Search results for: mobile game based learning

Authors: Zakaria Kharbouch, Mustapha Bouchaara, F. Elkouihen, A. Habbal, A. Ratnani, A. Faik

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Solid-state lithium-ion batteries have garnered increasing interest in modern energy research due to their potential for safer, more efficient, and sustainable energy storage systems. Among the critical components of these batteries, the electrolyte plays a pivotal role, with LLZO garnet-based electrolytes showing significant promise. Garnet materials offer intrinsic advantages such as high Li-ion conductivity, wide electrochemical stability, and excellent compatibility with lithium metal anodes. However, optimizing ionic conductivity in garnet structures poses a complex challenge, primarily due to the multitude of potential dopants that can be incorporated into the LLZO crystal lattice. The complexity of material design, influenced by numerous dopant options, requires a systematic method to find the most effective combinations. This study highlights the utility of machine learning (ML) techniques in the materials discovery process to navigate the complex range of factors in garnet-based electrolytes. Collaborators from the materials science and ML fields worked with a comprehensive dataset previously employed in a similar study and collected from various literature sources. This dataset served as the foundation for an extensive data refinement phase, where meticulous error identification, correction, outlier removal, and garnet-specific feature engineering were conducted. This rigorous process substantially improved the dataset's quality, ensuring it accurately captured the underlying physical and chemical principles governing garnet ionic conductivity. The data refinement effort resulted in a significant improvement in the predictive performance of the machine learning model. Originally starting at an accuracy of 0.32, the model underwent substantial refinement, ultimately achieving an accuracy of 0.88. This enhancement highlights the effectiveness of the interdisciplinary approach and underscores the substantial potential of machine learning techniques in materials science research.

Keywords: lithium batteries, all-solid-state batteries, machine learning, solid state electrolytes

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Hao Min Chuah, Tatsuya Yamazaki, Ryosui Iwasawa, Tatsumi Suto

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In recent years, with the advancement of digital technology, the movement to introduce so-called ICT (Information and Communication Technology), such as computer technology and network technology, to civil engineering construction sites and construction sites is accelerating. As part of this movement, attempts are being made in various situations to reproduce actual sites inside computers and use them for designing and construction planning, as well as for training inexperienced engineers. The installation of wave-dissipating blocks on coasts, etc., is a type of work that has been carried out by skilled workers based on their years of experience and is one of the tasks that is difficult for inexperienced workers to carry out on site. Wave-dissipating blocks are structures that are designed to protect coasts, beaches, and so on from erosion by reducing the energy of ocean waves. Wave-dissipating blocks usually weigh more than 1 t and are installed by being suspended by a crane, so it would be time-consuming and costly for inexperienced workers to train on-site. In this paper, therefore, a block installation simulator is developed based on Unity 3D, a game development engine. The simulator computes porosity. Porosity is defined as the ratio of the total volume of the wave breaker blocks inside the structure to the final shape of the ideal structure. Using the evaluation of porosity, the simulator can determine how well the user is able to install the blocks. The voxelization technique is used to calculate the porosity of the structure, simplifying the calculations. Other techniques, such as raycasting and box overlapping, are employed for accurate simulation. In the near future, the simulator will install an automatic block installation algorithm based on combinatorial optimization solutions and compare the user-demonstrated block installation and the appropriate installation solved by the algorithm.

Keywords: 3D simulator, porosity, user interface, voxelization, wave-dissipating blocks

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Authors: Foster Chilufya

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This study sought to determine factors that affect academic performance of mature age students in Science Education at University of Zambia. It was guided by Maslow’s Hierarchy of Needs. The theory provided relationship between achievement motivation and academic performance. A descriptive research design was used. Both Qualitative and Quantitative research methods were used to collect data from 88 respondents. Simple random and purposive sampling procedures were used to collect from the respondents. Concerning factors that motivate mature-age students to choose Science Education Programs, the following were cited: need for self-actualization, acquisition of new knowledge, encouragement from friends and family members, good performance at high school and diploma level, love for the sciences, prestige and desire to be promoted at places of work. As regards factors that affected the academic performance of mature-age students, both negative and positive factors were identified. These included: demographic factors such as age and gender, psychological characteristics such as motivation and preparedness to learn, self-set goals, self esteem, ability, confidence and persistence, student prior academic performance at high school and college level, social factors, institutional factors and the outcomes of the learning process. In order to address the factors that negatively affect academic performance of mature-age students, the following measures were identified: encouraging group discussions, encouraging interactive learning process, providing a conducive learning environment, reviewing Science Education curriculum and providing adequate learning materials. Based on these factors, it is recommended that, the School of Education introduces a program in Science Education specifically for students training to be teachers of science. Additionally, introduce majors in Physics Education, Biology Education, Chemistry Education and Mathematics Education relevant to what is taught in high schools.

Keywords: academic, performance, in-service, science

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Authors: Kevin Fernagut, Olivier Flauzac, Erick M. G. Robledo, Florent Nolot

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The adoption of modern lightweight virtualization often comes with new threats and network vulnerabilities. This paper seeks to assess this with a different approach studying the behavior of a testbed built with tools such as Kernel-Based Virtual Machine (KVM), Linux Containers (LXC) and Docker, by performing stress tests within a platform where students experiment simultaneously with cyber-attacks, and thus observe the impact on the campus network and also find the best solution for cyber-security learning. Interesting outcomes can be found in the literature comparing these technologies. It is, however, difficult to find results of the effects on the global network where experiments are carried out. Our work shows that other physical hosts and the faculty network were impacted while performing these trials. The problems found are discussed, as well as security solutions and the adoption of new network policies.

Keywords: containerization, containers, cybersecurity, cyberattacks, isolation, performance, virtualization, virtual machines

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Authors: Shiuh-Jer Huang, Chen-Zon Yan, C. K. Huang, Chun-Chien Ting

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Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.

Keywords: deep learning, image processing, convolution neural network, YOLOv2, 7A6 series manipulator

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Authors: Brent McKenzie, Victoria Taylor

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The use of QR (Quick Response Codes) codes for customer scanning with mobile phones is a rapidly growing trend. The QR code can provide the consumer with product information, user guides, product use, competitive pricing, etc. One sector for QR use has been in retail, through the use of Electronic Shelf Labeling (henceforth, ESL). In Europe, the use of ESL for pricing has been in practice for a number of years but continues to lag in acceptance in North America. Stated concerns include costs as a key constraint, but there is also evidence that consumer acceptance represents a limitation as well. The purpose of this study is to present the findings of a consumer based study to gage the impact on their use in the retail food sector.

Keywords: electronic price labels, consumer behaviour, grocery shopping, mixed methods research

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Authors: Shidrokh Goudarzi, Wan Haslina Hassan

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Heterogeneous wireless networks are converging towards an all-IP network as part of the so-called next-generation network. In this paradigm, different access technologies need to be interconnected; thus, vertical handovers or vertical handoffs are necessary for seamless mobility. In this paper, we conduct a review of existing vertical handover decision-making mechanisms that aim to provide ubiquitous connectivity to mobile users. To offer a systematic comparison, we categorize these vertical handover measurement and decision structures based on their respective methodology and parameters. Subsequently, we analyze several vertical handover approaches in the literature and compare them according to their advantages and weaknesses. The paper compares the algorithms based on the network selection methods, complexity of the technologies used and efficiency in order to introduce our vertical handover decision framework. We find that vertical handovers on heterogeneous wireless networks suffer from the lack of a standard and efficient method to satisfy both user and network quality of service requirements at different levels including architectural, decision-making and protocols. Also, the consolidation of network terminal, cross-layer information, multi packet casting and intelligent network selection algorithm appears to be an optimum solution for achieving seamless service continuity in order to facilitate seamless connectivity.

Keywords: heterogeneous wireless networks, vertical handovers, vertical handover metric, decision-making algorithms

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Authors: Joo-Hyung Oh, Sekwon Kim, Myoungsun Noh, Chaetae Im

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The number of service users of 4G VoLTE (voice over LTE) using LTE data networks is rapidly growing. VoLTE based on All-IP network enables clearer and higher-quality voice calls than 3G. It does, however, pose new challenges; a voice call through IP networks makes it vulnerable to security threats such as wiretapping and forged or falsified information. Moreover, in particular, stealing other users’ phone numbers and forging or falsifying call request messages from outgoing voice calls within VoLTE result in considerable losses that include user billing and voice phishing to acquaintances. This paper focuses on the threats of caller phone number spoofing in the VoLTE and countermeasure technology as safety measures for mobile communication networks.

Keywords: LTE, 4G, VoLTE, phone number spoofing

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Authors: Ramona-Niculina Jurcau, Ioana-Marieta Jurcau, Dong Hun Kwak, Nicolae-Alexandru Colceriu

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Background: Academic environment led, in time, to the birth of some research subjects concluded with many publications. One of these issues is related to the learning stress. Thus far, the PubMed website displays an impressive number of papers related to the academic stress. Aims: Through this study, we aimed to evaluate the research concerning academic learning stress (ALS), by a retrospective analysis of PubMed publications. Methods: We evaluated the ALS, considering: a) different keywords as - ‘academic stress’ (AS), ‘academic stressors’ (ASs), ‘academic learning stress’ (ALS), ‘academic student stress’ (ASS), ‘academic stress college’ (ASC), ‘medical academic stress’ (MAS), ‘non-medical academic stress’ (NMAS), ‘student stress’ (SS), ‘nursing student stress’ (NS), ‘college student stress’ (CSS), ‘university student stress’ (USS), ‘medical student stress’ (MSS), ‘dental student stress’ (DSS), ‘non-medical student stress’ (NMSS), ‘learning students stress’ (LSS), ‘medical learning student stress’ (MLSS), ‘non-medical learning student stress’ (NMLSS); b) the year average for decades; c) some selection filters provided by PubMed website: Article types - Journal Article (JA), Clinical Trial (CT), Review (R); Species - Humans (H); Sex - Male (M) and Female (F); Ages - 13-18, 19-24, 19-44. Statistical evaluation was made on the basis of the Student test. Results: There were differences between keywords, referring to all filters. Nevertheless, for all keywords were noted the following: the majority of studies have indicated that subjects were humans; there were no important differences between the number of subjects M and F; the age of participants was mentioned only in some studies, predominating those with teenagers and subjects between 19-24 years. Conclusions: 1) PubMed publications document that concern for the research field of academic stress, lasts for 56 years and was materialized in more than 5.010 papers. 2) Number of publications in the field of academic stress varies depending on the selected keywords: those with a general framing (AS, ASs, ALS, ASS, SS, USS, LSS) are more numerous than those with a specific framing (ASC, MAS, NMAS, NS, CSS, MSS, DSS, NMSS, MLSS, NMLSS); those concerning the academic medical environment (MAS, NS, MSS, DSS, MLSS) prevailed compared to the non-medical environment (NMAS, NMSS, NMLSS). 3) Most of the publications are included at JA, of which a small percentage are CT and R. 4) Most of the academic stress studies were conducted with subjects both M and F, most aged under 19 years and between 19-24 years.

Keywords: academic stress, student stress, academic learning stress, medical student stress

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Authors: Willem J. A. Louw

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The wave of new advanced computing technology that has been developed during the recent past has significantly changed the way we communicate, collaborate and collect information. It has created a new technology environment and paradigm in which our children and students grow-up and this impacts on their learning. Research confirmed that Generation Y students have a preference for learning in the new technology environment. The challenge or question is: How do we adjust our teaching and learning to make the most of these changes. The complexity of effective and efficient teaching and learning must not be underestimated and changes must be preceded by proper objective research to prevent any haphazard developments that could do more harm than benefit. A blended learning approach has been used in the Forestry department for a few numbers of years including the use of electronic-peer assisted learning (e-pal) in a fixed-computer set-up within a learning management system environment. It was decided to extend the investigation and do some exploratory research by using a range of different Tablet devices. For this purpose, learning activities or assignments were designed to cover aspects of communication, collaboration and collection of information. The Moodle learning management system was used to present normal module information, to communicate with students and for feedback and data collection. Student feedback was collected by using an online questionnaire and informal discussions. The research project was implemented in 2013, 2014 and 2015 amongst first and third-year students doing a forestry three-year technical tertiary qualification in commercial plantation management. In general, more than 80% of the students alluded to that the device was very useful in their learning environment while the rest indicated that the devices were not very useful. More than ninety percent of the students acknowledged that they would like to continue using the devices for all of their modules whilst the rest alluded to functioning efficiently without the devices. Results indicated that information collection (access to resources) was rated the highest advantageous factor followed by communication and collaboration. The main general advantages of using Tablets were listed by the students as being mobility (portability), 24/7 access to learning material and information of any kind on a user friendly device in a Wi-Fi environment, fast computing process speeds, saving time, effort and airtime through skyping and e-mail, and use of various applications. Ownership of the device is a critical factor while the risk was identified as a major potential constraint. Significant differences were reported between the different types and quality of Tablets. The preferred types are those with a bigger screen and the ones with overall better functionality and quality features. Tablets significantly increase the collaboration, communication and information collection needs of the students. It does, however, not replace the need of a computer/laptop because of limited storage and computation capacity, small screen size and inefficient typing.

Keywords: tablets, teaching, blended learning, tablet quality

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Authors: Bhaveek Maini, Sanjay Dhanka, Surita Maini

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Electroencephalography (EEG) is used to classify several epileptic seizures worldwide. It is a very crucial task for the neurologist to identify the epileptic seizure with manual EEG analysis, as it takes lots of effort and time. Human error is always at high risk in EEG, as acquiring signals needs manual intervention. Disease diagnosis using machine learning (ML) has continuously been explored since its inception. Moreover, where a large number of datasets have to be analyzed, ML is acting as a boon for doctors. In this research paper, authors proposed two different ML models, i.e., logistic regression (LR) and Naïve Bayes (NB), to predict epileptic seizures based on general parameters. These two techniques are applied to the epileptic seizures recognition dataset, available on the UCI ML repository. The algorithms are implemented on an 80:20 train test ratio (80% for training and 20% for testing), and the performance of the model was validated by 10-fold cross-validation. The proposed study has claimed accuracy of 81.87% and 95.49% for LR and NB, respectively.

Keywords: epileptic seizure recognition, logistic regression, Naïve Bayes, machine learning

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Authors: Marcia Figueira

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Context: Positivist transformations in agriculture were responsible for top-down – often coercive – mechanisms of uniformed modernization that weathered local diversities and agency. New development pathways need to now shift according to comprehensive integrations of knowledge - scientific, indigenous, and local, and to be sustained on political interventions, bottom-up change, and social learning if climate goals are to be met – both in mitigation and adaptation. Objectives The objectives of this research are to understand how social-ecological and socio-technical systems characterisation can be nested to bridge scientific research/knowledge into a local context and knowledge system; and, with it, stem sustainable innovation. Methods To do so, we conducted a scoping review to explore theoretical and empirical works linked to Ostrom’s Social-Ecological Systems framework and Geels’ multi-level perspective of socio-technical systems transformations in the context of agriculture. Results As a result, we were able to identify key variables and connections to 1- understand the rules in use and the community attributes influencing resource management; and 2- how they are and have been shaped and shaping systems innovations. Conclusion Based on these results, we discuss how to leverage action research for mutual learning toward a replicable but highly place-based agriculture transformation frame.

Keywords: agriculture systems innovations, social-ecological systems, socio-technical systems, action research

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Authors: Pavlína Částková, Jiří Kropáč, Jan Kubrický

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The paper deals with the current issue of Information and Communication Technologies and their implementation into the educational activities of preschool children. The issue is addressed in the context of technical education and the specifics of its implementation in a kindergarten. One of the main topics of this paper is a technical game activity of a preschool child, and its possibilities, benefits and risks. The paper presents games/toys as one of the means of exploring and understanding technology as an essential part of human culture.

Keywords: ICT, technical education, pre-school age, technical games

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Authors: Lee A. Browne, K. Rumbold

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The first fluorinating enzyme, 5'-fluoro-5'-deoxyadenosine synthase (fluorinase) was isolated from the soil bacterium Streptomyces cattleya. Such an enzyme, with the ability to catalyze a C-F bond, presents great potential as a biocatalyst. Naturally fluorinated compounds are extremely rare in nature. As a result, the number of fluorinases identified remains relatively few. The field of fluorination is almost completely synthetic. However, with the increasing demand for fluorinated organic compounds of commercial value in the agrochemical, pharmaceutical and materials industries, it has become necessary to utilize biologically based methods such as biocatalysts. A key step in this crucial process is the large-scale production of the fluorinase enzyme in considerable quantities for industrial applications. Thus, this study aimed to optimize expression of the fluorinase enzyme in both prokaryotic and eukaryotic expression systems in order to obtain high protein yields. The fluorinase gene was cloned into the pET 41b(+) and pPinkα-HC vectors and used to transform the expression hosts, E.coli BL21(DE3) and Pichia pastoris (PichiaPink™ strains) respectively. Expression trials were conducted to select optimal conditions for expression in both expression systems. Fluorinase catalyses a reaction between S-adenosyl-L-Methionine (SAM) and fluoride ion to produce 5'-fluorodeoxyadenosine (5'FDA) and L-Methionine. The activity of the enzyme was determined using HPLC by measuring the product of the reaction 5'FDA. A gradient mobile phase of 95:5 v/v 50mM potassium phosphate buffer to a final mobile phase containing 80:20 v/v 50mM potassium phosphate buffer and acetonitrile were used. This resulted in the complete separation of SAM and 5’-FDA which eluted at 1.3 minutes and 3.4 minutes respectively. This proved that the fluorinase enzyme was active. Optimising expression of the fluorinase enzyme was successful in both E.coli and PichiaPink™ where high expression levels in both expression systems were achieved. Protein production will be scaled up in PichiaPink™ using fermentation to achieve large-scale protein production. High level expression of protein is essential in biocatalysis for the availability of enzymes for industrial applications.

Keywords: biocatalyst, expression, fluorinase, PichiaPink™

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Authors: Yi Li, Yinan Zheng, Ya Ke, Yungwing Ho

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Motor learning is a process that frequently happens among humans and rodents, which is defined as the changes in the capability to perform a skill that is conformed to have a relatively permanent improvement through practice or experience. There are many ways to learn a behavior, among which is observational learning. Observational learning is the process of learning by watching the behaviors of others, for example, a child imitating parents, learning a new sport by watching the training videos or solving puzzles by watching the solutions. Many research explores observational learning in humans and primates. However, the neuronal mechanism of which, especially observational motor learning, was uncertain. It’s well accepted that mirror neurons are essential in the observational learning process. These neurons fire when the primate performs a goal-directed action and sees someone else demonstrating the same action, which suggests they have high firing activity both completing and watching the behavior. The mirror neurons are assumed to mediate imitation or play a critical and fundamental role in action understanding. They are distributed in many brain areas of primates, i.e., posterior parietal cortex (PPC), premotor cortex (M2), and primary motor cortex (M1) of the macaque brain. However, few researchers report the existence of mirror neurons in rodents. To verify the existence of mirror neurons and the possible role in motor learning in rodents, we performed customised string-pulling behavior combined with multiple behavior analysis methods, photometry, electrophysiology recording, c-fos staining and optogenetics in healthy mice. After five days of training, the demonstrator (demo) mice showed a significantly quicker response and shorter time to reach the string; fast, steady and accurate performance to pull down the string; and more precisely grasping the beads. During three days of observation, the mice showed more facial motions when the demo mice performed behaviors. On the first training day, the observer reduced the number of trials to find and pull the string. However, the time to find beads and pull down string were unchanged in the successful attempts on the first day and other training days, which indicated successful action understanding but failed motor learning through observation in mice. After observation, the post-hoc staining revealed that the c-fos expression was increased in the cognitive-related brain areas (medial prefrontal cortex) and motor cortices (M1, M2). In conclusion, this project indicated that the observation led to a better understanding of behaviors and activated the cognitive and motor-related brain areas, which suggested the possible existence of mirror neurons in these brain areas.

Keywords: observation, motor learning, string-pulling behavior, prefrontal cortex, motor cortex, cognitive

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Authors: Khaldi Youcef

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This investigation seeks to inquire into the effectiveness of using authentic videos for grammar teaching purposes. In this investigation, an English animated situation, Hercules, was used as a type of authentic multimedia to teach a particular grammatical structure, namely conditional sentences. This study also aims at investigating the EFL learners’ attitudes toward grammar learning after being exposed to such an authentic video. To reach that purpose, 56 EFL learners were required ultimately to respond to a questionnaire with an aim to reveal their attitudes towards grammar as a language entity and as a subject for being learned. Then, as a second stage of the investigation, the EFL learners were divided into a control group and an experimental group with 28 learners in each. The first group was taught grammar -conditional sentences- using a deductive-inductive approach, while the second group was exposed to an authentic video to learn conditional sentences. There was a post-lesson stage that included a questionnaire to be answered by learners of each group. The aim of this stage is to capture any change in learners' attitudes shown in the pre-lesson questionnaire. The findings of the first stage revealed learners' negative attitudes towards grammar learning. And the third stage results showed the effectiveness of authentic videos in entirely turning learners' attitudes toward grammar learning to be significantly positive. Also, the utility of authentic videos in highly motivating EFL learners can be deduced. The findings of this survey asserted the need for incorporation and integration of authentic videos in EFL classrooms as they resulted in rising effectively learners’ awareness of grammar and looking at it from a communicative perspective.

Keywords: multimedia, authentic videos, negative attitudes, grammar learning, EFL learners

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Authors: Majed Zobairy, Payam Mohammadpanahi

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Introduction: Skill movement education is one of extremely important duty for sport coaches and sport teachers. Researchers have done lots of studies in this filed to gain the best methodology in movement learning. One of the essential aspects in skill movement education is observational learning. Observational learning, or learning by watching demonstrations, has been characterized as one of the most important methods by which people learn variety of skill and behaviours.The purpose of this study was determined the effect of observational practice on the volleyball service learning with emphasis on the Role of Self–Efficacy. Methods: The Sample consisted of100 male students was assigned accessible sampling technique and homogeneous manner with emphasis on the Role of Self–Efficacy level to 4 groups. The first group performed physical training, the second group performed observational practice task, the third practiced physically and observationally and the fourth group served as the control group. The experimental groups practiced in a one day acquisition and performed the retention task, after 72 hours. Kolmogorov-Smirnov test and independent t-test were used for Statistical analyses. Results and Discussion: Results shows that observation practice task group can significantly improve volleyball services skills acquisition (T=7.73). Also mixed group (physically and observationally) is significantly better than control group regarding to volleyball services skills acquisition (T=7.04). Conclusion: Results have shown observation practice task group and mixed group are significantly better than control group in acquisition test. The present results are in line with previous studies, suggesting that observation learning can improve performance. On the other hand, results shows that self-efficacy level significantly effect on acquisition movement skill. In other words, high self-efficacy is important factor in skill learning level in volleyball service.

Keywords: observational practice, volleyball service, self–efficacy, sport science

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Authors: Raveendranath Veeramani, Parkash Chand, H. Y. Suma, A. Umamageswari

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Background and Aims: The aim of this study was to evaluate students’ perception of Integrated Learning Program[ILP]. Settings and Design: A questionnaire was used to survey and evaluate the perceptions of 1styear MBBS students at the Department of Anatomy at our medical college in India. Materials and Methods: The first MBBS Students of Anatomy were involved in the ILP on the Liver and extra hepatic biliary apparatus integrating the Departments of Anatomy, Biochemistry and Hepato-biliary Surgery. The evaluation of the ILP was done by two sets of short questionnaire that had ten items using the Likert five-point grading scale. The data involved both the students’ responses and their grading. Results: A majority of students felt that the ILP was better in as compared to the traditional lecture method of teaching.The integrated teaching method was better at fulfilling learning objectives (128 students, 83%), enabled better understanding (students, 94%), were more interesting (140 students, 90%), ensured that they could score better in exams (115 students, 77%) and involved greater interaction (100 students, 66%), as compared to traditional teaching methods. Most of the students (142 students, 95%) opined that more such sessions should be organized in the future. Conclusions: Responses from students show that the integrated learning session should be incorporated even at first phase of MBBS for selected topics so as to create interest in the medical sciences at the entry level and to make them understand the importance of basic science.

Keywords: integrated learning, students response, vertical integration, horizontal integration

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Authors: Nynke de Jong, Inge G. P. Duimel-Peeters

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The flipped classroom as an instructional strategy and a type of blended learning that reverses the traditional learning environment by delivering instructional content, off- and online, in- and outside the classroom, has been implemented in a 4-weeks module focusing on ageing in Europe at the Maastricht University. The main aim regarding the organization of this module was implementing flipped classroom-principles in order to create meaningful learning opportunities, while educational technologies are used to deliver content outside of the classroom. Technologies used in this module were an online interactive real time lecture from England, two interactive face-to-face lectures with visual supports, one group session including role plays and team-based learning meetings. The cohort of 2015-2016, using educational technologies, was compared with the cohort of 2014-2015 on module evaluation such as organization and instructiveness of the module, who studied the same content, although conforming the problem-based educational strategy, i.e. educational base of the Maastricht University. The cohort of 2015-2016 with its specific organization, was also more profound evaluated on outcomes as (1) experienced duration of the lecture by students, (2) experienced content of the lecture, (3) experienced the extent of the interaction and (4) format of lecturing. It was important to know how students reflected on duration and content taken into account their background knowledge so far, in order to distinguish between sufficient enough regarding prior knowledge and therefore challenging or not fitting into the course. For the evaluation, a structured online questionnaire was used, whereby above mentioned topics were asked for to evaluate by scoring them on a 4-point Likert scale. At the end, there was room for narrative feedback so that interviewees could express more in detail, if they wanted, what they experienced as good or not regarding the content of the module and its organization parts. Eventually, the response rate of the evaluation was lower than expected (54%), however, due to written feedback and exam scores, we dare to state that it gives a good and reliable overview that encourages to work further on it. Probably, the response rate may be explained by the fact that resit students were included as well, and that there maybe is too much evaluation as some time points in the program. However, overall students were excited about the organization and content of the module, but the level of self-directed behavior, necessary for this kind of educational strategy, was too low. They need to be more trained in self-directness, therefore the module will be simplified in 2016-2017 with more clear and fewer topics and extra guidance (step by step procedure). More specific information regarding the used technologies will be explained at the congress, as well as the outcomes (min and max rankings, mean and standard deviation).

Keywords: blended learning, flipped classroom, public health, self-directness

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Authors: Maria Gonzalez Alriols, Itziar Egues, Maria A. Andres, Mirari Antxustegi

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Peer to peer learning is an educational tool very useful to enhance teamwork and reinforce cooperation between mates. It is particularly successful to work with students of different level of previous knowledge, as it often happens among pupils of subjects in the first course of science and engineering studies. Depending on the performed pre-university academic itinerary, the acquired knowledge in disciplines as mathematics, physics, or chemistry may be quite different. This fact is an added difficulty to the tuition of first-course basic science subjects of engineering degrees, with inexperienced students that do not know each other. In this context, peer to peer learning applied in small groups facilitates the communication between mates and makes it easier for the students with low level to be helped by the ones with better prior knowledge. In this work, several collaborative learning strategies were designed to be applied in the tuition of the subject 'chemistry', which is imparted in the first course of an engineering degree. Students were organized in groups combining mates with different level of prior knowledge. The teaching role was offered to the more experienced students who were responsible for designing learning pills to help the other mates in their group. This workload was rewarded with an extra mark, and more extra points were offered to all the group mates if every student in the group reached a determined level at the end of the semester. It was very important to start these activities from the beginning of the semester in order to avoid absenteeism. The obtained results were positive as a higher percentage of mates signed up and passed the final exam, the obtained final marks were higher, and a much better atmosphere was observed in the class.

Keywords: peer to peer tuition, collaborative learning, engineering instruction, chemistry

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Authors: Leah Lee

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This paper examines the correlation between deep learning models and emotions with noises to see whether or not noises mask emotions. The deep learning models used are plain convolutional neural networks (CNN), auto-encoder, long short-term memory (LSTM), and Visual Geometry Group-16 (VGG-16). Emotion datasets used are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D), Toronto Emotional Speech Set (TESS), and Surrey Audio-Visual Expressed Emotion (SAVEE). To make it four times bigger, audio set files, stretch, and pitch augmentations are utilized. From the augmented datasets, five different features are extracted for inputs of the models. There are eight different emotions to be classified. Noise variations are white noise, dog barking, and cough sounds. The variation in the signal-to-noise ratio (SNR) is 0, 20, and 40. In summation, per a deep learning model, nine different sets with noise and SNR variations and just augmented audio files without any noises will be used in the experiment. To compare the results of the deep learning models, the accuracy and receiver operating characteristic (ROC) are checked.

Keywords: auto-encoder, convolutional neural networks, long short-term memory, speech emotion recognition, visual geometry group-16

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Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: Yang Zhuo

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This study explores the integration of MIDI technology as one of the important digital technologies in music teaching, from the perspective of teaching practice, into the process of cultivating students' teaching skills. At the same time, the framework elements of the learning environment for music education students are divided into four aspects: digital technology supported learning space, new knowledge learning, teaching methods, and teaching evaluation. In teaching activities, more attention should be paid to students' subjectivity and interaction between them so as to enhance their emotional experience in teaching practice simulation. In the process of independent exploration and cooperative interaction, problems should be discovered and solved, and basic knowledge of music and teaching methods should be exercised in practice.

Keywords: music education, educational technology, MIDI, teacher training

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Authors: P. Klinkesorn, S. Yordchim, T. Gibbs, J. Achariyopas

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Vocabulary and reading skill were examined in terms of teaching and learning via teacher-created website. The aims of this study are 1) to survey students’ opinions on the teacher-created website for learning vocabulary and reading skill 2) to survey the students’ motivation for learning vocabulary and reading skill through the teacher-created website. Motivation was applied to the results of the questionnaires and interview forms. Finding suggests that Teacher-Created Website can increase students’ motivation to read more, build up a large stock of vocabulary and improve their understanding of the vocabulary. Implications for developing both social engagement and emotional satisfaction are discussed.

Keywords: motivation, teacher-created website, Thai students, vocabulary and reading skill

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Authors: Nilubon Kurubanjerdjit, Nattakarn Iam-On, Ka-Lok Ng

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MicroRNAs are small non-coding RNA found in many different species. They play crucial roles in cancer such as biological processes of apoptosis and proliferation. The identification of microRNA-target genes can be an essential first step towards to reveal the role of microRNA in various cancer types. In this paper, we predict miRNA-target genes for lung cancer by integrating prediction scores from miRanda and PITA algorithms used as a feature vector of miRNA-target interaction. Then, machine-learning algorithms were implemented for making a final prediction. The approach developed in this study should be of value for future studies into understanding the role of miRNAs in molecular mechanisms enabling lung cancer formation.

Keywords: microRNA, miRNAs, lung cancer, machine learning, Naïve Bayes, SVM

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Authors: Helen Potkin

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This paper presents a practice-based case study of a project in which the student group designed and planned the curriculum content, classroom activities and assessment briefs in collaboration with the tutor. It focuses on the co-creation of the curriculum within a history and theory module, Researching the Contemporary, which runs for BA (Hons) Fine Art and Art History and for BA (Hons) Art Design History Practice at Kingston University, London. The paper analyses the potential of collaborative approaches to engender students’ investment in their own learning and to encourage reflective and self-conscious understandings of themselves as learners. It also addresses some of the challenges of working in this way, attending to the risks involved and feelings of uncertainty produced in experimental, fluid and open situations of learning. Alongside this, it acknowledges the tensions inherent in adopting such practices within the framework of the institution and within the wider of context of the commodification of higher education in the United Kingdom. The concept underpinning the initiative was to test out co-creation as a creative process and to explore the possibilities of altering the traditional hierarchical relationship between teacher and student in a more active, participatory environment. In other words, the project asked about: what kind of learning could be imagined if we were all in it together? It considered co-creation as producing different ways of being, or becoming, as learners, involving us reconfiguring multiple relationships: to learning, to each other, to research, to the institution and to our emotions. The project provided the opportunity for students to bring their own research and wider interests into the classroom, take ownership of sessions, collaborate with each other and to define the criteria against which they would be assessed. Drawing on students’ reflections on their experience of co-creation alongside theoretical considerations engaging with the processual nature of learning, concepts of equality and the generative qualities of the interrelationships in the classroom, the paper suggests that the dynamic nature of collaborative and participatory modes of engagement have the potential to foster relevant and significant learning experiences. The findings as a result of the project could be quantified in terms of the high level of student engagement in the project, specifically investment in the assessment, alongside the ambition and high quality of the student work produced. However, reflection on the outcomes of the experiment prompts a further set of questions about the nature of positionality in connection to learning, the ways our identities as learners are formed in and through our relationships in the classroom and the potential and productive nature of creative practice in education. Overall, the paper interrogates questions of what it means to work with students to invent and assemble the curriculum and it assesses the benefits and challenges of co-creation. Underpinning it is the argument that, particularly in the current climate of higher education, it is increasingly important to ask what it means to teach and to envisage what kinds of learning can be possible.

Keywords: co-creation, collaboration, learning, participation, risk

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Authors: Zixuan Xu, Yichun Lou, Yang Song, Zihuai Lin

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MediaPipe is an open-source machine learning computer vision framework that can be ported into a multi-platform environment, which makes it easier to use it to recognize the human activity. Based on this framework, many human recognition systems have been created, but the fundamental issue is the recognition of human behavior and posture. In this paper, two methods are proposed to recognize human gestures based on MediaPipe, the first one uses the Adaptive Boosting algorithm to recognize a series of fitness gestures, and the second one uses the Fast Dynamic Time Warping algorithm to recognize 413 continuous fitness actions. These two methods are also applicable to any human posture movement recognition.

Keywords: computer vision, MediaPipe, adaptive boosting, fast dynamic time warping

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Authors: A. K. Tsafe

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The study investigates the efficacy of Special Mathematics Teaching Strategy (SMTS) as against Conventional Mathematics Teaching Strategy (CMTS) in teaching students identified with Mathematics Learning Disabilities (MLDs) – dyslexia, Down syndrome, dyscalculia, etc., in some junior secondary schools around Sokoto metropolis. Errors and misconceptions in learning Mathematics displayed by these categories of students were observed. Theory of variation was used to provide a prism for viewing the MLDs from theoretical perspective. Experimental research design was used, involving pretest-posttest non-randomized approach. Pretest was administered to the intact class taught using CMTS before the class was split into experimental and control groups. Experimental group of the students – those identified with MLDs was taught with SMTS and later mean performance of students taught using the two strategies was sought to find if there was any significant difference between the performances of the students. A null hypothesis was tested at α = 0.05 level of significance. T-test was used to establish the difference between the mean performances of the two tests. The null hypothesis was rejected. Hence, the performance of students, identified with MLDs taught using SMTS was found to be better than their earlier performance taught using CMTS. The study, therefore, recommends amongst other things that teachers should be encouraged to use SMTS in teaching mathematics especially when students are found to be suffering from MLDs and exhibiting errors and misconceptions in the process of learning mathematics.

Keywords: disabilities, errors, learning, misconceptions

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Authors: Yuri Katz, Kun Liu, Arunram Atmacharan

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Due to illiquidity, mispricing on Credit Markets is inevitable. This creates huge challenges to banks and investors as they seek to find new ways of risk valuation and portfolio management in a post-credit crisis world. Here, we analyze the difference in behavior of the spread-to-maturity in investment and high-yield categories of US corporate bonds between 2014 and 2023. Deviation from the theoretical dependency of this measure in the universe under study allows to identify multiple cases of mispriced credit risk. Remarkably, we observe mispriced bonds in both categories of credit ratings. This identification is supported by the application of the state-of-the-art machine learning model in more than 90% of cases. Noticeably, the ML-driven model-based forecasting of a category of bond’s credit ratings demonstrate an excellent out-of-sample accuracy (AUC = 98%). We believe that these results can augment conventional valuations of credit portfolios.

Keywords: credit risk, credit ratings, bond pricing, spread-to-maturity, machine learning

Procedia PDF Downloads 63