Search results for: Physics informed machine learning

Authors: T. Konst

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Most jobs include training and communication tasks, but often the people in these jobs lack pedagogical competences to plan, implement and assess learning. This paper aims to discuss how a learning approach called innovation pedagogy developed in higher education can be utilized for learning development in various organizations. The methods presented how to implement innovation pedagogy such as process consultation and train the trainer model can provide added value to develop pedagogical knowhow in organizations and thus support their internal learning and development.

Keywords: innovation pedagogy, learning, organizational development, process consultation

Procedia PDF Downloads 366

Authors: A. Şahinoğlu, R. Gürbüz, A. Güllü, M. Karhan

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This study in production machines, it is aimed that machine will automatically perceive cutting data and alter cutting parameters. The two most important parameters have to be checked in machine control unit are progress feed rate and speeds. These parameters are aimed to be controlled by sounds of machine. Optimum sound’s features introduced to computer. During process, real time data is received and converted by Matlab software. Data is converted into numerical values. According to them progress and speeds decreases/increases at a certain rate and thus optimum sound is acquired. Cutting process is made in respect of optimum cutting parameters. During chip remove progress, features of cutting tools, kind of cut material, cutting parameters and used machine; affects on various parameters. Instead of required parameters need to be measured such as temperature, vibration, and tool wear that emerged during cutting process; detailed analysis of the sound emerged during cutting process will provide detection of various data that included in the cutting process by the much more easy and economic way. The relation between cutting parameters and sound is being identified.

Keywords: cutting process, sound processing, intelligent late, sound analysis

Procedia PDF Downloads 332

Authors: Houxiang Zhu, Chun Liang

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The CRISPR-Cpf1 system has been successfully applied in genome editing. However, target efficiency of the CRISPR-Cpf1 system varies among different gRNA sequences. The published CRISPR-Cpf1 gRNA data was reanalyzed. Many sequences and structural features of gRNAs (e.g., the position-specific nucleotide composition, position-nonspecific nucleotide composition, GC content, minimum free energy, and melting temperature) correlated with target efficiency were found. Using machine learning technology, a support vector machine (SVM) model was created to predict target efficiency for any given gRNAs. The first web service application, CRISPR-DT (CRISPR DNA Targeting), has been developed to help users design optimal gRNAs for the CRISPR-Cpf1 system by considering both target efficiency and specificity. CRISPR-DT will empower researchers in genome editing.

Keywords: CRISPR-Cpf1, genome editing, target efficiency, target specificity

Procedia PDF Downloads 259

Authors: Reihaneh Mahdavishahri, Dumayi Gutierrez

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Emotionally focused couple therapy (EFCT) is one of the most effective and evidence-based approaches to couple therapy. Yet, literature is scarce of its effective application with trans and non-binary couples. It is estimated that 1.4 million trans adults live in the United Stated, with about 40% of these individuals experiencing serious psychological distress within the past month. Trans and nonbinary adults are significantly likely to experience discrimination, harassment, family rejection, and relationship challenges throughout the course of their lives. As systemic therapists, applying an informed lens when working with trans and nonbinary couples can contribute to providing effective mental health care to these individuals. This paper aims to provide a comprehensive, intersectional, and culturally informed application of EFCT with trans and nonbinary couples. We will address the current literature on applications of EFCT with diverse couples, EFCT’s strengths and limitations on cultural humility, and the gaps within current systems of care for trans and nonbinary couples. We will then provide an adaptive application of EFCT to help trans, and nonbinary couples recover from potential attachment injuries in their relationships, intersecting gender minority stressors, and achieve healing and restoration in their interpersonal dynamics.

Keywords: attachment, culturally informed care, emotionally focused couple therapy, trans and nonbinary couples

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Authors: Itumeleng Phage

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Students’ comprehension of graphs may be affected by the characteristics of the discipline in which the graph is used, the type of the task as well as the background of the students who are the readers or interpreters of the graph. This research study investigated these aspects of the graph comprehension of 152 first-year undergraduate physics students by comparing their responses to corresponding tasks in the mathematics and physics disciplines. The discipline characteristics were analysed for four task-related constructs namely coordinates, representations, area and slope. Students’ responses to corresponding visual decoding and judgement tasks set in mathematics and kinematics contexts were statistically compared. The effects of the participants’ gender, year of school completion and study course were determined as reader characteristics. The results of the empirical study indicated that participants generally transferred their mathematics knowledge on coordinates and representation of straight line graphs to the physics contexts, but not in the cases of parabolic and hyperbolic functions or area under graphs. Insufficient understanding of the slope concept contributed to weak performances on this construct in both mathematics and physics contexts. Discipline characteristics seem to play a vital role in students’ understanding, while reader characteristics had insignificant to medium effects on their responses.

Keywords: kinematics graph, discipline characteristics, constructs, coordinates, representations, area and slope

Procedia PDF Downloads 258

Authors: Avi Shrivastava

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Our world continues to grapple with a lot of social issues. Economic growth and scientific advancements have not completely eradicated poverty, homelessness, discrimination and bias, gender inequality, health issues, mental illness, addiction, and other social issues. So, how do we improve the human condition in a world driven by advanced technology? The answer is simple: we will have to leverage technology to address some of the most important social challenges of the day. AI, or artificial intelligence, has emerged as a critical tool in the battle against issues that deprive marginalized and disadvantaged groups of the right to enjoy benefits that a society offers. Social work professionals can transform their lives by harnessing it. The lack of reliable data is one of the reasons why a lot of social work projects fail. Social work professionals continue to rely on expensive and time-consuming primary data collection methods, such as observation, surveys, questionnaires, and interviews, instead of tapping into AI-based technology to generate useful, real-time data and necessary insights. By leveraging AI’s data-mining ability, we can gain a deeper understanding of how to solve complex social problems and change lives of people. We can do the right work for the right people and at the right time. For example, AI can enable social work professionals to focus their humanitarian efforts on some of the world’s poorest regions, where there is extreme poverty. An interdisciplinary team of Stanford scientists, Marshall Burke, Stefano Ermon, David Lobell, Michael Xie, and Neal Jean, used AI to spot global poverty zones – identifying such zones is a key step in the fight against poverty. The scientists combined daytime and nighttime satellite imagery with machine learning algorithms to predict poverty in Nigeria, Uganda, Tanzania, Rwanda, and Malawi. In an article published by Stanford News, Stanford researchers use dark of night and machine learning, Ermon explained that they provided the machine-learning system, an application of AI, with the high-resolution satellite images and asked it to predict poverty in the African region. “The system essentially learned how to solve the problem by comparing those two sets of images [daytime and nighttime].” This is one example of how AI can be used by social work professionals to reach regions that need their aid the most. It can also help identify sources of inequality and conflict, which could reduce inequalities, according to Nature’s study, titled The role of artificial intelligence in achieving the Sustainable Development Goals, published in 2020. The report also notes that AI can help achieve 79 percent of the United Nation’s (UN) Sustainable Development Goals (SDG). AI is impacting our everyday lives in multiple amazing ways, yet some people do not know much about it. If someone is not familiar with this technology, they may be reluctant to use it to solve social issues. So, before we talk more about the use of AI to accomplish social work objectives, let’s put the spotlight on how AI and social work can complement each other.

Keywords: social work, artificial intelligence, AI based social work, machine learning, technology

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Authors: Xin Zhao

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The project aims to expand the notion of narrative learning and address the importance of learning by reflection in our learning and teaching context at a British university. Drawing on the key concepts such as development ZPD, transition and reflection-in and –on-action, this project analyses the learning experiences of a small sample of Chinese postgraduate students in a British University, who use part-time job experience to develop cross-cultural communication skills. The project adopts a mixed methods approach. Questionnaires and focus group interviews are used to examine the way in which students adapt (or not adapt) to the culture of learning in a British university and develop a renewed sense of self in transitions from one culture to the other. The project also looks at how the students appropriate opportunities for learning not just from classrooms but outside classrooms from everyday encounters. The project aims to address the implication of learning by reflection as development in transition. Time in and for learning, or duration, is taken for granted in theorising narrative learning. The project shall explore this very issue of time in relation to learning by reflection in considering time in/of/for learning as duration.

Keywords: cross-cultural competence, learning by refection, international student transition, part-time work experience

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Authors: Ali Zefeiti

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There has always been a debate on critical versus pragmatic approach to learning English. Different elements of teaching take different shapes in the two approaches. This study concerns itself with the students who are the main pillar of the teaching/learning operation. Students have always been placed into classrooms to learn what the curricula of different courses offer. There is little room for students to state their own learning needs as they often have to conform with the group requirement. This study focuses on an extra-curricular activity students did alongside their mainstream learning. The students come from different colleges and different EAP courses. They are united by their passion for the task and learning many things along the way. The data are collected through interviews and students' journals. The study was concerned with the effect of this extra-curricular activity on students' main learning trajectory. The students were engaged in the task of film production over the period of their English Language course. The findings show that students are able to set their own agenda for learning and have actually had a lot of skills and vocabulary to take to class.

Keywords: critical EAP, pragmatic EAP, self-directed learning, teaching methods

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Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

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Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition

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Authors: Peiming Li

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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

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Authors: Hanan A. Algamdi, Huda Y. Alyami

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Learning object repositories contribute to developing educational process through its advantages; as they employ technology effectively, and use it to create new resources for effective learning, as well as they provide opportunities for collaboration in content through providing the ability for editing, modifying and developing it. This supports the relationships between communities that benefit from these repositories, and reflects positively on the content quality. Therefore, this study aims at exploring the most prominent learning topics in the 21st century, which should be included in learning object repositories, and identifying the necessary set of learning skills that the repositories should develop among today students. For conducting this study, the analytical descriptive method will be employed, and study sample will include a group of leaders, experts, and specialists in curricula and e-learning at ministry of education in Kingdom of Saudi Arabia.

Keywords: learning object, repositories, 21st century, quality

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Authors: Masaki Omata, Shumma Hosokawa

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An experiment to verify the relationships between physiological indexes of an e-learner and the presence or absence of an operation during e-learning is described. Electroencephalogram (EEG), hemoencephalography (HEG), skin conductance (SC), and blood volume pulse (BVP) values were measured while participants performed experimental learning tasks. The results show that there are significant differences between the SC values when reading with clicking on learning materials and the SC values when reading without clicking, and between the HEG ratio when reading (with and without clicking) and the HEG ratio when resting for four of five participants. We conclude that the SC signals can be used to estimate whether or not a learner is performing an active task and that the HEG ratios can be used to estimate whether a learner is learning.

Keywords: e-learning, physiological index, physiological signal, state of learning

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Authors: Jame O. Ezema, Odenigbo Veronica

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The study covers the topic Information Communication and Technology (ICTs) knowledge as a way of enhancing Literacy and Lifelong learning in Nigeria. This work delved into defining of ICTs. Types of ICTs and media technologies were also mentioned. It further explained how ICTs can be strengthened and the uses of ICTs in education was duly emphasized. The paper also enumerated some side effects of ICTs on learners while the role of ICTs in enhancing literacy was explained. The study carried out strategies to use ICTs meaningfully in Literacy Programs and also emphasized the word lifelong learning in Nigeria. Some recommendations were made towards acquiring ICTs knowledge, so as to enhance Literacy and Lifelong learning in Nigeria.

Keywords: literacy, distance-learning, life-long learning for sustainable development, e-learning

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Authors: Riaan Kleyn

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Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.

Keywords: computer vision, wine grapes, machine learning, machine harvested grapes

Procedia PDF Downloads 93

Authors: Min Kyeong Cha, Ju Yeon Mun, Seong Baeg Kim

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Recently, ‘play of learning’ became important and is emphasized as a useful learning tool. Therefore, interest in edutainment contents is growing. Storytelling is considered first as a method that improves the transmission of information and learner's interest when planning edutainment contents. In this study, we designed edutainment contents in the form of an adventure game that applies the storytelling method. This content provides questions and items constituted dynamically and reorganized learning contents through analysis of test results. It allows learners to solve various questions through effective iterative learning. As a result, the learners can reach mastery learning.

Keywords: storytelling, edutainment, mastery learning, computer operating principle

Procedia PDF Downloads 315

Authors: Shu-Chen Wu

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This qualitative study investigated Chinese teachers’ perspectives on learning at play. Six kindergarten teachers were interviewed to obtain their understanding of learning at play. Exemplary play episodes from their classrooms were selected with the assistance of the participating teachers. Four three-minute videos containing the largest amount of learning elements based on the teachers’ views were selected for analysis. Applying video-stimulated interviews, the selected video clips were shown to eight teachers in two focus groups to elicit their perspectives on learning at play. The findings revealed that Chinese teachers have a very structured representation of learning at play, which should contribute to the development of professional practices and curricular policies.

Keywords: learning at play, teachers’ perspectives, co-constructed views, video-stimulated interviews

Procedia PDF Downloads 229

Authors: Xiaoqing Wang, Junfeng Wang, Xiaolan Zhu

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Android operating system has been recognized by most application developers because of its good open-source and compatibility, which enriches the categories of applications greatly. However, it has become the target of malware attackers due to the lack of strict security supervision mechanisms, which leads to the rapid growth of malware, thus bringing serious safety hazards to users. Therefore, it is critical to detect Android malware effectively. Generally, the permissions declared in the AndroidManifest.xml can reflect the function and behavior of the application to a large extent. Since current Android system has not any restrictions to the number of permissions that an application can request, developers tend to apply more than actually needed permissions in order to ensure the successful running of the application, which results in the abuse of permissions. However, some traditional detection methods only consider the requested permissions and ignore whether it is actually used, which leads to incorrect identification of some malwares. Therefore, a machine learning detection method based on the actually used permissions combination and API calls was put forward in this paper. Meanwhile, several experiments are conducted to evaluate our methodology. The result shows that it can detect unknown malware effectively with higher true positive rate and accuracy while maintaining a low false positive rate. Consequently, the AdaboostM1 (J48) classification algorithm based on information gain feature selection algorithm has the best detection result, which can achieve an accuracy of 99.8%, a true positive rate of 99.6% and a lowest false positive rate of 0.

Keywords: android, API Calls, machine learning, permissions combination

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Authors: Sahil Kumar, Surinder Pal, Rahul Kapoor

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This paper is focused on to define the basic difference between hot and cold chamber high pressure die casting process which is not fully defined in a research before paper which we have studied. The pressure die casting is basically defined into two types (1) Hot chamber Die Casting (2) Cold chamber Die Casting. Cold chamber die casting is used for casting alloys that require high pressure and have a high melting temperature, such as brass, aluminum, magnesium, copper based alloys and other high melting point nonferrous alloys. Hot chamber die casting is suitable for casting zinc, tin, lead, and low melting point alloys. In hot chamber die casting machine, the molten metal is an integral pan of the machine. It mainly consists of hot chamber and gooseneck type metal container made of cast iron. This machine is mainly used for low melting alloys and alloys of metals like zinc, lead etc. Metals and alloys having a high melting point and those which are having an affinity for iron cannot be cast by this machine, which could otherwise attack the shot sleeve and damage the machine.

Keywords: hot chamber die casting, cold chamber die casting, metals and alloys, casting technology

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Authors: Tomas Trainys, Algimantas Venckauskas

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Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors.

Keywords: bio-cryptography, biometrics, cryptographic key generation, data fusion, information security, SVM, pattern recognition, finger vein method.

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Authors: Paramvir Singh

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The locomotion of aquatic organisms has long fascinated biologists and engineers alike, with fish fins serving as a prime example of nature's remarkable adaptations for efficient underwater propulsion. This paper presents a comprehensive study focused on the hydrodynamic analysis of fish fin kinematics, employing an innovative approach that combines machine learning and image processing techniques. Through high-speed videography and advanced computational tools, we gain insights into the complex and dynamic motion of the fins of a Tilapia (Oreochromis Niloticus) fish. This study was initially done by experimentally capturing videos of the various motions of a Tilapia in a custom-made setup. Using deep learning and image processing on the videos, the motion of the Caudal and Pectoral fin was extracted. This motion included the fin configuration (i.e., the angle of deviation from the mean position) with respect to time. Numerical investigations for the flapping fins are then performed using a Computational Fluid Dynamics (CFD) solver. 3D models of the fins were created, mimicking the real-life geometry of the fins. Thrust Characteristics of separate fins (i.e., Caudal and Pectoral separately) and when the fins are together were studied. The relationship and the phase between caudal and pectoral fin motion were also discussed. The key objectives include mathematical modeling of the motion of a flapping fin at different naturally occurring frequencies and amplitudes. The interactions between both fins (caudal and pectoral) were also an area of keen interest. This work aims to improve on research that has been done in the past on similar topics. Also, these results can help in the better and more efficient design of the propulsion systems for biomimetic underwater vehicles that are used to study aquatic ecosystems, explore uncharted or challenging underwater regions, do ocean bed modeling, etc.

Keywords: biomimetics, fish fin kinematics, image processing, fish tracking, underwater vehicles

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Authors: Yessica Nataliani, Miin-Shen Yang

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Cell formation (CF) is an important step in group technology. It is used in designing cellular manufacturing systems using similarities between parts in relation to machines so that it can identify part families and machine groups. There are many CF methods in the literature, but there is less spectral clustering used in CF. In this paper, we propose a spectral clustering algorithm for machine-part CF. Some experimental examples are used to illustrate its efficiency. Overall, the spectral clustering algorithm can be used in CF with a wide variety of machine/part matrices.

Keywords: group technology, cell formation, spectral clustering, grouping efficiency

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Authors: Samia Hassan, Fouzia Latif

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In many countries, education is still far from being a knowledge industry in the sense of own practices that are not yet being transformed by knowledge about the efficacy of those practices. The core question of this paper is why students get bored in class? Have we balanced between the creation and advancement of an engaging learning community and effective learning environment? And between, giving kids confidence to achieve their maximum and potential goals, we sand managing student’s behavior. We conclude that creating a positive learning environment enhances opportunities for young children to feel safe, secure, and to supported in order to do their best learning. Many factors can use in classrooms aid to the positive environment like course content, class preparation, and behavior.

Keywords: effective, environment, learning, positive

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Authors: A. Hassannia, S. Ramezani

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The technology of superconductivity has developed in many power system devices such as transmission cable, transformer, current limiter, motor and generator. Superconducting wires can carry high density current without loss, which is the capability that is used to design the compact, lightweight and more efficient electrical machines. Superconducting motors have found applications in marine and air propulsion systems as well as superconducting generators are considered in low power hydraulic and wind generators. This paper presents a rim-driven superconducting synchronous generator for hydraulic power plant. The rim-driven concept improves the performance of hydro turbine. Furthermore, high magnetic field that is produced by superconducting windings allows replacing the rotor core. As a consequent, the volume and weight of the machine is decreased significantly. In this paper, a 1 MW coreless rim-driven superconducting synchronous generator is designed. Main performance characteristics of the proposed machine are then evaluated using finite elements method and compared to an ordinary similar size synchronous generator.

Keywords: coreless machine, electrical machine design, hydraulic generator, rim-driven machine, superconducting generator

Procedia PDF Downloads 172

Authors: C. von Essen

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This study outlines and evaluates a constructivist design approach to the creation of educational video on postgraduate business degree programmes. Many online courses are tapping into the educational affordances of video, as this form of online learning has the potential to create rich, multimodal experiences. And yet, in many learning contexts video is still being used to transmit instruction to passive learners, rather than promote learner engagement and knowledge creation. Constructivism posits the notion that learning is shaped as students make connections between their experiences and ideas. This paper pivots on the following research question: how can we design educational video in ways which promote constructivist learning and stimulate analytic viewing? By exploring and categorizing over two thousand educational videos created since 2014 for over thirty postgraduate courses in business, economics, mathematics and statistics, this paper presents and critically reflects on a taxonomy of video styles and features. It links the pedagogical intent of video – be it concept explanation, skill demonstration, feedback, real-world application of ideas, community creation, or the cultivation of course narrative – to specific presentational characteristics such as visual effects including diagrammatic and real-life graphics and aminations, commentary and sound options, chronological sequencing, interactive elements, and presenter set-up. The findings of this study inform a framework which captures the pedagogical, technological and production considerations instructional designers and educational media specialists should be conscious of when planning and preparing the video. More broadly, the paper demonstrates how learning theory and technology can coalesce to produce informed and pedagogical grounded instructional design choices. This paper reveals how crafting video in a more conscious and critical manner can produce powerful, new educational design.

Keywords: educational video, constructivism, instructional design, business education

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Authors: Mohammed T. Taher, Usman Ghani, Ahmed S. Khan

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This paper compares the findings of two studies conducted to determine the effectiveness of simulation-based, hands-on and feedback mechanism on students learning by answering the following questions: 1). Does the use of simulation improve students’ learning outcomes? 2). How do students perceive the instructional design features embedded in the simulation program such as exploration and scaffolding support in learning new concepts? 3.) What is the effect of feedback mechanisms on students’ learning in the use of simulation-based labs? The paper also discusses the other aspects of findings which reveal that simulation by itself is not very effective in promoting student learning. Simulation becomes effective when it is followed by hands-on activity and feedback mechanisms. Furthermore, the paper presents recommendations for improving student learning through the use of simulation-based, hands-on, and feedback-based teaching methodologies.

Keywords: simulation-based teaching, hands-on learning, feedback-based learning, scaffolding

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Authors: Habsah Mohamad Sabli, Mohammad Fardillah Wahi

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The main aim of this research study is to better understand and measure students' perceptions towards the effectiveness of mobile learning. This paper reports on the results of a survey of three hundred nineteen students at Polytechnic Mukah Sarawak (PMU) about their perception to the use of mobile technology in education. An analysis of the quantitative survey findings is presented focusing on the ramification for mobile-learning (m-learning) practices in higher learning and teaching environments. In this paper we present our research findings about the level of perception and gender correlations with perceived ease of use and perceived usefulness using M-Learning in learning activities among students in Polytechnic Mukah (PMU). Based on gender respondent, were 150 female (47.0%) and 169 male (53.0%). The survey findings further revealed that perception of students are in moderately high and agree for using m-learning. The perceived ease of use and perceived usefulness is significant with weak correlations between students to adapt m-learning for active learning activities. The outcome of this research can benefit the decision makers of higher institution in Mukah Sarawak regard to way to enhance m-learning and promote effective teaching and learning activities as well as strengthening the quality of learning delivery.

Keywords: M-learning, student attitudes, student perception, mobile technology

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: Edward K. Boahen, Brunel E. Bouya-Moko, Changda Wang

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The major threat to online social network (OSN) users is account compromisation. Spammers now spread malicious messages by exploiting the trust relationship established between account owners and their friends. The challenge in detecting a compromised account by service providers is validating the trusted relationship established between the account owners, their friends, and the spammers. Another challenge is the increase in required human interaction with the feature selection. Research available on supervised learning (machine learning) has limitations with the feature selection and accounts that cannot be profiled, like application programming interface (API). Therefore, this paper discusses the various behaviours of the OSN users and the current approaches in detecting a compromised OSN account, emphasizing its limitations and challenges. We propose a deep learning approach that addresses and resolve the constraints faced by the previous schemes. We detailed our proposed optimized nonsymmetric deep auto-encoder (OPT_NDAE) for unsupervised feature learning, which reduces the required human interaction levels in the selection and extraction of features. We evaluated our proposed classifier using the NSL-KDD and KDDCUP'99 datasets in a graphical user interface enabled Weka application. The results obtained indicate that our proposed approach outperformed most of the traditional schemes in OSN compromised account detection with an accuracy rate of 99.86%.

Keywords: computer security, network security, online social network, account compromisation

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Authors: K. Kannapathi-Naidoo

Abstract:

The higher education landscape has undergone changes in the past decade, with concepts such as blended learning, online learning, and hybrid models appearing more frequently in research and practice. The year 2020 marked a mass migration from face-to-face learning and more traditional forms of education to online learning in higher education institutions across the globe due to the Covid-19 pandemic. As a result, contact learning students and lecturing staff alike were thrust into the world of online learning at an unprecedented pace. Traditional modes of learning had to be amended, and pedagogical strategies required adjustments. This study was located within a compulsory first-year academic literacy module in a higher education institution. The study aimed to explore students’ learning preferences between online, face-face, and blended learning within the context of academic literacy. Data was collected through online qualitative questionnaires administered to 150 first-year students, which were then analysed thematically. The findings of the study revealed that 48.5% of the participants preferred a blended learning approach to academic literacy. The main themes that emerged in support of their preference were best of both worlds, flexibility, productivity, and lecturer accessibility. As a result, this paper advocates for the blended learning approach for academic literacy skills-based modules.

Keywords: academic literacy, blended learning, online learning, student learning preferences

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Authors: Mihail Calalb

Abstract:

The aim of this research is to identify ways for achieving sustainable conceptual understanding within science lessons. For this purpose, a set of teaching and learning strategies, parts of the theory of visible teaching and learning (VTL), is studied. As a result, a new didactic approach named "learning by being" is proposed and its correlation with educational paradigms existing nowadays in science teaching domain is analysed. In the context of VTL the author describes the main strategies of "learning by being" such as guided self-scaffolding, structuring of information, and recurrent use of previous knowledge or help seeking. Due to the synergy effect of these learning strategies applied simultaneously in class, the impact factor of learning by being on cognitive achievement of students is up to 93 % (the benchmark level is equal to 40% when an experienced teacher applies permanently the same conventional strategy during two academic years). The key idea in "learning by being" is the assumption by the student of cognitive goals. From this perspective, the article discusses the role of student’s personal learning effort within several teaching strategies employed in VTL. The research results emphasize that three mandatory student – related moments are present in each constructivist teaching approach: a) students’ personal learning effort, b) student – teacher mutual feedback and c) metacognition. Thus, a successful educational strategy will target to achieve an involvement degree of students into the class process as high as possible in order to make them not only know the learning objectives but also to assume them. In this way, we come to the ownership of cognitive goals or students’ deep intrinsic motivation. A series of approaches are inherent to the students’ ownership of cognitive goals: independent research (with an impact factor on cognitive achievement equal to 83% according to the results of VTL); knowledge of success criteria (impact factor – 113%); ability to reveal similarities and patterns (impact factor – 132%). Although it is generally accepted that the school is a public service, nonetheless it does not belong to entertainment industry and in most of cases the education declared as student – centered actually hides the central role of the teacher. Even if there is a proliferation of constructivist concepts, mainly at the level of science education research, we have to underline that conventional or frontal teaching, would never disappear. Research results show that no modern method can replace an experienced teacher with strong pedagogical content knowledge. Such a teacher will inspire and motivate his/her students to love and learn physics. The teacher is precisely the condensation point for an efficient didactic strategy – be it constructivist or conventional. In this way, we could speak about "hybridized teaching" where both the student and the teacher have their share of responsibility. In conclusion, the core of "learning by being" approach is guided learning effort that corresponds to the notion of teacher–student harmonic oscillator, when both things – guidance from teacher and student’s effort – are equally important.

Keywords: conceptual understanding, learning by being, ownership of cognitive goals, science learning

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