Search results for: learning management systems

Authors: A. K. Tsafe

Abstract:

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

Procedia PDF Downloads 77

Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Kathaleeya Chanda, Panupong Chanplin, Suppara Charoenpoom

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This purposes of the development of creative instruction model through digital media are to: 1) enable learners to learn from instruction media application; 2) help learners implementing instruction media correctly and appropriately; and 3) facilitate learners to apply technology for searching information and practicing skills to implement technology creatively. The sample group consists of 130 cases of secondary students studying in Bo Kluea School, Bo Kluea Nuea Sub-district, Bo Kluea District, Nan Province. The probability sampling was selected through the simple random sampling and the statistics used in this research are percentage, mean, standard deviation and one group pretest – posttest design. The findings are summarized as follows: The congruence index of instruction media for occupation and technology subjects is appropriate. By comparing between learning achievements before implementing the instruction media and learning achievements after implementing the instruction media, it is found that the posttest achievements are higher than the pretest achievements with statistical significance at the level of .05. For the learning achievements from instruction media implementation, pretest mean is 16.24 while posttest mean is 26.28. Besides, pretest and posttest results are compared and differences of mean are tested, the test results show that the posttest achievements are higher than the pretest achievements with statistical significance at the level of .05. This can be interpreted that the learners achieve better learning progress.

Keywords: teaching learning model, digital media, creative instruction model, Bo Kluea school

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Authors: Seyed Hossein Iranmanesh, Hassan Nouri, Seyed Reza Iranmanesh

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In the dynamic and complex landscape of today’s business, organizations often face challenges in impactful decision-making across multi-project settings. To efficiently allocate resources, coordinate tasks, and optimize project outcomes, establishing robust decision-making processes is essential. Furthermore, the increasing importance of information systems and their integration within organizational workflows introduces an additional layer of complexity. This research proposes the use of blockchain technology as a suitable solution to enhance decision-making in multi-project environments, particularly within the realm of information systems. The conceptual framework in this study comprises four independent variables and one dependent variable. The identified independent variables for the targeted research include: Blockchain Layer in Integrated Systems, Quality of Generated Information ,User Satisfaction with Integrated Systems and Utilization of Integrated Systems. The project’s performance, considered as the dependent variable and moderated by organizational policies and procedures, reflects the impact of blockchain technology adoption on organizational effectiveness1. The results highlight the significant influence of blockchain implementation on organizational performance.

Keywords: multi-project environments, decision support systems, information systems, blockchain technology, decentralized systems.

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Authors: Chengxuan Geng, Yizhou E

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This paper mainly examines the changes in the relationship between earnings management and audit quality before and after financial crisis in the context of American firms from 2005 to 2010. Based on a sample of 3584 firm year observations, we find that there are changes concerning the relation between accrual-based earnings management and audit quality during the pre-crisis and post-crisis periods. However, the results do not provide enough evidence with regard to the variances in the association between real activities earnings management and audit quality during these two periods.

Keywords: audit quality, earnings management, financial crisis, relationship

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Authors: Alexander Street, Nina Wollersberger, Paul Fernie, Leonardo Muller, Ming Hung HSU, Helen Odell-Miller, Jorg Fachner, Patrizia Di Campli San Vito, Stephen Brewster, Hari Shaji, Satvik Venkatesh, Paolo Itaborai, Nicolas Farina, Alexis Kirke, Sube Banerjee, Eduardo Reck Miranda

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Escalating neuropsychiatric symptoms (NPS) in people with dementia may lead to earlier care home admission. Music listening has been reported to stimulate cognitive function, potentially reducing agitation in this population. We present a scoping review, reporting on current developments and discussing the potential for music listening with related technology in managing agitation in dementia care. Of two searches for music listening studies, one focused on older people or people living with dementia where music listening interventions, including technology, were delivered in participants’ homes or in institutions to address neuropsychiatric symptoms, quality of life and independence. The second included any population focusing on the use of music technology for health and wellbeing. In search one 70/251 full texts were included. The majority reported either statistical significance (6, 8.5%), significance (17, 24.2%) or improvements (26, 37.1%). Agitation was specifically reported in 36 (51.4%). The second search included 51/99 full texts, reporting improvement (28, 54.9%), significance (11, 21.5%), statistical significance (1, 1.9%) and no difference compared to the control (6, 11.7%). The majority in the first focused on mood and agitation, and the second on mood and psychophysiological responses. Five studies used AI or machine learning systems to select music, all involving healthy controls and reporting benefits. Most studies in both reviews were not conducted in a home environment (review 1 = 12; 17.1%; review 2 = 11; 21.5%). Preferred music listening may help manage NPS in the care home settings. Based on these and other data extracted in the review, a reasonable progression would be to co-design and test music listening systems and protocols for NPS in all settings, including people’s homes. Machine learning and automated technology for music selection and arousal adjustment, driven by live biodata, have not been explored in dementia care. Such approaches may help deliver the right music at the appropriate time in the required dosage, reducing the use of medication and improving quality of life.

Keywords: music listening, dementia, agitation, scoping review, technology

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Authors: Tayyibah Khanam, Mohammad Saad Alam, Sanchari Deb, Yasser Rafat

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Electric vehicles are considered as viable replacements to gasoline cars since they help in reducing harmful emissions and stimulate power generation through renewable energy sources, hence contributing to sustainability. However, one of the significant obstacles in the mass deployment of electric vehicles is the charging time anxiety among users and, thus, the subsequent large waiting times for available chargers at charging stations. Data analytics, on the other hand, has revolutionized the decision-making tasks of management and operating systems since its arrival. In this paper, we attempt to optimize the choice of EV charging stations for users in their vicinity by minimizing the time taken to reach the charging stations and the waiting times for available chargers. Time taken to travel to the charging station is calculated by the Google Maps API and the waiting times are predicted by polynomial regression of the historical data stored. The proposed framework utilizes real-time data and historical data from all operating charging stations in the city and assists the user in finding the best suitable charging station for their current situation and can be implemented in a mobile phone application. The algorithm successfully predicts the most optimal choice of a charging station and the minimum required time for various sample data sets.

Keywords: charging data, electric vehicles, machine learning, waiting times

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Authors: Bola M. Tunde-Awe

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The study investigated the influence of teacher’s non-verbal communication on secondary school students’ learning outcomes in English language. The study was a survey research. Participants were three hundred Senior Secondary School II students randomly selected from ten schools in Akoko South West Local Government Area of Ondo State, Nigeria. The instrument used for data collection was a questionnaire containing twenty items on a four-point Likert scale which measured teacher’s use of three types of non-verbal communication modes: body movement, eye contact and spatial distance. The data collected was analysed using simple percentage. Findings revealed that teacher’s use of these non-verbal communication modes enhanced learners’ learning outcomes in English language: a total of 271 (90.33%) participants affirmed that teacher’s body language influenced their learning of English; 224 (74.66%) maintained the same stand for eye contact; while 202 (67.33%) affirmed that teacher’s spatial distance had positive influence. Consequent upon these findings, it was recommended that teachers of English language should constantly utilize non-verbal communication in their instructional delivery. Also, non-verbal communication modes should be included in teacher education programme to equip prospective pre-service teachers with the art of non-verbal communication.

Keywords: non-verbal communication, body language, eye contact, spatial distance, learning outcomes

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Authors: Wanlapa Boonsod, Nisachon Yangprasong, Udomsak Kitthawee

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Thermal physics education is a complicated and challenging topic to discuss in any classroom. As a result, most students tend to be uninterested in learning this topic. In the current study, a convection experiment set was devised to show how heat can be transferred by a convection system to a thermoelectric plate until a LED flashes. This research aimed to 1) create a natural convection experimental set, 2) study learning achievement on the convection experimental set with the predict-observe-explain (POE) technique, and 3) study satisfaction for the convection experimental set with the predict-observe-explain (POE) technique. The samples were chosen by purposive sampling and comprised 28 students in grade 11 at Patumkongka School in Bangkok, Thailand. The primary research instrument was the plan for predict-observe-explain (POE) technique on heat transfer using a convection experimental set. Heat transfer experimental set by convection. The instruments used to collect data included a heat transfer achievement model by convection, a Satisfaction Questionnaire after the learning activity, and the predict-observe-explain (POE) technique for heat transfer using a convection experimental set. The research format comprised a one-group pretest-posttest design. The data was analyzed by GeoGebra program. The statistics used in the research were mean, standard deviation and t-test for dependent samples. The results of the research showed that achievement on heat transfer using convection experimental set was composed of thermo-electrics on the top side attached to the heat sink and another side attached to a stainless plate. Electrical current was displayed by the flashing of a 5v LED. The entire set of thermo-electrics was set up on the top of the box and heated by an alcohol burner. The achievement of learning was measured with the predict-observe-explain (POE) technique, with the natural convection experimental set statistically higher than before learning at a 0.01 level. Satisfaction with POE for physics learning of heat transfer by using convection experimental set was at a high level (4.83 from 5.00).

Keywords: convection, heat transfer, physics education, POE

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Authors: Siti Marlia Abd Rahim, Mad Khir Johari Abdullah Sani

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The ever-increasing use of social media applications by library users has raised concerns about the purpose and effectiveness of these platforms in academic libraries. While social media has the potential to revolutionize library services, its usage for non-educational purposes and security concerns have hindered its full potential. This paper aims to address the user behavioral factors affecting social media engagement in academic libraries and examine the impact of social media engagement on user participation. Additionally, it seeks to measure the effect of user participation in social media on the development of powerful learning communities.

Keywords: social media adoption, social media engagement, academic library, social media in academic library, learning community

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Authors: Nurhak Altın, Ayhan Kucukmanisa, Oguzhan Urhan

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It is important that highways are in good condition for traffic safety. Road crashes (road cracks, erosion of lane markings, etc.) can cause accidents by affecting driving. Image processing based methods for detecting road cracks are available in the literature. In this paper, a deep learning based road crack detection approach is proposed. YOLO (You Look Only Once) is adopted as core component of the road crack detection approach presented. The YOLO network structure, which is developed for object detection, is trained with road crack images as a new class that is not previously used in YOLO. The performance of the proposed method is compared using different training methods: using randomly generated weights and training their own pre-trained weights (transfer learning). A similar training approach is applied to the simplified version of the YOLO network model (tiny yolo) and the results of the performance are examined. The developed system is able to process 8 fps on NVIDIA Jetson TX1 development kit.

Keywords: deep learning, embedded platform, real-time processing, road crack detection

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: air quality prediction, deep learning algorithms, time series forecasting, look-back window

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Authors: Argyro Maria Skourmalla

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The present paper draws on the example of the University of Luxembourg as a multilingual and international setting. The University of Luxembourg, which is located between France, Germany, and Belgium, has adopted a new multilingualism policy in 2020, establishing English, French, German, and Luxembourgish as the official languages of the Institution. With around 7.000 students, more than half of which are international students, the University is a meeting point for languages and cultures. This paper includes data from an online survey that with undergraduate students from different disciplines at the University of Luxembourg. Students shared their personal experience and opinions regarding language use in this higher education context, as well as practices they use in learning in this multilingual context. Findings show the role of technology in assisting students in different aspects of learning this multilingual context. At the same time, more needs to be done to avoid an exclusively monolingual paradigm in higher education. Findings also show that some languages remain ‘unseen’ in this context. Overall, even though linguistic diversity in this University is seen as an asset, a lot needs to be done towards the recognition of staff and students’ linguistic repertoires for inclusion and education equity.

Keywords: higher education, learning, linguistic diversity, multilingual practices

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Yi-Fung Lin, Yuan-Mei Liao

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Background: Pain is common among patients with cancer and is also one of the most disturbing symptoms. As this suffering is subjective, if patients proactively participate in their pain self-management, pain could be alleviated effectively. However, not everyone can carry out self-management very well because human behavior is a product of the cognition process. In this process, we can see that "self-efficacy" plays an essential role in affecting human behaviors. Methods: We used the eight steps of concept analysis proposed by Walker and Avant to clarify the concept of “self-efficacy for cancer pain management.” A comprehensive literature review was conducted for relevant publications that were published during the period of 1977 to 2021. We used several keywords, including self-efficacy, self-management, concept analysis, conceptual framework, and cancer pain, to search the following databases: PubMed, CINAHL, Web of Science, and Embase. Results: We identified three defining attributes for the concept of self-efficacy for cancer pain management, including pain management abilities, confidence, and continuous pain monitoring, and recognized six skills related to pain management abilities: problem-solving, decision-making, resource utilization, forming partnerships between medical professionals and patients, planning actions, and self-regulation. Five antecedents for the concept of self-efficacy for cancer pain management were identified: pain experience, existing cancer pain, pain-related knowledge, a belief in pain management, and physical/mental state. Consequences related to self-efficacy for cancer pain management were achievement of pain self-management, well pain control, satisfying quality of life, and containing motivation. Conclusions: This analysis provides researchers with a clearer understanding of the concept of “self-efficacy for cancer pain management.” The findings presented here provide a foundation for future research and nursing interventions to enhance self-efficacy for cancer pain management.

Keywords: cancer pain, concept analysis, self-efficacy, self-management

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Authors: Abdullah Eqal Al Mazrooei

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In this paper, a nonlinear dynamical system is presented. This system is a bilinear class. The bilinear systems are very important kind of nonlinear systems because they have many applications in real life. They are used in biology, chemistry, manufacturing, engineering, and economics where linear models are ineffective or inadequate. They have also been recently used to analyze and forecast weather conditions. Bilinear systems have three advantages: First, they define many problems which have a great applied importance. Second, they give us approximations to nonlinear systems. Thirdly, they have a rich geometric and algebraic structures, which promises to be a fruitful field of research for scientists and applications. The type of nonlinearity that is treated and analyzed consists of bilinear interaction between the states vectors and the system input. By using some properties of the tensor product, these systems can be transformed to linear systems. But, here we discuss the nonlinearity when the state vector is multiplied by itself. So, this model will be able to handle evolutions according to the Lotka-Volterra models or the Lorenz weather models, thus enabling a wider and more flexible application of such models. Here we apply by using an estimator to estimate temperatures. The results prove the efficiency of the proposed system.

Keywords: Lorenz models, nonlinear systems, nonlinear estimator, state-space model

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Authors: Ali Amin

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This paper describes a flipped learning initiative which was trialed in the delivery of the course: structural analysis and modelling. A short series of interactive videos were developed, which introduced the key concepts of each topic. The purpose of the videos was to introduce concepts and give the students more time to develop their thoughts prior to the lecture. This allowed more time for face to face engagement during the lecture. As part of the initial study, videos were developed for half the topics covered. The videos included a short summary of the key concepts ( < 10 mins each) as well as fully worked-out examples (~30mins each). Qualitative feedback was attained from the students. On a scale from strongly disagree to strongly agree, students were rate statements such as 'The pre-class videos assisted your learning experience', 'I felt I could appreciate the content of the lecture more by watching the videos prior to class'. As a result of the pre-class engagement, the students formed more specific and targeted questions during class, and this generated greater comprehension of the material. The students also scored, on average, higher marks in questions pertaining to topics which had videos assigned to them.

Keywords: flipped learning, structural analysis, pre-class videos, engineering education

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Authors: Mai Maitiaili Tuerdi

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Turkey is one of the countries in which fast food service is growing increasingly in the world. The emergence of KFC and McDonald's to Turkish market is affecting every aspects of local fast-food services. The Turkey's famous catering companies named "Simit Sarayi" and "Güllüoğlu" are famous for accepting the Western fast food management service and skills in order to increase their market share. Also, they have created their unique management skills in this field. In this paper, through empirical and comparative study method we will analyze the famous Turkish local fast-food companies and western human resource management. We will argue how to create and adapt the human resource management while the company is economically and socially growing.

Keywords: human resources management, Turkey, fast food, management

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

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In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

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Authors: Chih-Ping Chen

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Despite the increasing popularity of and interest in mobile games, there has been little research that evaluates gender differences in users’ actual preferences for mobile game content, and the factors that influence entertainment and mobile-learning habits. To fill this void, this study examines different gender users’ experience of mobile English learning game adoption in order to identify the areas of development in Taiwan, using Uses and Gratification Theory, Expectation Confirmation Theory and experiential value. The integration of these theories forms the basis of an extended research concept. Users’ responses to questions about cognitive perceptions, confirmation, gratifications and continuous use were collected and analyzed with various factors derived from the theories.

Keywords: expectation confirmation theory, experiential value, gender difference, mobile game, uses and gratification

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Authors: Peter Adly Hamdy Fahmy

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The aim of the study was to examine the effects of a physical education program on student learning by combining the teaching of personal and social responsibility (TPSR) with a physical education model and TPSR with a traditional teaching model, these learning outcomes involving self-learning. -Study. Athletic performance, enthusiasm for sport, group cohesion, sense of responsibility and game performance. The participants were 3 secondary school physical education teachers and 6 physical education classes, 133 participants with students from the experimental group with 75 students and the control group with 58 students, and each teacher taught the experimental group and the control group for 16 weeks. The research methods used surveys, interviews and focus group meetings. Research instruments included the Personal and Social Responsibility Questionnaire, Sports Enthusiasm Scale, Group Cohesion Scale, Sports Self-Efficacy Scale, and Game Performance Assessment Tool. Multivariate analyzes of covariance and repeated measures ANOVA were used to examine differences in student learning outcomes between combining the TPSR with a physical education model and the TPSR with a traditional teaching model. The research findings are as follows: 1) The TPSR sports education model can improve students' learning outcomes, including sports self-efficacy, game performance, sports enthusiasm, team cohesion, group awareness and responsibility. 2) A traditional teaching model with TPSR could improve student learning outcomes, including sports self-efficacy, responsibility, and game performance. 3) The sports education model with TPSR could improve learning outcomes more than the traditional teaching model with TPSR, including sports self-efficacy, sports enthusiasm, responsibility and game performance. 4) Based on qualitative data on teachers' and students' learning experience, the physical education model with TPSR significantly improves learning motivation, group interaction and sense of play. The results suggest that physical education with TPSR could further improve learning outcomes in the physical education program. On the other hand, the hybrid model curriculum projects TPSR - Physical Education and TPSR - Traditional Education are good curriculum projects for moral character education that can be used in school physics.

Keywords: approach competencies, physical, education, teachers employment, graduate, physical education and sport sciences, SWOT analysis character education, sport season, game performance, sport competence

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Authors: Shawndra Bowers, Allie Brandriet, Betsy Gilbertson

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This paper will highlight how Auburn Online’s instructional designers leveraged student and faculty data to update and improve online course design and instructional materials. When designing and revising online courses, it can be difficult for faculty to know what strategies are most likely to engage learners and improve educational outcomes in a specific discipline. It can also be difficult to identify which metrics are most useful for understanding and improving teaching, learning, and course design. At Auburn Online, the instructional designers use a suite of data based student’s performance, participation, satisfaction, and engagement, as well as faculty perceptions, to inform sound learning and design principles that guide growth-mindset consultations with faculty. The consultations allow the instructional designer, along with the faculty member, to co-create an actionable course improvement plan. Auburn Online gathers learning analytics from a variety of sources that any instructor or instructional design team may have access to at their own institutions. Participation and performance data, such as page: views, assignment submissions, and aggregate grade distributions, are collected from the learning management system. Engagement data is pulled from the video hosting platform, which includes unique viewers, views and downloads, the minutes delivered, and the average duration each video is viewed. Student satisfaction is also obtained through a short survey that is embedded at the end of each instructional module. This survey is included in each course every time it is taught. The survey data is then analyzed by an instructional designer for trends and pain points in order to identify areas that can be modified, such as course content and instructional strategies, to better support student learning. This analysis, along with the instructional designer’s recommendations, is presented in a comprehensive report to instructors in an hour-long consultation where instructional designers collaborate with the faculty member on how and when to implement improvements. Auburn Online has developed a triage strategy of priority 1 or 2 level changes that will be implemented in future course iterations. This data-informed decision-making process helps instructors focus on what will best work in their teaching environment while addressing which areas need additional attention. As a student-centered process, it has created improved learning environments for students and has been well received by faculty. It has also shown to be effective in addressing the need for improvement while removing the feeling the faculty’s teaching is being personally attacked. The process that Auburn Online uses is laid out, along with the three-tier maintenance and revision guide that will be used over a three-year implementation plan. This information can help others determine what components of the maintenance and revision plan they want to utilize, as well as guide them on how to create a similar approach. The data will be used to analyze, revise, and improve courses by providing recommendations and models of good practices through determining and disseminating best practices that demonstrate an impact on student success.

Keywords: data-driven, improvement, online courses, faculty development, analytics, course design

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Authors: Sufia Ferdousi

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Child-friendly education (CFE) is very important for the children, especially the early year’s students, because it fosters the holistic development of a child. Teacher plays a key role in creating child-friendly education. This study intends to learn about child-friendly education in Bangladesh. The purpose of the study is to explore how CFE is being practiced in Bangladesh. The study attempted to fulfill the purpose through case study investigation. One school, named Nalanda, was selected for the study as it claims to run the school through CFE approach. The objective of the study was to identify, how this school is different from the other schools in Bangladesh, to explore overall teaching learning system like, curriculum, teaching strategies, assessments and to investigate the support system for Child Friendly Education provided to the teachers through training or mentoring. The nature of the case study was qualitative method to get maximum information from the students, parents, teachers and school authorities. The findings were based on 3 classroom observations, interviews with 1 teacher, 1 head teacher and 1 trainer, FGD with 10 students and 6 parents, were used to collect the data. It has been found that Nalanda is different than the other schools in Bangladesh in terms of, parents’ motivation about school curriculum, and sufficiency of teachers’ knowledge on joyful learning/child-friendly learning. The students took part in the extracurricular activities alongside the national curriculum. Teachers showed particular strength in the teaching learning strategies, using materials and assessment. And Nalanda gives strong support for teacher’s training. In conclusion, The Nalanda School in Dhaka was found appropriate for the requirements of Child-friendly education.

Keywords: child friendly education, overall teaching learning system, the requirements of child-friendly education, the alternative education approach

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Authors: Vasudeva Murthy Challakere Ramaswamy

Abstract:

Background: Conventionally forensic pathology is learnt through autopsy demonstrations which carry various limitations such as unavailability of cases in the mortuary, medico-legal implication and infection. Over the years forensic pathology and science has undergone significant evolution in this digital world. Forensic imaging is a technology which can be effectively utilized for overcoming the current limitations in the undergraduate learning of forensic curriculum. Materials and methods: demonstration of forensic imaging was done using a novel technology of autopsy which has been recently introduced across the globe. Three sessions were conducted in international medical university for a total of 196 medical students. The innovative educational tool was evacuated by using quantitative questionnaire with the scoring scales between 1 to 10. Results: The mean score for acceptance of new tool was 82% and about 74% of the students recommended incorporation of the forensic imaging in the regular curriculum. 82% of students were keen on collaborative research and taking further training courses in forensic imaging. Conclusion: forensic imaging can be an effective tool and also a suitable alternative for teaching undergraduate students. This feedback also supports the fact that students favour the use of contemporary technologies in learning medicine.

Keywords: forensic imaging, forensic pathology, medical students, learning tool

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Authors: Abeer A. Aljohani

Abstract:

COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Won Jung, Azianti Ismail

Abstract:

Functional safety is about electrical, electronics, and programmable electronic safety-related system focuses on the potential risk of malfunction which may have a significant impact on the safety of humans and/or the environment based on IEC 61508. In November 2011, the automotive industry has been introduced to automotive functional safety ISO 26262 which addresses the complete safety installation from sensor to actuator with its technical as well as management issues. Nowadays, most of the modern automobiles are equipped with embedded electronic systems which include many Electronic Controller Units (ECUs), electronic sensors, signals, bus systems and coding. Due to upcoming more sophisticated systems installed in automobiles, the need to carry out detailed safety is very crucial. Assimilation of existing practices with this new standard is a major challenge for the automotive industry in reducing redundancy, time and resources. Therefore, this paper will analyze the research trends on pre and post introduction of ISO 26262 through publications as well as to take a glimpse in the activities for implementing this standard by the automotive manufacturers around the world. It is going to highlight issues and challenges which have been discussed among the experts in this field. Even though it will take some time for this standard to be fully implemented, the benefits from this implementation will raise the competitiveness in the global automotive market.

Keywords: ISO 26262, automotive, functional safety, implementation, standard, challenges

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Authors: Ibrahim Alhassan Libata

Abstract:

This study was carried out to assess attitude of secondary school students towards science and technical education in Yauri metropolis, Kebbi State, Nigeria. The population of the study was 200. Proportionate random sampling method was used in selecting 132 as sample size. Science and technical education is the most powerful forces for change in the world today, and students who hope to have a hand in shaping a better future must participate for their advancements. Four Null hypotheses were generated to guide the conduct of the study, questionnaire was the only instrument used in the study; the instrument was subjected to test-retest reliability. The reliability index of the instrument was 0.69. Overall scores of the Students were analyzed and a mean score was determined, the mean score of students was 85. There were no significant differences between the attitudes of male and female students towards science and technical education. The results also revealed that there was significant difference between the attitude of boding and day school students towards science and technical education, personality constraints of students is one factor militating against the participation of students in science and technical education, socio-economic status of the parents over the years have been the dominant factor of student’s inadequate representation in the field of science and technical education. Based on the findings of this study, the researcher recommended that teachers should motivate students, which they can do through their teaching styles and by showing them the relevance of the learning topics to their everyday lives. Government and the school management should create the learning environment that helps motivate students not only to come to classes but also want to learn and enjoy learning science and technical education, establishment of more Science and Technical Colleges education, more Public enlightenment campaigns to motivate parents and the entire community to support their children in studying science and technical education.

Keywords: attitude, students, science, Yauri

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Authors: Lori L. Scarlatos, Ryan Courtney

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This paper explores the impact that playing a casual game can have on a player's learning and subsequent behavior. A casual mobile game, Container Chaos, was created to teach undergraduate students about the carbon footprint of various disposable beverage containers. Learning was tested with a short quiz, and behavior was tested by observing which beverage containers players choose when offered a drink and a snack. The game was tested multiple times, under a variety of different circumstances. Findings of these tests indicate that, with extended play over time, players can learn new information and sometimes even change their behavior as a result. This has implications for how other casual games can be used to teach concepts and possibly modify behavior.

Keywords: behavior, carbon footprint, casual games, environmental impact, material sciences

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Authors: M. M. Essa, S. Subash, M. Akbar, S. Al-Adawi, A. Al-Asmi, G. J. Guillemein

Abstract:

Objective: Transgenic (tg) mice which contain an amyloid precursor protein (APP) gene mutation, develop extracellular amyloid beta (Aβ) deposition in the brain, and severe memory and behavioural deficits with age. These mice serve as an important animal model for testing the efficacy of novel drug candidates for the treatment and management of symptoms of Alzheimer's disease (AD). Several reports have suggested that oxidative stress is the underlying cause of Aβ neurotoxicity in AD. Pomegranates contain very high levels of antioxidants and several medicinal properties that may be useful for improving the quality of life in AD patients. In this study, we investigated the effect of dietary supplementation of Omani pomegranate extract on the memory, anxiety and learning skills along with inflammation in an AD mouse model containing the double Swedish APP mutation (APPsw/Tg2576). Methods: The experimental groups of APP-transgenic mice from the age of 4 months were fed custom-mix diets (pellets) containing 4% pomegranate. We assessed spatial memory and learning ability, psychomotor coordination, and anxiety-related behavior in Tg and wild-type mice at the age of 4-5 months and 18-19 months using the Morris water maze test, rota rod test, elevated plus maze test, and open field test. Further, inflammatory parameters also analysed. Results: APPsw/Tg2576 mice that were fed a standard chow diet without pomegranates showed significant memory deficits, increased anxiety-related behavior, and severe impairment in spatial learning ability, position discrimination learning ability and motor coordination along with increased inflammation compared to the wild type mice on the same diet, at the age of 18-19 months In contrast, APPsw/Tg2576 mice that were fed a diet containing 4% pomegranates showed a significant improvements in memory, learning, locomotor function, and anxiety with reduced inflammatory markers compared to APPsw/Tg2576 mice fed the standard chow diet. Conclusion: Our results suggest that dietary supplementation with pomegranates may slow the progression of cognitive and behavioural impairments in AD. The exact mechanism is still unclear and further extensive research needed.

Keywords: Alzheimer's disease, pomegranates, oman, cognitive decline, memory loss, anxiety, inflammation

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Authors: Eny Syatriana, Ardiansyah

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The purpose of recent study was to develop a learning model on writing, based on the reading texts which will be read by the students using reflection strategy. The strategy would allow the students to read the text and then they would write back the main idea and to develop the text by using their own sentences. So, the writing practice was begun by reading an interesting text, then the students would develop the text which has been read into their writing. The problem questions are (1) what kind of learning model that can develop the students writing ability? (2) what is the achievement of the students of STKIP YPUP through reflection strategy? (3) is the using of the strategy effective to develop students competence In writing? (4) in what level are the students interest toward the using of a strategy In writing subject? This development research consisted of some steps, they are (1) need analysis (2) model design (3) implementation (4) model evaluation. The need analysis was applied through discussion among the writing lecturers to create a learning model for writing subject. To see the effectiveness of the model, an experiment would be delivered for one class. The instrument and learning material would be validated by the experts. In every steps of material development, there was a learning process, where would be validated by an expert. The research used development design. These Principles and procedures or research design and development .This study, researcher would do need analysis, creating prototype, content validation, and limited empiric experiment to the sample. In each steps, there should be an assessment and revision to the drafts before continue to the next steps. The second year, the prototype would be tested empirically to four classes in STKIP YPUP for English department. Implementing the test greatly was done through the action research and followed by evaluation and validation from the experts.

Keywords: learning model, reflection, strategy, reading, writing, development

Procedia PDF Downloads 349