Prior to the main study, pilot study was performed to determine the reliability and validity of the researcher-made pretest and posttest. After manifesting the homogeneity of the participants, the participants (n = 90) were randomly assigned into three equal groups, i.e., two experimental groups and a control group. They were pretested by a vocabulary test, in order to test participants' pre-knowledge of vocabulary. Then, vocabulary instruction was provided through three methods of visual instruction, the use of context and the use of conventional techniques. At the end of the study, all participants took the same posttest in order to assess their vocabulary gain. The results of independent sample t-test indicated that there is a significant difference between learning vocabulary visually and learning vocabulary contextually. The results of paired sample t-test showed that different teaching strategies have significantly different impacts on learners’ vocabulary gains. Also, the contextual strategy was significantly more effective than visual strategy in improving students’ performance in vocabulary test.

Keywords: vocabulary instruction, explicit instruction, implicit instruction, strategy

Procedia PDF Downloads 332

Authors: Murad Almadani, Khalil Abu-Hantash, Xinyu Wang, Herbert Jelinek, Kinda Khalaf

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Gait analysis is used by healthcare professionals as a tool to gain a better understanding of the movement impairment and track progress. In most circumstances, monitoring patients in their real-life environments with low-cost equipment such as cameras and wearable sensors is more important. Inertial sensors, on the other hand, cannot provide enough information on angular dynamics. This research offers a method for tracking 2D joint coordinates using cutting-edge vision algorithms and a single RGB camera. We provide an end-to-end comprehensive deep learning pipeline for marker-less gait parameter estimation, which, to our knowledge, has never been done before. To make our pipeline function in real-time for real-world applications, we leverage the AlphaPose human posture prediction model and a deep learning transformer. We tested our approach on the well-known GPJATK dataset, which produces promising results.

Keywords: gait analysis, human pose estimation, deep learning, real time gait estimation, AlphaPose, transformer

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Authors: Elif Eroglu Hall, Nurdan Sevim

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The purposes of this research are to clarify the determinants of Muslim immigrants in consuming halal food by using components of Theory of Planned Behavior. The study was done by surveying Turkish immigrants living in Cologne Germany. The results of this study show that the intentions of Muslim Turkish immigrants in consuming halal food is influenced by attitude, subjective norms and perceived behavioral control.

Keywords: halal food, immigrants, religion, theory of planned behavior

Procedia PDF Downloads 293

Authors: Edicio M. Faller

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The use of technology in the delivery of teaching and learning is vital nowadays especially in education. Computer Assisted Learning Module (CALM) software is the use of computer in the delivery of instruction with a tailored fit program intended for a specific lesson or a set of topics. The CALM software developed in this study is intended to supplement the traditional teaching methods in technical-vocational (TECH-VOC) instruction specifically the Consumer Electronics Servicing course. There are three specific objectives of this study. First is to create a learning enhancement and review materials on the selected lessons. Second, is to computerize the end-of-chapter quizzes. Third, is to generate a computerized mock exam and summative assessment. In order to obtain the objectives of the study the researcher adopted the Agile Model where the development of the study undergoes iterative and incremental process of the Software Development Life Cycle. The study conducted an acceptance testing using a survey questionnaire to evaluate the CALM software. The results showed that CALM software was generally interpreted as very satisfactory. To further improve the CALM software it is recommended that the program be updated, enhanced and lastly, be converted from stand-alone to a client/server architecture.

Keywords: computer assisted learning module, software development life cycle, computerized mock exam, consumer electronics servicing

Procedia PDF Downloads 390

Authors: Shayan Mohajer Hamidi

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Federated learning has emerged as a promising approach for distributed model training while preserving data privacy. However, the challenges of communication overhead, limited network resources, and slow convergence hinder its widespread adoption. On the other hand, knowledge distillation has shown great potential in compressing large models into smaller ones without significant loss in performance. In this paper, we propose an innovative framework that combines federated learning and knowledge distillation to address these challenges and enhance the efficiency of distributed learning. Our approach, called Federated Knowledge Distillation (FKD), enables multiple clients in a federated learning setting to collaboratively distill knowledge from a teacher model. By leveraging the collaborative nature of federated learning, FKD aims to improve model compression while maintaining privacy. The proposed framework utilizes a coded teacher model that acts as a reference for distilling knowledge to the client models. To demonstrate the effectiveness of FKD, we conduct extensive experiments on various datasets and models. We compare FKD with baseline federated learning methods and standalone knowledge distillation techniques. The results show that FKD achieves superior model compression, faster convergence, and improved performance compared to traditional federated learning approaches. Furthermore, FKD effectively preserves privacy by ensuring that sensitive data remains on the client devices and only distilled knowledge is shared during the training process. In our experiments, we explore different knowledge transfer methods within the FKD framework, including Fine-Tuning (FT), FitNet, Correlation Congruence (CC), Similarity-Preserving (SP), and Relational Knowledge Distillation (RKD). We analyze the impact of these methods on model compression and convergence speed, shedding light on the trade-offs between size reduction and performance. Moreover, we address the challenges of communication efficiency and network resource utilization in federated learning by leveraging the knowledge distillation process. FKD reduces the amount of data transmitted across the network, minimizing communication overhead and improving resource utilization. This makes FKD particularly suitable for resource-constrained environments such as edge computing and IoT devices. The proposed FKD framework opens up new avenues for collaborative and privacy-preserving distributed learning. By combining the strengths of federated learning and knowledge distillation, it offers an efficient solution for model compression and convergence speed enhancement. Future research can explore further extensions and optimizations of FKD, as well as its applications in domains such as healthcare, finance, and smart cities, where privacy and distributed learning are of paramount importance.

Keywords: federated learning, knowledge distillation, knowledge transfer, deep learning

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Authors: Stijn Horck

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Background: The value of lessons learned from failure increases when these insights can be put to use by those who did not experience the failure. While learning from others has mostly been researched between individuals or teams within the same environment, transferring knowledge from the person who experienced the failure to an outsider comes with extra challenges. As sense-making of failure is an individual process leading to different learning experiences, the potential of lessons learned from failure is highly variable depending on who is transferring the lessons learned. Using an integrated framework of linguistic aspects related to attributional egotism, this study aims to offer a complete explanation of the challenges in transferring lessons learned from failures that are experienced by others. Method: A case study of a failed foundation established to address the information needs for GPs in times of COVID-19 has been used. An overview of failure causes and lessons learned were made through a preliminary analysis of data collected in two phases with metaphoric examples of failure types. This was followed up by individual narrative interviews with the board members who have all experienced the same events to analyse the individual variance of lessons learned through discourse analysis. This research design uses the researcher-as-instrument approach since the recipient of these lessons learned is the author himself. Results: Thirteen causes were given why the foundation has failed, and nine lessons were formulated. Based on the individually emphasized events, the explanation of the failure events mentioned by all or three respondents consisted of more linguistic aspects related to attributional egotism than failure events mentioned by only one or two. Moreover, the learning events mentioned by all or three respondents involved lessons learned that are based on changed insight, while the lessons expressed by only one or two are more based on direct value. Retrospectively, the lessons expressed as a group in the first data collection phase seem to have captured some but not all of the direct value lessons. Conclusion: Individual variance in expressing lessons learned to outsiders can be reduced using metaphoric or analogical explanations from a third party. In line with the attributional egotism theory, individuals separated from a group that has experienced the same failure are more likely to refer to failure causes of which the chances to be contradicted are the smallest. Lastly, this study contributes to the academic literature by demonstrating that the use of linguistic analysis is suitable for investigating the knowledge transfer from lessons learned after failure.

Keywords: failure, discourse analysis, knowledge transfer, attributional egotism

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Authors: Xiaobai Li, Li Li, Yujin Hu, Weiming Deng, Zhe Ding

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A size-dependent Euler–Bernoulli beam model, which accounts for nonlocal stress field, strain gradient field and higher order inertia force field, is derived based on the nonlocal strain gradient theory considering velocity gradient effect. The governing equations and boundary conditions are derived both in dimensional and dimensionless form by employed the Hamilton principle. The analytical solutions based on different continuum theories are compared. The effect of higher order inertia terms is extremely significant in high frequency range. It is found that there exists an asymptotic frequency for the proposed beam model, while for the nonlocal strain gradient theory the solutions diverge. The effect of strain gradient field in thickness direction is significant in low frequencies domain and it cannot be neglected when the material strain length scale parameter is considerable with beam thickness. The influence of each of three size effect parameters on the natural frequencies are investigated. The natural frequencies increase with the increasing material strain gradient length scale parameter or decreasing velocity gradient length scale parameter and nonlocal parameter.

Keywords: Euler-Bernoulli Beams, free vibration, higher order inertia, Nonlocal Strain Gradient Theory, velocity gradient

Procedia PDF Downloads 263

Authors: Amal Khalifa, Nicolas Vana Santos

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Steganography has been known for centuries as an efficient approach for covert communication. Due to its popularity and ease of access, image steganography has attracted researchers to find secure techniques for hiding information within an innocent looking cover image. In this research, we propose a novel deep-learning approach to digital image steganography. The proposed method, DeepWaveletFusion, uses convolutional neural networks (CNN) to hide a secret image into a cover image of the same size. Two CNNs are trained back-to-back to merge the Discrete Wavelet Transform (DWT) of both colored images and eventually be able to blindly extract the hidden image. Based on two different image similarity metrics, a weighted gain function is used to guide the learning process and maximize the quality of the retrieved secret image and yet maintaining acceptable imperceptibility. Experimental results verified the high recoverability of DeepWaveletFusion which outperformed similar deep-learning-based methods.

Keywords: deep learning, steganography, image, discrete wavelet transform, fusion

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Authors: Gila Kurtz, Dan Kohen Vacs

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Social robots (SR) is an emerging field striving to deploy computers capable of resembling human shapes and mimicking human movements, gestures, and behaviors. The evolving capability of SR to interact with human offers groundbreaking ways for learning and training opportunities. Studies show that SR can offer instructional experiences for fostering creativity, entertainment, enjoyment, and curiosity. These added values are essential for empowering instructional opportunities as gamified learning experiences. We present our project focused on deploying an activity to be experienced in an escape room aimed at team-based learning scaffolded by an SR, NAO. An escape room is a well-known approach for gamified activities focused on a simulated scenario experienced by team-based participants. Usually, the simulation takes place in a physical environment where participants must complete a series of challenges in a limited amount of time. During this experience, players learn something about the assigned topic of the room. In the current learning simulation, students must "save the nation" by locating sensitive information stolen and stored in a vault of four locks. Team members have to look for hints and solve riddles mediated by NAO. Each solution provides a unique code for opening one of the four locks. NAO is also used to provide ongoing feedback on the team's performance. We captured the proceeding of our activity and used it to conduct an evaluation study among ten experts in related areas. The experts were interviewed on their overall assessment of the learning activity and their perception of the added value related to the robot. The results were very encouraging on the feasibility that NAO can serve as a motivational tutor in adults' collaborative game-based learning. We believe that this study marks the first step toward a template for developing innovative team-based training using escape rooms supported by a humanoid robot.

Keywords: social robot, NAO, learning, team based activity, escape room

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Authors: R. Khoza, K. K. Govender

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The aim of the study was to review and critically evaluate the supply chain management (SCM) strategies and challenges in the private residential construction sector in South Africa. The study was grounded in three theories, namely, theory of constraints, principal-agency theory, and stakeholder theory. A quantitative approach was used to survey 320 private residential construction companies which registered with the National Homebuilders Registration Council (NHBRC) within the Gauteng province. The data from 250 questionnaires returned were analysed using SPSS (Versions 23) and Smart PLS. It became evident that the SCM challenges included lack of trust between the supplier and the organization; lack of adoption of SCM system; lack of a sufficiently skilled SCM workforce; and poor implementation of contract management. The findings also indicate that there is a significant positive relationship between the performance of the private residential construction sector in South Africa and SCM challenges, SCM strategies and SCM processes. A framework is proposed comprising SCM practices and strategies of private residential construction sector in South Africa, which will enable them to enhance performance.

Keywords: management challenges, residential housing, South Africa, supply chain management

Procedia PDF Downloads 78

Authors: Ahlem Abid, Farah Jemili

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Intrusion detection has been the subject of numerous studies in industry and academia, but cyber security analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve intrusion detection system, the visualisation of the security events in form of graphs and diagrams is important to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and using a machine learning graph algorithm which can detect in real time different attacks as early as possible. We use the MAWILab intrusion detection dataset . We choose Microsoft Azure as a unified cloud environment to load our dataset on. We implement the k2 algorithm which is a graphical machine learning algorithm to classify attacks. Our system showed a good performance due to the graphical machine learning algorithm and spark structured streaming engine.

Keywords: Apache Spark Streaming, Graph, Intrusion detection, k2 algorithm, Machine Learning, MAWILab, Microsoft Azure Cloud

Procedia PDF Downloads 143

Authors: Suzan Anwar, Utkarsh Goyal

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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.

Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest

Procedia PDF Downloads 136

Authors: Mohab Magdy Mostafa Mohamed

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The objective of this paper is to introduce a vertical handover (VHO) algorithm between wireless LANs (WLANs) and LTE mobile networks. The proposed algorithm is based on the fuzzy control theory and takes into consideration power level, subscriber velocity, and target cell load instead of only power level in traditional algorithms. Simulation results show that network performance in terms of number of handovers and handover occurrence distance is improved.

Keywords: vertical handover, fuzzy control theory, power level, speed, target cell load

Procedia PDF Downloads 346

Authors: Haohao Wang

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Technology, multimedia in Open Educational Resources, can contribute positively to student performance in an online instructional environment. Student performance data of past four years were obtained from an online course entitled Applied Calculus (MA139). This paper examined the data to determine whether multimedia (independent variable) had any impact on student performance (dependent variable) in online math learning, and how students felt about the value of the technology. Two groups of student data were analyzed, group 1 (control) from the online applied calculus course that did not use multimedia instructional materials, and group 2 (treatment) of the same online applied calculus course that used multimedia instructional materials. For the MA139 class, results indicate a statistically significant difference (p = .001) between the two groups, where group 1 had a final score mean of 56.36 (out of 100), group 2 of 70.68. Additionally, student testimonials were discussed in which students shared their experience in learning applied calculus online with multimedia instructional materials.

Keywords: online learning, open educational resources, multimedia, technology

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Authors: Saif Hussein Abdallah Alghazo, Norshima Humaidi

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Adopting cybersecurity protective behaviour among the employees is seriously considered in the organization, especially when the Internet of Things (IoT) is widely used in Industrial Revolution 4.0 (IR 4.0) era. Cybersecurity issues arise due to weaknesses of employees’ behaviour such as carelessness and failure to adopt good practices of information security behaviour. Therefore, this study aims to explore the dimensions that might influence employees’ behaviour to adopt good cybersecurity practices and to develop a new holistic model related to this concept. The study proposed this by reviewing the existing works of literature related to this field extensively, especially by focusing on the existing theory such as Protection Motivation Theory (PMT). Moreover, this study has also explored the role of cybersecurity competency among the security manager in the organization since this construct is essential to enhance the protective behaviour towards cybersecurity among the employees in the organization. The proposed research model is important to be quantitatively tested in the future as the findings will serve as the input to the act that will enhance employee’s cybersecurity protective behaviour in the IR 4.0 environment.

Keywords: cybersecurity protective behaviour, protection motivation theory, IR 4.0, cybersecurity competency

Procedia PDF Downloads 148

Authors: Tanit Pruktara

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The research aimed to study the students’ learning achievement and awareness level on electrical energy consumption and conservation and also to investigate the students’ attitude on the developed multimedia supplemented instructional unit for learning household electrical energy consumption and conservation in grade 10 Thailand student. This study used a quantitative method using MCQ for pre and post-achievement tests and Likert scales for awareness and attitude survey questionnaires. The results from this were employed to improve the multimedia to be appropriate for the classroom and with real life situations in the second phase, the main study. The experimental results showed that the developed learning unit significantly improved the students’ learning achievement as well as their awareness of electric energy conservation. Additional we found the student will enjoy participating in class activities when the lessons are taught using multimedia and helps them to develop the relevance between the course and real world situations.

Keywords: lesson plan, media and animations, training course, vocational school in Thailand

Procedia PDF Downloads 173

Authors: Peter I. I. Ikokwu

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Recent developments in the world, both in the health and education sectors, have further popularized the importance of Information and Communication Technology (ICT). ICT is available for many purposes, including teaching and learning, and its use in education is believed to empower both teachers and students by making the educational process more effective and interactive. The study examined the perceptions of teachers on the factors affecting the use of ICT in the teaching and learning of chemistry and the mitigating factors. The study involved all the lecturers (herein referred to as teachers) in the Colleges of Education in South Eastern Nigeria. The survey design was employed. 35 teachers were selected by stratified random sampling from about 78 chemistry teachers in these Colleges. However, 34 questionnaires were recovered, comprising 13 males and 21 females. 3 research questions and 3 hypotheses guided the study. Results show that the teachers have a clear perception of the factors militating against the use of ICT in the teaching and learning of chemistry, with a pooled mean of 2.96. But there was no significant difference in the perceptions of male and female teachers. Also, they identified the mitigating factors highlighted with no significant difference between the perceptions of the males and females with pooled means of 3.23 and 3.11, respectively. In all, it is noteworthy that lack of funds, irregular and inadequate power supply, and inadequate time in the school timetable was among the militating factors. Recommendations were made for the consideration of the government, the teachers, and the Institutions.

Keywords: chemistry, teachers, perception, ICT, learning

Procedia PDF Downloads 88

Authors: Nidhal Jamia, Sami El-Borgi

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In this paper, the problem of a mixed-Mode crack embedded in an infinite medium made of a functionally graded piezoelectric material (FGPM) with crack surfaces subjected to electro-mechanical loadings is investigated. Eringen’s non-local theory of elasticity is adopted to formulate the governing electro-elastic equations. The properties of the piezoelectric material are assumed to vary exponentially along a perpendicular plane to the crack. Using Fourier transform, three integral equations are obtained in which the unknown variables are the jumps of mechanical displacements and electric potentials across the crack surfaces. To solve the integral equations, the unknowns are directly expanded as a series of Jacobi polynomials, and the resulting equations solved using the Schmidt method. In contrast to the classical solutions based on the local theory, it is found that no mechanical stress and electric displacement singularities are present at the crack tips when nonlocal theory is employed to investigate the problem. A direct benefit is the ability to use the calculated maximum stress as a fracture criterion. The primary objective of this study is to investigate the effects of crack length, material gradient parameter describing FGPMs, and lattice parameter on the mechanical stress and electric displacement field near crack tips.

Keywords: functionally graded piezoelectric material (FGPM), mixed-mode crack, non-local theory, Schmidt method

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Authors: R. W. Chan, Kan Kan Chan

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Augmented Reality (AR), with the affordance of bridging between real world and virtual world, brings users immersive experience. It has been applied in education gradually and even come into practice in student daily learning. However, a systematic review shows that there are limited researches in the area of vocabulary acquisition in early childhood education. Since kindergarten is a key stage where children acquire language and AR as an emerging and potential technology to support the vocabulary acquisition, this study aims to explore its value in in real classroom with teacher’s view. Participants were a class of 5 to 6 years old kids studying in a Macau school that follows Cambridge curriculum and emphasizes multicultural ethos. There were 11 boys, 13 girls, and in a total of 24 kids. They learnt animal vocabulary using mobile device and AR flashcards, IPad to scan AR flashcards and interact with pop-up virtual objects. In order to estimate the effectiveness of using Augmented Reality, children attended vocabulary pre-posttest. In addition, teacher interview was administrated after this learning activity to seek practitioner’s opinion towards this technology. For data analysis, paired samples t-test was utilized to measure the instructional effect based on the pre-posttest data. Result shows that Augmented Reality could significantly enhance children vocabulary learning with large effect size. Teachers indicated that children enjoyed the AR learning activity but clear instruction is needed. Suggestions for the future implementation of vocabulary acquisition using AR are suggested.

Keywords: augmented reality, kindergarten children, vocabulary learning, Macau

Procedia PDF Downloads 147

Authors: Damian Gordon, Paul Doyle, Anna Becevel, Júlia Vilafranca Molero, Cinta Gascon, Arianna Vitiello, Tina Baloh

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It has long been recognized that the creation of any teaching content can be enhanced if the development process follows a pre-defined approach, which is often referred to as an instructional design methodology. These methodologies typically define a number of stages, or phases, that an educator should undertake to help ensure the quality of the final teaching content that is developed. In this paper, we present an instructional design methodology that is focused specifically on the introduction of blended resources into a heretofore bricks-and-mortar course. To achieve this, research was undertaken concerning a range of models of instructional design, as well as literature covering some of the key challenges and “pain points” of blending. Following this, our model, the BL-5D model, is presented, which incorporates some key questions at each stage of this five-stage methodology to guide the development process. Finally, a discussion of some of the key themes and issues that have been uncovered in this work is presented, as well as a template for a blended learning case study that emerged from this approach.

Keywords: blended learning, challenges of blended learning, design methodologies, instructional design

Procedia PDF Downloads 113

Authors: Anano Tenieshvili, Teona Lodia

Abstract:

Social anxiety disorder (SAD) is one of the most common mental health problems not only in adults but also in adolescents. Individuals with SAD exhibit difficulties in interpersonal relationships, understanding emotions, and regulating them as well. For social and emotional adaptation, it is crucial to identify, understand, accept and manage emotions correctly. Researchers actively learn those factors that contribute to the development and maintenance of this condition. Therefore, the main purpose of this study is to acquire knowledge about the association between social anxiety and individual characteristics, such as theory of mind (ToM), verbal irony comprehension, and personal traits. 112 adolescents aged from 12 to 18 were selected for this research. 15 of them are diagnosed with Social anxiety disorder. Statistical analysis was performed on the entire sample, and furthermore, two groups, adolescents with and without social anxiety disorder, were compared separately. Social anxiety and personal traits were assessed by questionnaires. Theory of mind and comprehension of verbal irony were measured using tests. Statistical analysis indicated a positive relationship between social anxiety and comprehension of ironic criticism. Moreover, social anxiety was significantly positively correlated with neuroticism and isolation tendency, whereas it was negatively related to extraversion and frustration tolerance. On top of that, statistical analysis revealed a positive relationship between ToM and verbal irony comprehension. However, the relationship between social anxiety and ToM was not statistically significant. In conclusion, the current research expands knowledge about social anxiety and supports the results of some previous studies.

Keywords: personal traits, social anxiety, theory of mind, verbal irony comprehension

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