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performance</title> <meta name="description" content="Search results for: learning performance"> <meta name="keywords" content="learning performance"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="learning performance"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 18478</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: learning performance</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18478</span> Enhancing Organizational Performance through Adaptive Learning: A Case Study of ASML</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Shadani">Ramin Shadani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study introduces adaptive performance as a key organizational performance dimension and explores the relationship between the dimensions of a learning organization and adaptive performance. A survey was therefore conducted using the dimensions of the Learning Organization Questionnaire (DLOQ), followed by factor analysis and structural equation modeling in order to investigate the dynamics between learning organization practices and adaptive performance. Results confirm that adaptive performance is indeed one important dimension of organizational performance. The study also shows that perceived knowledge and adaptive performance mediate the positive relationship between the practices of a learning organization with perceived financial performance. We extend existing DLOQ research by demonstrating that adaptive performance, as a nonfinancial organizational learning outcome, has a significant impact on financial performance. Our study also provides additional validation of the measures of DLOQ's performance. Indeed, organizations need to take a glance at how the activities of learning and development can provide better overall improvement in performance, especially in enhancing adaptive capability. The study has provided requisite empirical support that activities of learning and development within organizations allow much-improved intangible performance outcomes, especially through adaptive performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20performance" title="adaptive performance">adaptive performance</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20learning" title=" continuous learning"> continuous learning</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20performance" title=" financial performance"> financial performance</a>, <a href="https://publications.waset.org/abstracts/search?q=leadership%20style" title=" leadership style"> leadership style</a>, <a href="https://publications.waset.org/abstracts/search?q=organizational%20learning" title=" organizational learning"> organizational learning</a>, <a href="https://publications.waset.org/abstracts/search?q=organizational%20performance" title=" organizational performance"> organizational performance</a> </p> <a href="https://publications.waset.org/abstracts/191916/enhancing-organizational-performance-through-adaptive-learning-a-case-study-of-asml" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191916.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">27</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18477</span> Organizational Learning, Job Satisfaction and Work Performance among Nurses</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rafia%20Rafique">Rafia Rafique</a>, <a href="https://publications.waset.org/abstracts/search?q=Arifa%20Khadim"> Arifa Khadim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research investigates the moderating role of job satisfaction between organizational learning and work performance among nurses. Correlation research design was used. Non-probability purposive sampling technique was utilized to recruit a sample of 110 nurses from public hospitals situated in the city of Lahore. The construct of organizational learning was measured using subscale of Integrated Scale for Measuring Organizational Learning. Job satisfaction was measured with the help of Job Satisfaction Survey. Performance of employees (task performance, contextual performance and counterproductive work behavior) was assessed by Individual Work Performance Questionnaire. Job satisfaction negatively moderates the relationship between organizational learning and counterproductive work behavior. Education has a significant positive relationship with organizational learning. Age, current hospital experience, marital satisfaction and salary of the nurses have positive relationship while number of children has significant negative relationship with counterproductive work behavior. These outcomes can be insightful in understanding the dynamics involved in work performance. Based on the result of this study relevant solutions can be proposed to improve the work performance of nurses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=counterproductive%20work%20behavior" title="counterproductive work behavior">counterproductive work behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=nurses" title=" nurses"> nurses</a>, <a href="https://publications.waset.org/abstracts/search?q=organizational%20learning" title=" organizational learning"> organizational learning</a>, <a href="https://publications.waset.org/abstracts/search?q=work%20performance" title=" work performance"> work performance</a> </p> <a href="https://publications.waset.org/abstracts/71137/organizational-learning-job-satisfaction-and-work-performance-among-nurses" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71137.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18476</span> The Differences in Skill Performance Between Online and Conventional Learning Among Nursing Students</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nurul%20Nadrah">Nurul Nadrah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a result of the COVID-19 pandemic, a movement control order was implemented, leading to the adoption of online learning as a substitute for conventional classroom instruction. Thus, this study aims to determine the differences in skill performance between online learning and conventional methods among nursing students. We employed a quasi-experimental design with purposive sampling, involving a total of 59 nursing students, and used online learning as the intervention. As a result, the study found there was a significant difference in student skill performance between online learning and conventional methods. As a conclusion, in times of hardship, it is necessary to implement alternative pedagogical approaches, especially in critical fields like nursing, to ensure the uninterrupted progression of educational programs. This study suggests that online learning can be effectively employed as a means of imparting knowledge to nursing students during their training. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nursing%20education" title="nursing education">nursing education</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20learning" title=" online learning"> online learning</a>, <a href="https://publications.waset.org/abstracts/search?q=skill%20performance" title=" skill performance"> skill performance</a>, <a href="https://publications.waset.org/abstracts/search?q=conventional%20learning%20method" title=" conventional learning method"> conventional learning method</a> </p> <a href="https://publications.waset.org/abstracts/187830/the-differences-in-skill-performance-between-online-and-conventional-learning-among-nursing-students" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187830.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">46</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18475</span> Evaluation Metrics for Machine Learning Techniques: A Comprehensive Review and Comparative Analysis of Performance Measurement Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed-Ali%20Sadegh-Zadeh">Seyed-Ali Sadegh-Zadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaveh%20Kavianpour"> Kaveh Kavianpour</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Atashbar"> Hamed Atashbar</a>, <a href="https://publications.waset.org/abstracts/search?q=Elham%20Heidari"> Elham Heidari</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Shiry%20Ghidary"> Saeed Shiry Ghidary</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20M.%20Hajiyavand"> Amir M. Hajiyavand</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evaluation metrics play a critical role in assessing the performance of machine learning models. In this review paper, we provide a comprehensive overview of performance measurement approaches for machine learning models. For each category, we discuss the most widely used metrics, including their mathematical formulations and interpretation. Additionally, we provide a comparative analysis of performance measurement approaches for metric combinations. Our review paper aims to provide researchers and practitioners with a better understanding of performance measurement approaches and to aid in the selection of appropriate evaluation metrics for their specific applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evaluation%20metrics" title="evaluation metrics">evaluation metrics</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20measurement" title=" performance measurement"> performance measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20robustness%20and%20stability" title=" model robustness and stability"> model robustness and stability</a>, <a href="https://publications.waset.org/abstracts/search?q=comparative%20analysis" title=" comparative analysis"> comparative analysis</a> </p> <a href="https://publications.waset.org/abstracts/184552/evaluation-metrics-for-machine-learning-techniques-a-comprehensive-review-and-comparative-analysis-of-performance-measurement-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184552.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18474</span> Learning Performance of Sports Education Model Based on Self-Regulated Learning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yi-Hsiang%20Pan">Yi-Hsiang Pan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ching-Hsiang%20Chen"> Ching-Hsiang Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei-Ting%20Hsu"> Wei-Ting Hsu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study was to compare the learning effects of the sports education model (SEM) to those of the traditional teaching model (TTM) in physical education classes in terms of students learning motivation, action control, learning strategies, and learning performance. A quasi-experimental design was utilized in this study, and participants included two physical educators and four classes with a total of 94 students in grades 5 and 6 of elementary schools. Two classes implemented the SEM (n=47, male=24, female=23; age=11.89, SD=0.78) and two classes implemented the TTM (n=47, male=25, female=22, age=11.77; SD=0.66). Data were collected from these participants using a self-report questionnaire (including a learning motivation scale, action control scale, and learning strategy scale) and a game performance assessment instrument, and multivariate analysis of covariance was used to conduct statistical analysis. The findings of the study revealed that the SEM was significantly better than the TTM in promoting students learning motivation, action control, learning strategies, and game performance. It was concluded that the SEM could promote the mechanics of students self-regulated learning process, and thereby improve students movement performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=self-regulated%20learning%20theory" title="self-regulated learning theory">self-regulated learning theory</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20process" title=" learning process"> learning process</a>, <a href="https://publications.waset.org/abstracts/search?q=curriculum%20model" title=" curriculum model"> curriculum model</a>, <a href="https://publications.waset.org/abstracts/search?q=physical%20education" title=" physical education"> physical education</a> </p> <a href="https://publications.waset.org/abstracts/51176/learning-performance-of-sports-education-model-based-on-self-regulated-learning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51176.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">342</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18473</span> Education and Learning in Indonesia to Refer to the Democratic and Humanistic Learning System in Finland</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nur%20Sofi%20Hidayah">Nur Sofi Hidayah</a>, <a href="https://publications.waset.org/abstracts/search?q=Ratih%20Tri%20Purwatiningsih"> Ratih Tri Purwatiningsih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Learning is a process attempts person to obtain a new behavior changes as a whole, as a result of his own experience in the interaction with the environment. Learning involves our brain to think, while the ability of the brain to each student's performance is different. To obtain optimal learning results then need time to learn the exact hour that the brain's performance is not too heavy. Referring to the learning system in Finland which apply 45 minutes to learn and a 15-minute break is expected to be the brain work better, with the rest of the brain, the brain will be more focused and lessons can be absorbed well. It can be concluded that learning in this way students learn with brain always fresh and the best possible use of the time, but it can make students not saturated in a lesson. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning" title="learning">learning</a>, <a href="https://publications.waset.org/abstracts/search?q=working%20hours%20brain" title=" working hours brain"> working hours brain</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20efficient%20learning" title=" time efficient learning"> time efficient learning</a>, <a href="https://publications.waset.org/abstracts/search?q=working%20hours%20in%20the%20brain%20receive%20stimulus." title=" working hours in the brain receive stimulus."> working hours in the brain receive stimulus.</a> </p> <a href="https://publications.waset.org/abstracts/39794/education-and-learning-in-indonesia-to-refer-to-the-democratic-and-humanistic-learning-system-in-finland" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39794.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">397</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18472</span> A Study on Performance Prediction in Early Design Stage of Apartment Housing Using Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seongjun%20Kim">Seongjun Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanghoon%20Shim"> Sanghoon Shim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinwooung%20Kim"> Jinwooung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaehwan%20Jung"> Jaehwan Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung-Ah%20Kim"> Sung-Ah Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the development of information and communication technology, the convergence of machine learning of the ICT area and design is attempted. In this way, it is possible to grasp the correlation between various design elements, which was difficult to grasp, and to reflect this in the design result. In architecture, there is an attempt to predict the performance, which is difficult to grasp in the past, by finding the correlation among multiple factors mainly through machine learning. In architectural design area, some attempts to predict the performance affected by various factors have been tried. With machine learning, it is possible to quickly predict performance. The aim of this study is to propose a model that predicts performance according to the block arrangement of apartment housing through machine learning and the design alternative which satisfies the performance such as the daylight hours in the most similar form to the alternative proposed by the designer. Through this study, a designer can proceed with the design considering various design alternatives and accurate performances quickly from the early design stage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=apartment%20housing" title="apartment housing">apartment housing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20prediction" title=" performance prediction"> performance prediction</a> </p> <a href="https://publications.waset.org/abstracts/80644/a-study-on-performance-prediction-in-early-design-stage-of-apartment-housing-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80644.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">481</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18471</span> Performance Evaluation of Distributed Deep Learning Frameworks in Cloud Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuen-Tai%20Wang">Shuen-Tai Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang-An%20Kuo"> Fang-An Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Chau-Yi%20Chou"> Chau-Yi Chou</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Bin%20Fang"> Yu-Bin Fang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a> </p> <a href="https://publications.waset.org/abstracts/110135/performance-evaluation-of-distributed-deep-learning-frameworks-in-cloud-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110135.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">211</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18470</span> Enhancement of Learning Style in Kolej Poly-Tech MARA (KPTM) via Mobile EEF Learning System (MEEFLS)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20E.%20Marwan">M. E. Marwan</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Madar"> A. R. Madar</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Fuad"> N. Fuad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mobile communication provides access to the outside world without borders everywhere and at any time. The learning method that related to mobile communication technology is known as mobile learning (M-learning). It is a method that communicates learning materials with mobile device technology. The purpose of this method is to increase the interest in learning among students and assist them in obtaining learning materials at Kolej Poly-Tech MARA (KPTM) in order to improve the student’s performance in their study and to encourage educators to diversify the teaching practices. This paper discusses the student’s awareness for enhancement of learning style using mobile technologies and their readiness to apply the elements of mobile learning in learning to improve performance and interest in learning among students. An application called Mobile EEF Learning System (MEEFLS) has been developed as a tool to be used as a pilot test in KPTM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=awareness" title="awareness">awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20learning" title=" mobile learning"> mobile learning</a>, <a href="https://publications.waset.org/abstracts/search?q=MEEFLS" title=" MEEFLS"> MEEFLS</a>, <a href="https://publications.waset.org/abstracts/search?q=teaching%20and%20learning" title=" teaching and learning"> teaching and learning</a>, <a href="https://publications.waset.org/abstracts/search?q=readiness" title=" readiness"> readiness</a> </p> <a href="https://publications.waset.org/abstracts/11289/enhancement-of-learning-style-in-kolej-poly-tech-mara-kptm-via-mobile-eef-learning-system-meefls" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11289.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">379</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18469</span> Impact of VARK Learning Model at Tertiary Level Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Munazza%20A.%20Mirza">Munazza A. Mirza</a>, <a href="https://publications.waset.org/abstracts/search?q=Khawar%20Khurshid"> Khawar Khurshid </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Individuals are generally associated with different learning styles, which have been explored extensively in recent past. The learning styles refer to the potential of an individual by which s/he can easily comprehend and retain information. Among various learning style models, VARK is the most accepted model which categorizes the learners with respect to their sensory characteristics. Based on the number of preferred learning modes, the learners can be categorized as uni-modal, bi-modal, tri-modal, or quad/multi-modal. Although there is a prevalent belief in the learning styles, however, the model is not being frequently and effectively utilized in the higher education. This research describes the identification model to validate teacher’s didactic practice and student’s performance linkage with the learning styles. The identification model is recommended to check the effective application and evaluation of the various learning styles. The proposed model is a guideline to effectively implement learning styles inventory in order to ensure that it will validate performance linkage with learning styles. If performance is linked with learning styles, this may help eradicate the distrust on learning style theory. For this purpose, a comprehensive study was conducted to compare and understand how VARK inventory model is being used to identify learning preferences and their correlation with learner’s performance. A comparative analysis of the findings of these studies is presented to understand the learning styles of tertiary students in various disciplines. It is concluded with confidence that the learning styles of students cannot be associated with any specific discipline. Furthermore, there is not enough empirical proof to link performance with learning styles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning%20style" title="learning style">learning style</a>, <a href="https://publications.waset.org/abstracts/search?q=VARK" title=" VARK"> VARK</a>, <a href="https://publications.waset.org/abstracts/search?q=sensory%20preferences" title=" sensory preferences"> sensory preferences</a>, <a href="https://publications.waset.org/abstracts/search?q=identification%20model" title=" identification model"> identification model</a>, <a href="https://publications.waset.org/abstracts/search?q=didactic%20practices" title=" didactic practices"> didactic practices</a> </p> <a href="https://publications.waset.org/abstracts/110251/impact-of-vark-learning-model-at-tertiary-level-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110251.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">277</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18468</span> Impact of Grade Sensitivity on Learning Motivation and Academic Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salwa%20Aftab">Salwa Aftab</a>, <a href="https://publications.waset.org/abstracts/search?q=Sehrish%20Riaz"> Sehrish Riaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this study was to check the impact of grade sensitivity on learning motivation and academic performance of students and to remove the degree of difference that exists among students regarding the cause of their learning motivation and also to gain knowledge about this matter since it has not been adequately researched. Data collection was primarily done through the academic sector of Pakistan and was depended upon the responses given by students solely. A sample size of 208 university students was selected. Both paper and online surveys were used to collect data from respondents. The results of the study revealed that grade sensitivity has a positive relationship with the learning motivation of students and their academic performance. These findings were carried out through systematic correlation and regression analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=academic%20performance" title="academic performance">academic performance</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation" title=" correlation"> correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=grade%20sensitivity" title=" grade sensitivity"> grade sensitivity</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20motivation" title=" learning motivation"> learning motivation</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/53128/impact-of-grade-sensitivity-on-learning-motivation-and-academic-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53128.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">400</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18467</span> The Impact of Training Method on Programming Learning Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chechen%20Liao">Chechen Liao</a>, <a href="https://publications.waset.org/abstracts/search?q=Chin%20Yi%20Yang"> Chin Yi Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although several factors that affect learning to program have been identified over the years, there continues to be no indication of any consensus in understanding why some students learn to program easily and quickly while others have difficulty. Seldom have researchers considered the problem of how to help the students enhance the programming learning outcome. The research had been conducted at a high school in Taiwan. Students participating in the study consist of 330 tenth grade students enrolled in the Basic Computer Concepts course with the same instructor. Two types of training methods-instruction-oriented and exploration-oriented were conducted. The result of this research shows that the instruction-oriented training method has better learning performance than exploration-oriented training method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning%20performance" title="learning performance">learning performance</a>, <a href="https://publications.waset.org/abstracts/search?q=programming%20learning" title=" programming learning"> programming learning</a>, <a href="https://publications.waset.org/abstracts/search?q=TDD" title=" TDD"> TDD</a>, <a href="https://publications.waset.org/abstracts/search?q=training%20method" title=" training method"> training method</a> </p> <a href="https://publications.waset.org/abstracts/10123/the-impact-of-training-method-on-programming-learning-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10123.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">427</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18466</span> Enhancing Nursing Teams' Learning: The Role of Team Accountability and Team Resources</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sarit%20Rashkovits">Sarit Rashkovits</a>, <a href="https://publications.waset.org/abstracts/search?q=Anat%20Drach-%20Zahavy"> Anat Drach- Zahavy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research considers the unresolved question regarding the link between nursing team accountability and team learning and the resulted team performance in nursing teams. Empirical findings reveal disappointing evidence regarding improvement in healthcare safety and quality. Therefore, there is a need in advancing managerial knowledge regarding the factors that enhance constant healthcare teams' proactive improvement efforts, meaning team learning. We first aim to identify the organizational resources that are needed for team learning in nursing teams; second, to test the moderating role of nursing teams' learning resources in the team accountability-team learning link; and third, to test the moderated mediation model suggesting that nursing teams' accountability affects team performance by enhancing team learning when relevant resources are available to the team. We point on the intervening role of three team learning resources, namely time availability, team autonomy and performance data on the relation between team accountability and team learning and test the proposed moderated mediation model on 44 nursing teams (462 nurses and 44 nursing managers). The results showed that, as was expected, there was a positive significant link between team accountability and team learning and the subsequent team performance when time availability and team autonomy were high rather than low. Nevertheless, the positive team accountability- team learning link was significant when team performance feedback was low rather than high. Accordingly, there was a positive mediated effect of team accountability on team performance via team learning when either time availability or team autonomy were high and the availability of team performance data was low. Nevertheless, this mediated effect was negative when time availability and team autonomy were low and the availability of team performance data was high. We conclude that nurturing team accountability is not enough for achieving nursing teams' learning and the subsequent improved team performance. Rather there is need to provide nursing teams with adequate time, autonomy, and be cautious with performance feedback, as the latter may motivate nursing teams to repeat routine work strategies rather than explore improved ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nursing%20teams%27%20accountability" title="nursing teams' accountability">nursing teams' accountability</a>, <a href="https://publications.waset.org/abstracts/search?q=nursing%20teams%27%20learning" title=" nursing teams' learning"> nursing teams' learning</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20feedback" title=" performance feedback"> performance feedback</a>, <a href="https://publications.waset.org/abstracts/search?q=teams%27%20autonomy" title=" teams' autonomy "> teams' autonomy </a> </p> <a href="https://publications.waset.org/abstracts/49816/enhancing-nursing-teams-learning-the-role-of-team-accountability-and-team-resources" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49816.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">264</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18465</span> The Effects of Learning Engagement on Interpreting Performance among English Major Students</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jianhua%20Wang">Jianhua Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ying%20Zhou"> Ying Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Xi%20%20Zhang"> Xi Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To establish the influential mechanism of learning engagement on interpreter’s performance, the present study submitted a questionnaire to a sample of 927 English major students with 804 valid ones and used the structural equation model as the basis for empirical analysis and statistical inference on the sample data. In order to explore the mechanism for interpreting learning engagement on student interpreters’ performance, a path model of interpreting processes with three variables of ‘input-environment-output’ was constructed. The results showed that the effect of each ‘environment’ variable on interpreting ability was different from and greater than the ‘input’ variable, and learning engagement was the greatest influencing factor. At the same time, peer interaction on interpreting performance has significant influence. Results suggest that it is crucial to provide effective guidance for optimizing learning engagement and interpreting teaching research by both improving the environmental support and building the platform of peer interaction, beginning with learning engagement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning%20engagement" title="learning engagement">learning engagement</a>, <a href="https://publications.waset.org/abstracts/search?q=interpreting%20performance" title=" interpreting performance"> interpreting performance</a>, <a href="https://publications.waset.org/abstracts/search?q=interpreter%20training" title=" interpreter training"> interpreter training</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20major%20students" title=" English major students"> English major students</a> </p> <a href="https://publications.waset.org/abstracts/112290/the-effects-of-learning-engagement-on-interpreting-performance-among-english-major-students" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112290.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">207</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18464</span> If You Can't Teach Yourself, No One Can</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Timna%20Mayer">Timna Mayer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explores the vast potential of self-directed learning in violin pedagogy. Based in practice and drawing on concepts from neuropsychology, the author, a violinist and teacher, outlines five learning principles. Self-directed learning is defined as an ongoing process based on problem detection, definition, and resolution. The traditional roles of teacher and student are reimagined within this context. A step-by-step guide to applied self-directed learning suggests a model for both teachers and students that realizes student independence in the classroom, leading to higher-level understanding and more robust performance. While the value of self-directed learning is well-known in general pedagogy, this paper is novel in applying the approach to the study of musical performance, a field which is currently dominated by habit and folklore, rather than informed by science. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neuropsychology%20and%20musical%20performance" title="neuropsychology and musical performance">neuropsychology and musical performance</a>, <a href="https://publications.waset.org/abstracts/search?q=self-directed%20learning" title=" self-directed learning"> self-directed learning</a>, <a href="https://publications.waset.org/abstracts/search?q=strategic%20problem%20solving" title=" strategic problem solving"> strategic problem solving</a>, <a href="https://publications.waset.org/abstracts/search?q=violin%20pedagogy" title=" violin pedagogy"> violin pedagogy</a> </p> <a href="https://publications.waset.org/abstracts/124802/if-you-cant-teach-yourself-no-one-can" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124802.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18463</span> Effect of Leadership Style on Organizational Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khadija%20Mushtaq">Khadija Mushtaq</a>, <a href="https://publications.waset.org/abstracts/search?q=Mian%20Saqib%20Mehmood"> Mian Saqib Mehmood</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper attempts to determine the impact of leadership style and learning orientation on organizational performance in Pakistan. A sample of 158 middle managers selected from sports and surgical factories from Sialkot. The empirical estimation is based on a multiple linear regression analysis of the relationship between leadership style, learning orientation and organizational performance. Leadership style is measure through transformational leadership and transactional leadership. The transformational leadership has insignificant impact on organizational performance. The transactional leadership has positive and significant relation with organizational performance. Learning orientation also has positive and significant relation with organizational performance. Linear regression used to estimate the relation between dependent and independent variables. This study suggests top manger should prefer continuous process for improvement for any change in system rather radical change. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transformational%20leadership" title="transformational leadership">transformational leadership</a>, <a href="https://publications.waset.org/abstracts/search?q=transactional%20leadership" title=" transactional leadership"> transactional leadership</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20orientation" title=" learning orientation"> learning orientation</a>, <a href="https://publications.waset.org/abstracts/search?q=organizational%20performance" title=" organizational performance"> organizational performance</a>, <a href="https://publications.waset.org/abstracts/search?q=Pakistan" title=" Pakistan"> Pakistan</a> </p> <a href="https://publications.waset.org/abstracts/33257/effect-of-leadership-style-on-organizational-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33257.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">404</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18462</span> Coevaluations Software among Students in Active Learning Methodology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adriano%20Pinargote">Adriano Pinargote</a>, <a href="https://publications.waset.org/abstracts/search?q=Josue%20Mosquera"> Josue Mosquera</a>, <a href="https://publications.waset.org/abstracts/search?q=Eduardo%20Montero"> Eduardo Montero</a>, <a href="https://publications.waset.org/abstracts/search?q=Dalton%20Noboa"> Dalton Noboa</a>, <a href="https://publications.waset.org/abstracts/search?q=Jenny%20Venegas"> Jenny Venegas</a>, <a href="https://publications.waset.org/abstracts/search?q=Genesis%20Vasquez%20Escuela"> Genesis Vasquez Escuela </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the framework of Pre University learning of the Polytechnic School of the Litoral, Guayaquil, Ecuador, the methodology of Active Learning (Flipped Classroom) has been implemented for applicants who wish to obtain a quota within the university. To complement the Active Learning cycle, it has been proposed that the respective students influence the qualification of their work groups, for which a web platform has been created that allows them to evaluate the performance of their peers through a digital coevaluation that measures through statistical methods, the group and individual performance score that can reflect in numbers a weighting score corresponding to the grade of each student. Their feedback provided by the group help to improve the performance of the activities carried out in classes because the note reflects the commitment with their classmates shown in the class, within this analysis we will determine if this implementation directly influences the performance of the grades obtained by the student. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20learning" title="active learning">active learning</a>, <a href="https://publications.waset.org/abstracts/search?q=coevaluation" title=" coevaluation"> coevaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=flipped%20classroom" title=" flipped classroom"> flipped classroom</a>, <a href="https://publications.waset.org/abstracts/search?q=pre%20university" title=" pre university"> pre university</a> </p> <a href="https://publications.waset.org/abstracts/125101/coevaluations-software-among-students-in-active-learning-methodology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125101.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18461</span> Hate Speech Detection Using Deep Learning and Machine Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nabil%20Shawkat">Nabil Shawkat</a>, <a href="https://publications.waset.org/abstracts/search?q=Jamil%20Saquer"> Jamil Saquer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social media has accelerated our ability to engage with others and eliminated many communication barriers. On the other hand, the widespread use of social media resulted in an increase in online hate speech. This has drastic impacts on vulnerable individuals and societies. Therefore, it is critical to detect hate speech to prevent innocent users and vulnerable communities from becoming victims of hate speech. We investigate the performance of different deep learning and machine learning algorithms on three different datasets. Our results show that the BERT model gives the best performance among all the models by achieving an F1-score of 90.6% on one of the datasets and F1-scores of 89.7% and 88.2% on the other two datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hate%20speech" title="hate speech">hate speech</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=abusive%20words" title=" abusive words"> abusive words</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a> </p> <a href="https://publications.waset.org/abstracts/164751/hate-speech-detection-using-deep-learning-and-machine-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164751.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18460</span> Competence on Learning Delivery Modes and Performance of Physical Education Teachers in Senior High Schools in Davao</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Juvanie%20C.%20Lapesigue">Juvanie C. Lapesigue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Worldwide school closures result from a significant public health crisis that has affected the nation and the entire world. It has affected students, educators, educational organizations globally, and many other aspects of society. Academic institutions worldwide teach students using diverse approaches of various learning delivery modes. This paper investigates the competence and performance of physical education teachers using various learning delivery modes, including Distance learning, Blended Learning, and Homeschooling during online distance education. To identify the Gap between their age generation using various learning delivery that affects teachers' preparation for distance learning and evaluates how these modalities impact teachers’ competence and performance in the case of a pandemic. The respondents were the Senior High School teachers of the Department of Education who taught in Davao City before and during the pandemic. Purposive sampling was utilized on 61 Senior High School Teachers in Davao City Philippines. The result indicated that teaching performance based on pedagogy and assessment has significantly affected teaching performance in teaching physical education, particularly those Non-PE teachers teaching physical education subjects. It should be supplied with enhancement training workshops to help them be more successful in preparation in terms of teaching pedagogy and assessment in the following norm. Hence, a proposed unique training design for non-P.E. Teachers has been created to improve the teachers’ performance in terms of pedagogy and assessment in teaching P.E subjects in various learning delivery modes in the next normal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distance%20learning" title="distance learning">distance learning</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20delivery%20modes" title=" learning delivery modes"> learning delivery modes</a>, <a href="https://publications.waset.org/abstracts/search?q=P.E%20teachers" title=" P.E teachers"> P.E teachers</a>, <a href="https://publications.waset.org/abstracts/search?q=senior%20high%20school" title=" senior high school"> senior high school</a>, <a href="https://publications.waset.org/abstracts/search?q=teaching%20competence" title=" teaching competence"> teaching competence</a>, <a href="https://publications.waset.org/abstracts/search?q=teaching%20performance" title=" teaching performance"> teaching performance</a> </p> <a href="https://publications.waset.org/abstracts/161646/competence-on-learning-delivery-modes-and-performance-of-physical-education-teachers-in-senior-high-schools-in-davao" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161646.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">93</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18459</span> A Machine Learning Approach for Performance Prediction Based on User Behavioral Factors in E-Learning Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naduni%20Ranasinghe">Naduni Ranasinghe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=academic%20performance%20prediction" title="academic performance prediction">academic performance prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=e%20learning" title=" e learning"> e learning</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title=" learning analytics"> learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20model" title=" predictive model"> predictive model</a> </p> <a href="https://publications.waset.org/abstracts/146791/a-machine-learning-approach-for-performance-prediction-based-on-user-behavioral-factors-in-e-learning-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146791.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">157</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18458</span> Improving Performance and Progression of Novice Programmers: Factors Considerations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hala%20Shaari">Hala Shaari</a>, <a href="https://publications.waset.org/abstracts/search?q=Nuredin%20Ahmed"> Nuredin Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Teaching computer programming is recognized to be difficult and a real challenge. The biggest problem faced by novice programmers is their lack of understanding of basic programming concepts. A visualized learning tool was developed and used by volunteered first-year students for two semesters. The purposes of this paper are firstly, to emphasize factors which directly affect the performance of our students negatively. Secondly, to examine whether the proposed tool would improve their performance and learning progression. The results of adopting this tool were conducted using a pre-survey and post-survey questionnaire. As a result, students who used the learning tool showed better performance in their programming subject. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=factors" title="factors">factors</a>, <a href="https://publications.waset.org/abstracts/search?q=novice" title=" novice"> novice</a>, <a href="https://publications.waset.org/abstracts/search?q=programming" title=" programming"> programming</a>, <a href="https://publications.waset.org/abstracts/search?q=visualization" title=" visualization"> visualization</a> </p> <a href="https://publications.waset.org/abstracts/64977/improving-performance-and-progression-of-novice-programmers-factors-considerations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64977.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">363</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18457</span> An Ontology for Smart Learning Environments in Music Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Sofianos">Konstantinos Sofianos</a>, <a href="https://publications.waset.org/abstracts/search?q=Michail%20Stefanidakis"> Michail Stefanidakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, despite the great advances in technology, most educational frameworks lack a strong educational design basis. E-learning has become prevalent, but it faces various challenges such as student isolation and lack of quality in the learning process. An intelligent learning system provides a student with educational material according to their learning background and learning preferences. It records full information about the student, such as demographic information, learning styles, and academic performance. This information allows the system to be fully adapted to the student’s needs. In this paper, we propose a framework and an ontology for music education, consisting of the learner model and all elements of the learning process (learning objects, teaching methods, learning activities, assessment). This framework can be integrated into an intelligent learning system and used for music education in schools for the development of professional skills and beyond. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intelligent%20learning%20systems" title="intelligent learning systems">intelligent learning systems</a>, <a href="https://publications.waset.org/abstracts/search?q=e-learning" title=" e-learning"> e-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=music%20education" title=" music education"> music education</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20web" title=" semantic web"> semantic web</a> </p> <a href="https://publications.waset.org/abstracts/153256/an-ontology-for-smart-learning-environments-in-music-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153256.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18456</span> Using Happening Performance in Vocabulary Teaching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Gultekin">Mustafa Gultekin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is believed that drama can be used in language classes to create a positive atmosphere for students to use the target language in an interactive way. Thus, drama has been extensively used in many settings in language classes. Although happening has been generally used as a performance art of theatre, this new kind of performance has not been widely known in language teaching area. Therefore, it can be an innovative idea to use happening in language classes, and thus a positive environment can be created for students to use the language in an interactive way. Happening can be defined as an art performance that puts emphasis on interaction in an audience. Because of its interactive feature, happening can also be used in language classes to motivate students to use the language in an interactive environment. The present study aims to explain how a happening performance can be applied to a learning environment to teach vocabulary in English. In line with this purpose, a learning environment was designed for a vocabulary presentation lesson. At the end of the performance, students were asked to compare the traditional way of teaching and happening performance in terms of effectiveness. It was found that happening performance provided the students with a more creative and interactive environment to use the language. Therefore, happening can be used in language classrooms as an innovative tool for education. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=English" title="English">English</a>, <a href="https://publications.waset.org/abstracts/search?q=happening" title=" happening"> happening</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20learning" title=" language learning"> language learning</a>, <a href="https://publications.waset.org/abstracts/search?q=vocabulary%20teaching" title=" vocabulary teaching"> vocabulary teaching</a> </p> <a href="https://publications.waset.org/abstracts/69224/using-happening-performance-in-vocabulary-teaching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69224.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18455</span> Correlation Analysis to Quantify Learning Outcomes for Different Teaching Pedagogies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kanika%20Sood">Kanika Sood</a>, <a href="https://publications.waset.org/abstracts/search?q=Sijie%20Shang"> Sijie Shang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A fundamental goal of education includes preparing students to become a part of the global workforce by making beneficial contributions to society. In this paper, we analyze student performance for multiple courses that involve different teaching pedagogies: a cooperative learning technique and an inquiry-based learning strategy. Student performance includes student engagement, grades, and attendance records. We perform this study in the Computer Science department for online and in-person courses for 450 students. We will perform correlation analysis to study the relationship between student scores and other parameters such as gender, mode of learning. We use natural language processing and machine learning to analyze student feedback data and performance data. We assess the learning outcomes of two teaching pedagogies for undergraduate and graduate courses to showcase the impact of pedagogical adoption and learning outcome as determinants of academic achievement. Early findings suggest that when using the specified pedagogies, students become experts on their topics and illustrate enhanced engagement with peers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bag-of-words" title="bag-of-words">bag-of-words</a>, <a href="https://publications.waset.org/abstracts/search?q=cooperative%20learning" title=" cooperative learning"> cooperative learning</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a>, <a href="https://publications.waset.org/abstracts/search?q=inquiry-based%20learning" title=" inquiry-based learning"> inquiry-based learning</a>, <a href="https://publications.waset.org/abstracts/search?q=in-person%20learning" title=" in-person learning"> in-person learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20learning" title=" online learning"> online learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=teaching%20pedagogy" title=" teaching pedagogy"> teaching pedagogy</a> </p> <a href="https://publications.waset.org/abstracts/157641/correlation-analysis-to-quantify-learning-outcomes-for-different-teaching-pedagogies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157641.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">77</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18454</span> The Impact of E-Learning on the Performance of History Learners in Eswatini General Certificate of Secondary Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Osodo">Joseph Osodo</a>, <a href="https://publications.waset.org/abstracts/search?q=Motsa%20Thobekani%20Phila"> Motsa Thobekani Phila</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study investigated the impact of e-learning on the performance of history learners in Eswatini general certificate of secondary education in the Manzini region of Eswatini. The study was guided by the theory of connectivism. The study had three objectives which were to find out the significance of e-learning during the COVID-19 era in learning History subject; challenges faced by history teachers’ and learners’ in e-learning; and how the challenges were mitigated. The study used a qualitative research approach and descriptive research design. Purposive sampling was used to select eight History teachers and eight History learners from four secondary schools in the Manzini region. Data were collected using face to face interviews. The collected data were analyzed and presented in thematically. The findings showed that history teachers had good knowledge on what e-learning was, while students had little understanding of e-learning. Some of the forms of e-learning that were used during the pandemic in teaching history in secondary schools included TV, radio, computer, projectors, and social media especially WhatsApp. E-learning enabled the continuity of teaching and learning of history subject. The use of e-learning through the social media was more convenient to the teacher and the learners. It was concluded that in some secondary school in the Manzini region, history teacher and learners encountered challenges such as lack of finances to purchase e-learning gadgets and data bundles, lack of skills as well as access to the Internet. It was recommended that History teachers should create more time to offer additional learning support to students whose performance was affected by the COVID-19 pandemic effects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=e-learning" title="e-learning">e-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=history" title=" history"> history</a>, <a href="https://publications.waset.org/abstracts/search?q=connectivism" title=" connectivism"> connectivism</a> </p> <a href="https://publications.waset.org/abstracts/162952/the-impact-of-e-learning-on-the-performance-of-history-learners-in-eswatini-general-certificate-of-secondary-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162952.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">76</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18453</span> The Relationships among Learning Emotion, Major Satisfaction, Learning Flow, and Academic Achievement in Medical School Students</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20J.%20Yune">S. J. Yune</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Y.%20Lee"> S. Y. Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20J.%20Im"> S. J. Im</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20S.%20Kam"> B. S. Kam</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Y.%20Baek"> S. Y. Baek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study explored whether academic emotion, major satisfaction, and learning flow are associated with academic achievement in medical school. We know that emotion and affective factors are important factors in students' learning and performance. Emotion has taken the stage in much of contemporary educational psychology literature, no longer relegated to secondary status behind traditionally studied cognitive constructs. Medical school students (n=164) completed academic emotion, major satisfaction, and learning flow online survey. Academic performance was operationalized as students' average grade on two semester exams. For data analysis, correlation analysis, multiple regression analysis, hierarchical multiple regression analyses and ANOVA were conducted. The results largely confirmed the hypothesized relations among academic emotion, major satisfaction, learning flow and academic achievement. Positive academic emotion had a correlation with academic achievement (β=.191). Positive emotion had 8.5% explanatory power for academic achievement. Especially, sense of accomplishment had a significant impact on learning performance (β=.265). On the other hand, negative emotion, major satisfaction, and learning flow did not affect academic performance. Also, there were differences in sense of great (F=5.446, p=.001) and interest (F=2.78, p=.043) among positive emotion, boredom (F=3.55, p=.016), anger (F=4.346, p=.006), and petulance (F=3.779, p=.012) among negative emotion by grade. This study suggested that medical students' positive emotion was an important contributor to their academic achievement. At the same time, it is important to consider that some negative emotions can act to increase one’s motivation. Of particular importance is the notion that instructors can and should create learning environment that foster positive emotion for students. In doing so, instructors improve their chances of positively impacting students’ achievement emotions, as well as their subsequent motivation, learning, and performance. This result had an implication for medical educators striving to understand the personal emotional factors that influence learning and performance in medical training. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=academic%20achievement" title="academic achievement">academic achievement</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20emotion" title=" learning emotion"> learning emotion</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20flow" title=" learning flow"> learning flow</a>, <a href="https://publications.waset.org/abstracts/search?q=major%20satisfaction" title=" major satisfaction"> major satisfaction</a> </p> <a href="https://publications.waset.org/abstracts/58446/the-relationships-among-learning-emotion-major-satisfaction-learning-flow-and-academic-achievement-in-medical-school-students" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58446.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">272</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18452</span> Tongue Image Retrieval Based Using Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20FAROOQ">Ahmad FAROOQ</a>, <a href="https://publications.waset.org/abstracts/search?q=Xinfeng%20Zhang"> Xinfeng Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Sabah"> Fahad Sabah</a>, <a href="https://publications.waset.org/abstracts/search?q=Raheem%20Sarwar"> Raheem Sarwar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Traditional Chinese Medicine, tongue diagnosis is a vital inspection tool (TCM). In this study, we explore the potential of machine learning in tongue diagnosis. It begins with the cataloguing of the various classifications and characteristics of the human tongue. We infer 24 kinds of tongues from the material and coating of the tongue, and we identify 21 attributes of the tongue. The next step is to apply machine learning methods to the tongue dataset. We use the Weka machine learning platform to conduct the experiment for performance analysis. The 457 instances of the tongue dataset are used to test the performance of five different machine learning methods, including SVM, Random Forests, Decision Trees, and Naive Bayes. Based on accuracy and Area under the ROC Curve, the Support Vector Machine algorithm was shown to be the most effective for tongue diagnosis (AUC). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20imaging" title="medical imaging">medical imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20retrieval" title=" image retrieval"> image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=tongue" title=" tongue"> tongue</a> </p> <a href="https://publications.waset.org/abstracts/176849/tongue-image-retrieval-based-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176849.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">81</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18451</span> Predicting Relative Performance of Sector Exchange Traded Funds Using Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jun%20Wang">Jun Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ge%20Zhang"> Ge Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning has been used in many areas today. It thrives at reviewing large volumes of data and identifying patterns and trends that might not be apparent to a human. Given the huge potential benefit and the amount of data available in the financial market, it is not surprising to see machine learning applied to various financial products. While future prices of financial securities are extremely difficult to forecast, we study them from a different angle. Instead of trying to forecast future prices, we apply machine learning algorithms to predict the direction of future price movement, in particular, whether a sector Exchange Traded Fund (ETF) would outperform or underperform the market in the next week or in the next month. We apply several machine learning algorithms for this prediction. The algorithms are Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Neural Networks (NN). We show that these machine learning algorithms, most notably GNB and NN, have some predictive power in forecasting out-performance and under-performance out of sample. We also try to explore whether it is possible to utilize the predictions from these algorithms to outperform the buy-and-hold strategy of the S&P 500 index. The trading strategy to explore out-performance predictions does not perform very well, but the trading strategy to explore under-performance predictions can earn higher returns than simply holding the S&P 500 index out of sample. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=ETF%20prediction" title=" ETF prediction"> ETF prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20trading" title=" dynamic trading"> dynamic trading</a>, <a href="https://publications.waset.org/abstracts/search?q=asset%20allocation" title=" asset allocation"> asset allocation</a> </p> <a href="https://publications.waset.org/abstracts/160995/predicting-relative-performance-of-sector-exchange-traded-funds-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160995.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">98</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18450</span> Supervised/Unsupervised Mahalanobis Algorithm for Improving Performance for Cyberattack Detection over Communications Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Radhika%20Ranjan%20Roy">Radhika Ranjan Roy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classiﬁcation tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahalanobis%20distance" title="Mahalanobis distance">Mahalanobis distance</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=NS-KDD" title=" NS-KDD"> NS-KDD</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20intrinsic%20dimensionality" title=" local intrinsic dimensionality"> local intrinsic dimensionality</a>, <a href="https://publications.waset.org/abstracts/search?q=chi-square" title=" chi-square"> chi-square</a>, <a href="https://publications.waset.org/abstracts/search?q=positive%20semi-definite" title=" positive semi-definite"> positive semi-definite</a>, <a href="https://publications.waset.org/abstracts/search?q=area%20under%20the%20curve" title=" area under the curve"> area under the curve</a> </p> <a href="https://publications.waset.org/abstracts/161865/supervisedunsupervised-mahalanobis-algorithm-for-improving-performance-for-cyberattack-detection-over-communications-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161865.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18449</span> Predictive Analytics of Student Performance Determinants</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahtab%20Davari">Mahtab Davari</a>, <a href="https://publications.waset.org/abstracts/search?q=Charles%20Edward%20Okon"> Charles Edward Okon</a>, <a href="https://publications.waset.org/abstracts/search?q=Somayeh%20Aghanavesi"> Somayeh Aghanavesi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=student%20performance" title="student performance">student performance</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20machine%20learning" title=" supervised machine learning"> supervised machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-validation" title=" cross-validation"> cross-validation</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/155072/predictive-analytics-of-student-performance-determinants" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155072.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">126</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=learning%20performance&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=learning%20performance&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=learning%20performance&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=learning%20performance&page=5">5</a></li> <li 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