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performance prediction system</title> <meta name="description" content="Search results for: academic performance prediction system"> <meta name="keywords" content="academic performance prediction system"> <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 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method="get" 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="academic performance prediction system"> <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> 29996</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: academic performance prediction system</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29996</span> Application of Artificial Neural Network to Prediction of Feature Academic Performance of Students </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20K.%20Alhassan">J. K. Alhassan</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20S.%20Actsu"> C. S. Actsu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study is on the prediction of feature performance of undergraduate students with Artificial Neural Networks (ANN). With the growing decline in the quality academic performance of undergraduate students, it has become essential to predict the students’ feature academic performance early in their courses of first and second years and to take the necessary precautions using such prediction-based information. The feed forward multilayer neural network model was used to train and develop a network and the test carried out with some of the input variables. A result of 80% accuracy was obtained from the test which was carried out, with an average error of 0.009781. <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=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=students" title=" students"> students</a> </p> <a href="https://publications.waset.org/abstracts/36018/application-of-artificial-neural-network-to-prediction-of-feature-academic-performance-of-students" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36018.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">468</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">29995</span> The Role of Psychological Factors in Prediction Academic Performance of Students</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hadi%20Molaei">Hadi Molaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasavoli%20Davoud"> Yasavoli Davoud</a>, <a href="https://publications.waset.org/abstracts/search?q=Keshavarz"> Keshavarz</a>, <a href="https://publications.waset.org/abstracts/search?q=Mozhde%20Poordana"> Mozhde Poordana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study aimed was to prediction the academic performance based on academic motivation, self-efficacy and Resiliency in the students. The present study was descriptive and correlational. Population of the study consisted of all students in Arak schools in year 1393-94. For this purpose, the number of 304 schools students in Arak was selected using multi-stage cluster sampling. They all questionnaires, self-efficacy, Resiliency and academic motivation Questionnaire completed. Data were analyzed using Pearson correlation and multiple regressions. Pearson correlation showed academic motivation, self-efficacy, and Resiliency with academic performance had a positive and significant relationship. In addition, multiple regression analysis showed that the academic motivation, self-efficacy and Resiliency were predicted academic performance. Based on the findings could be conclude that in order to increase the academic performance and further progress of students must provide the ground to strengthen academic motivation, self-efficacy and Resiliency act on them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=academic%20motivation" title="academic motivation">academic motivation</a>, <a href="https://publications.waset.org/abstracts/search?q=self-efficacy" title=" self-efficacy"> self-efficacy</a>, <a href="https://publications.waset.org/abstracts/search?q=resiliency" title=" resiliency"> resiliency</a>, <a href="https://publications.waset.org/abstracts/search?q=academic%20performance" title=" academic performance"> academic performance</a> </p> <a href="https://publications.waset.org/abstracts/24763/the-role-of-psychological-factors-in-prediction-academic-performance-of-students" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24763.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">498</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">29994</span> EDM for Prediction of Academic Trends and Patterns</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Trupti%20Diwan">Trupti Diwan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting student failure at school has changed into a difficult challenge due to both the large number of factors that can affect the reduced performance of students and the imbalanced nature of these kinds of data sets. This paper surveys the two elements needed to make prediction on Students’ Academic Performances which are parameters and methods. This paper also proposes a framework for predicting the performance of engineering students. Genetic programming can be used to predict student failure/success. Ranking algorithm is used to rank students according to their credit points. The framework can be used as a basis for the system implementation & prediction of students’ Academic Performance in Higher Learning Institute. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20data%20mining" title=" educational data mining"> educational data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=student%20failure" title=" student failure"> student failure</a>, <a href="https://publications.waset.org/abstracts/search?q=grammar-based%20genetic%20programming" title=" grammar-based genetic programming"> grammar-based genetic programming</a> </p> <a href="https://publications.waset.org/abstracts/20702/edm-for-prediction-of-academic-trends-and-patterns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20702.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">422</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">29993</span> Integration of Educational Data Mining Models to a Web-Based Support System for Predicting High School Student Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sokkhey%20Phauk">Sokkhey Phauk</a>, <a href="https://publications.waset.org/abstracts/search?q=Takeo%20Okazaki"> Takeo Okazaki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=academic%20performance%20prediction%20system" title="academic performance prediction system">academic performance prediction system</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20data%20mining" title=" educational data mining"> educational data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=dominant%20factors" title=" dominant factors"> dominant factors</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection%20method" title=" feature selection method"> feature selection method</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20model" title=" prediction model"> prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=student%20performance" title=" student performance"> student performance</a> </p> <a href="https://publications.waset.org/abstracts/127780/integration-of-educational-data-mining-models-to-a-web-based-support-system-for-predicting-high-school-student-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127780.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">106</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">29992</span> Analysis on Prediction Models of TBM Performance and Selection of Optimal Input Parameters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hang%20Lo%20Lee">Hang Lo Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Ki%20Il%20Song"> Ki Il Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Hee%20Hwan%20Ryu"> Hee Hwan Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=TBM%20performance%20prediction%20model" title="TBM performance prediction model">TBM performance prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20system" title=" classification system"> classification system</a>, <a href="https://publications.waset.org/abstracts/search?q=simple%20regression%20analysis" title=" simple regression analysis"> simple regression analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20analysis" title=" residual analysis"> residual analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20input%20parameters" title=" optimal input parameters"> optimal input parameters</a> </p> <a href="https://publications.waset.org/abstracts/52738/analysis-on-prediction-models-of-tbm-performance-and-selection-of-optimal-input-parameters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52738.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">309</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">29991</span> Monthly River Flow Prediction Using a Nonlinear Prediction Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20H.%20Adenan">N. H. Adenan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20M.%20Noorani"> M. S. M. Noorani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> River flow prediction is an essential to ensure proper management of water resources can be optimally distribute water to consumers. This study presents an analysis and prediction by using nonlinear prediction method involving monthly river flow data in Tanjung Tualang from 1976 to 2006. Nonlinear prediction method involves the reconstruction of phase space and local linear approximation approach. The phase space reconstruction involves the reconstruction of one-dimensional (the observed 287 months of data) in a multidimensional phase space to reveal the dynamics of the system. Revenue of phase space reconstruction is used to predict the next 72 months. A comparison of prediction performance based on correlation coefficient (CC) and root mean square error (RMSE) have been employed to compare prediction performance for nonlinear prediction method, ARIMA and SVM. Prediction performance comparisons show the prediction results using nonlinear prediction method is better than ARIMA and SVM. Therefore, the result of this study could be used to developed an efficient water management system to optimize the allocation water resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=river%20flow" title="river flow">river flow</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20prediction%20method" title=" nonlinear prediction method"> nonlinear prediction method</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20space" title=" phase space"> phase space</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20linear%20approximation" title=" local linear approximation"> local linear approximation</a> </p> <a href="https://publications.waset.org/abstracts/2867/monthly-river-flow-prediction-using-a-nonlinear-prediction-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2867.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">412</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">29990</span> How Adolescents Fare Mentally: Single- vs. Multi-route College Admissions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karen%20Yang">Karen Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In China, college admissions follow a single-route system where students compete primarily based on academic performance. In contrast, the U.S. uses a multi-route system, offering pathways that consider non-academic achievements such as sports, art, or writing, allowing students to select the route that best suits them. We developed a tournament model to explore which system is more effective at reducing student anxiety. Our analysis indicates that the performance depends on factors such as the overall admission rate, the distribution of quotas among the different routes, and societal norms regarding comparison benchmarks of getting anxious. Since allocating quotas to non-academic routes functions similarly to affirmative action, students with lower academic performance benefit from the multi-route system, while those with higher academic performance may be disadvantaged. Anxiety levels in a multi-route system can surpass those in a single-route system when the proportion of high-ability students is greater than that of low-ability students and the admission opportunities in the academic route fall below the anxiety-inducing benchmark. In societies where being at the average level triggers anxiety, the multi-route system can significantly elevate anxiety rates compared to the single-route system. Even when students can exert effort, the results remain consistent, with effort levels in the multi-route system potentially being lower than in the single-route system. Survey data largely support the model's assumptions and predictions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=college%20admissions" title="college admissions">college admissions</a>, <a href="https://publications.waset.org/abstracts/search?q=tournament" title=" tournament"> tournament</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20route%20system" title=" single route system"> single route system</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-route%20system" title=" multi-route system"> multi-route system</a>, <a href="https://publications.waset.org/abstracts/search?q=affirmative%20action" title=" affirmative action"> affirmative action</a> </p> <a href="https://publications.waset.org/abstracts/195864/how-adolescents-fare-mentally-single-vs-multi-route-college-admissions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/195864.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">7</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">29989</span> Impact of Contemporary Performance Measurement System and Organization Justice on Academic Staff Work Performance </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amizawati%20Mohd%20Amir">Amizawati Mohd Amir</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruhanita%20Maelah"> Ruhanita Maelah</a>, <a href="https://publications.waset.org/abstracts/search?q=Zaidi%20Mohd%20Noor"> Zaidi Mohd Noor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As part of the Malaysia Higher Institutions' Strategic Plan in promoting high-quality research and education, the Ministry of Higher Education has introduced various instrument to assess the universities performance. The aims are that university will produce more commercially-oriented research and continue to contribute in producing professional workforce for domestic and foreign needs. Yet the spirit of the success lies in the commitment of university particularly the academic staff to translate the vision into reality. For that reason, the element of fairness and justice in assessing individual academic staff performance is crucial to promote directly linked between university and individual work goals. Focusing on public research universities (RUs) in Malaysia, this study observes at the issue through the practice of university contemporary performance measurement system. Accordingly management control theory has conceptualized that contemporary performance measurement consisting of three dimension namely strategic, comprehensive and dynamic building upon equity theory, the relationships between contemporary performance measurement system and organizational justice and in turn the effect on academic staff work performance are tested based on online survey data administered on 365 academic staff from public RUs, which were analyzed using statistics analysis SPSS and Equation Structure Modeling. The findings validated the presence of strategic, comprehensive and dynamic in the contemporary performance measurement system. The empirical evidence also indicated that contemporary performance measure and procedural justice are significantly associated with work performance but not for distributive justice. Furthermore, procedural justice does mediate the relationship between contemporary performance measurement and academic staff work performance. Evidently, this study provides evidence on the importance of perceptions of justice towards influencing academic staff work performance. This finding may be a fruitful input in the setting up academic staff performance assessment policy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=comprehensive" title="comprehensive">comprehensive</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic" title=" dynamic"> dynamic</a>, <a href="https://publications.waset.org/abstracts/search?q=distributive%20justice" title=" distributive justice"> distributive justice</a>, <a href="https://publications.waset.org/abstracts/search?q=contemporary%20performance%20measurement%20system" title=" contemporary performance measurement system"> contemporary performance measurement system</a>, <a href="https://publications.waset.org/abstracts/search?q=strategic" title=" strategic"> strategic</a>, <a href="https://publications.waset.org/abstracts/search?q=procedure%20justice" title=" procedure justice"> procedure justice</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/78800/impact-of-contemporary-performance-measurement-system-and-organization-justice-on-academic-staff-work-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78800.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">409</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">29988</span> Using High Performance Computing for Online Flood Monitoring and Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stepan%20Kuchar">Stepan Kuchar</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Golasowski"> Martin Golasowski</a>, <a href="https://publications.waset.org/abstracts/search?q=Radim%20Vavrik"> Radim Vavrik</a>, <a href="https://publications.waset.org/abstracts/search?q=Michal%20Podhoranyi"> Michal Podhoranyi</a>, <a href="https://publications.waset.org/abstracts/search?q=Boris%20Sir"> Boris Sir</a>, <a href="https://publications.waset.org/abstracts/search?q=Jan%20Martinovic"> Jan Martinovic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flood%20prediction%20process" title="flood prediction process">flood prediction process</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20performance%20computing" title=" high performance computing"> high performance computing</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20flood%20prediction%20system" title=" online flood prediction system"> online flood prediction system</a>, <a href="https://publications.waset.org/abstracts/search?q=parallelization" title=" parallelization"> parallelization</a> </p> <a href="https://publications.waset.org/abstracts/21155/using-high-performance-computing-for-online-flood-monitoring-and-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21155.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">493</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">29987</span> Evaluation of Machine Learning Algorithms and Ensemble Methods for Prediction of Students’ Graduation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soha%20A.%20Bahanshal">Soha A. Bahanshal</a>, <a href="https://publications.waset.org/abstracts/search?q=Vaibhav%20Verdhan"> Vaibhav Verdhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayong%20Kim"> Bayong Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction" title="prediction">prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20trees" title=" decision trees"> decision trees</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=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20model" title=" ensemble model"> ensemble model</a>, <a href="https://publications.waset.org/abstracts/search?q=student%20graduation" title=" student graduation"> student graduation</a>, <a href="https://publications.waset.org/abstracts/search?q=GOT%20graduate%20on%20time" title=" GOT graduate on time"> GOT graduate on time</a> </p> <a href="https://publications.waset.org/abstracts/167620/evaluation-of-machine-learning-algorithms-and-ensemble-methods-for-prediction-of-students-graduation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167620.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">29986</span> Academic Performance and Therapeutic Breathing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abha%20Gupta">Abha Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Seema%20Maira"> Seema Maira</a>, <a href="https://publications.waset.org/abstracts/search?q=Smita%20Sinha"> Smita Sinha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explores using breathing techniques to boost the academic performance of students and describes how teachers can foster the technique in their classrooms. The innovative study examines the differential impact of therapeutic breathing exercises, called pranayama, on students’ academic performance. The paper introduces approaches to therapeutic breathing exercises as an alternative to improve school performance, as well as the self-regulatory behavior, which is known to correlate with academic performance. The study was conducted in a school-wide pranayama program with positive outcomes. The intervention consisted of two breathing exercises, (1) deep breathing, and (2) alternate nostril breathing. It is a quantitative study spanning over a year with about 100 third graders was conducted using daily breathing exercises to investigate the impact of pranayama on academic performance. Significant cumulative gain-scores were found for students who practiced the approach. <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=pranayama" title=" pranayama"> pranayama</a>, <a href="https://publications.waset.org/abstracts/search?q=therapeutic%20breathing" title=" therapeutic breathing"> therapeutic breathing</a>, <a href="https://publications.waset.org/abstracts/search?q=yoga" title=" yoga"> yoga</a> </p> <a href="https://publications.waset.org/abstracts/19448/academic-performance-and-therapeutic-breathing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19448.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">491</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">29985</span> The Role of Time Management Skills in Academic Performance of the University Lecturers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thuduwage%20Lasanthika%20Sajeevanie">Thuduwage Lasanthika Sajeevanie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Success is very important, and there are many factors affecting the success of any situation or a person. In Sri Lankan Context, it is hardly possible to find an empirical study relating to time management and academic success. Globally many organizations, individuals practice time management to be effective. Hence it is very important to examine the nature of time management practice. Thus this study will fill the existing gap relating to achieving academic success through proper time management practices. The research problem of this study is what is the relationship exist among time management skills and academic success of university lecturers in state universities. The objective of this paper is to identify the impact of time management skills for academic success of university lecturers. This is a conceptual study, and it was done through a literature survey by following purposive sampling technique for the selection of literature. Most of the studies have found that time management is highly related to academic performance. However, most of them have done on the academic performance of the students, and there were very few studies relating to academic performance of the university lecturers. Hence it can be further suggested to conduct a study relating to identifying the relationship between academic performance and time management skills of university lecturers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=academic%20success" title="academic success">academic success</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20management%20skills" title=" time management skills"> time management skills</a>, <a href="https://publications.waset.org/abstracts/search?q=university%20lecturers" title=" university lecturers"> university lecturers</a> </p> <a href="https://publications.waset.org/abstracts/65744/the-role-of-time-management-skills-in-academic-performance-of-the-university-lecturers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65744.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">357</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">29984</span> Performance Analysis of Bluetooth Low Energy Mesh Routing Algorithm in Case of Disaster Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asmir%20Gogic">Asmir Gogic</a>, <a href="https://publications.waset.org/abstracts/search?q=Aljo%20Mujcic"> Aljo Mujcic</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandra%20Ibric"> Sandra Ibric</a>, <a href="https://publications.waset.org/abstracts/search?q=Nermin%20Suljanovic"> Nermin Suljanovic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bluetooth%20low%20energy" title="bluetooth low energy">bluetooth low energy</a>, <a href="https://publications.waset.org/abstracts/search?q=disaster%20prediction" title=" disaster prediction"> disaster prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=mesh%20routing%20protocols" title=" mesh routing protocols"> mesh routing protocols</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20networks" title=" wireless sensor networks"> wireless sensor networks</a> </p> <a href="https://publications.waset.org/abstracts/43894/performance-analysis-of-bluetooth-low-energy-mesh-routing-algorithm-in-case-of-disaster-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43894.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">386</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">29983</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">29982</span> Human Relationships in the Virtual Classrooms as Predictors of Students Academic Resilience and Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eddiebal%20P.%20Layco">Eddiebal P. Layco</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is to describe students' virtual classroom relationships in terms of their relationship to their peers and teachers; academic resilience; and performance. Further, the researcher wants to examine if these virtual classroom relations predict students' resilience and performance in their academics. The data were collected from 720 junior and senior high school or grade 7 to 12 students in selected state universities and colleges (SUCs) in Region III offering online or virtual classes during S.Y. 2020-2021. Results revealed that virtual classroom relationships such as teacher-student and peer relationships predict academic resilience and performance. This implies that students' academic relations with their teachers and peers have something to do with their ability to bounce back and beat the odds amidst challenges they faced in the online or virtual learning environment. These virtual relationships significantly influence also their academic performance. Adequate teacher support and positive peer relations may lead to enhanced academic resilience, which may also promote a meaningful and fulfilled life academically. Result suggests that teachers should develop their students' academic resiliency and maintain good relationships in the classroom since these results in academic success. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=virtual%20classroom%20relationships" title="virtual classroom relationships">virtual classroom relationships</a>, <a href="https://publications.waset.org/abstracts/search?q=teacher-pupil%20relationship" title=" teacher-pupil relationship"> teacher-pupil relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=peer-relationship" title=" peer-relationship"> peer-relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=academic%20resilience" title=" academic resilience"> academic resilience</a>, <a href="https://publications.waset.org/abstracts/search?q=academic%20performance" title=" academic performance"> academic performance</a> </p> <a href="https://publications.waset.org/abstracts/138837/human-relationships-in-the-virtual-classrooms-as-predictors-of-students-academic-resilience-and-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138837.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">153</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">29981</span> Motivational Factors on Non-Academic Staff of Higher Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Atya%20Nur%20Aisha">Atya Nur Aisha</a>, <a href="https://publications.waset.org/abstracts/search?q=Pamoedji%20Hardjomidjojo"> Pamoedji Hardjomidjojo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassierli"> Yassierli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Motivation is an important aspect which affects employee behavior to achieve performance. Working motivation tend to be unstable, it easily changing. This condition could be affected by individual factors, namely working ability, and organizational factors, such as working condition and incentives system. The purpose of this study was to examine the impact of individual and organizational factors on non-academic staff motivation. A questionnaire was designed and distributed to 150 non-academic staff of a university in Indonesia. Regression analysis was used to identify the relationship. Results revealed that individual working ability and incentives system had a positive impact on non-academic staff motivation (sig 0.001). This study provides information about practical implication for university authorities and theoretical implications for researchers who interested in exploring motivational and employee performance in a higher education context. It was proposed to increase productivity and work motivation of non-academic staff, university authorities should maintain equality and feasibility of incentives system and design a human resource development to improve employee ability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=motivation" title="motivation">motivation</a>, <a href="https://publications.waset.org/abstracts/search?q=incentives" title=" incentives"> incentives</a>, <a href="https://publications.waset.org/abstracts/search?q=working%20ability" title=" working ability"> working ability</a>, <a href="https://publications.waset.org/abstracts/search?q=non-academic%20staff" title=" non-academic staff"> non-academic staff</a> </p> <a href="https://publications.waset.org/abstracts/39687/motivational-factors-on-non-academic-staff-of-higher-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39687.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">410</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">29980</span> Logistic Regression Based Model for Predicting Students’ Academic Performance in Higher Institutions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Osaze%20Oshoiribhor">Emmanuel Osaze Oshoiribhor</a>, <a href="https://publications.waset.org/abstracts/search?q=Adetokunbo%20MacGregor%20John-Otumu"> Adetokunbo MacGregor John-Otumu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes. <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=ML" title=" ML"> ML</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/151047/logistic-regression-based-model-for-predicting-students-academic-performance-in-higher-institutions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151047.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">97</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">29979</span> The Influence of Gender and Harmful Alcohol Consumption on Academic Performance in Spanish University Students</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20Rodr%C3%ADguez">M. S. Rodríguez</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Cadaveira"> F. Cadaveira</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20F.%20P%C3%A1ramo"> M. F. Páramo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> First year university students comprise one of the groups most likely to indulge in hazardous alcohol consumption. The transition from secondary school to university presents a range of academic, social and developmental challenges requiring new responses that will meet the demands of this highly competitive environment. The main purpose of this research was to analyze the influence of gender and hazardous alcohol consumption on academic performance of 300 university students in Spain in a three-year follow-up study. Alcohol consumption was measured using the Alcohol Use Identification Test (AUDIT), and the average university grades were provided by the Academic Management Services of the University. Analysis of variance showed that the level of alcohol consumption significantly affected academic performance. Students undertaking hazardous alcohol consumption obtained the lowest grades during the first three years at university. These effects were particularly marked in the sample of women with a hazardous pattern of alcohol consumption, although the interaction between gender and this type of consumption was not significant. The study highlights the impact of hazardous alcohol consumption on the academic trajectory of university students. The findings confirm that alcohol consumption predicts poor academic performance in first year students and that the low level of performance is maintained throughout the university career. <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=alcohol%20consumption" title=" alcohol consumption"> alcohol consumption</a>, <a href="https://publications.waset.org/abstracts/search?q=gender" title=" gender"> gender</a>, <a href="https://publications.waset.org/abstracts/search?q=university%20students" title=" university students"> university students</a> </p> <a href="https://publications.waset.org/abstracts/49985/the-influence-of-gender-and-harmful-alcohol-consumption-on-academic-performance-in-spanish-university-students" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49985.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">311</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">29978</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">29977</span> Chinese Doctoral Students in Canada: The Influence of Financial Status and Cultural Cognition on Academic Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xuefan%20Li">Xuefan Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Parts of Chinese doctoral students in Canada are facing significant academic pressure. The factors contributing to such pressure are diverse, including financial conditions and cultural differences. Students from various academic disciplines have been interviewed to investigate the factors that Chinese students consider when selecting Canada as a destination for doctoral studies, as well as to identify the challenges they face during their academic pursuits and the associated factors influencing their performance. The findings indicate that their motivations to pursue doctoral study in Canada are concluded as both push and pull factors. Financial conditions and cultural differences are critical factors affecting academic performance, with disciplinary variations in the degree of influence observed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinese%20doctoral%20students" title="Chinese doctoral students">Chinese doctoral students</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20status" title=" financial status"> financial status</a>, <a href="https://publications.waset.org/abstracts/search?q=cultural%20cognition" title=" cultural cognition"> cultural cognition</a>, <a href="https://publications.waset.org/abstracts/search?q=academic%20performance" title=" academic performance"> academic performance</a> </p> <a href="https://publications.waset.org/abstracts/164053/chinese-doctoral-students-in-canada-the-influence-of-financial-status-and-cultural-cognition-on-academic-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164053.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">72</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">29976</span> Enhanced Extra Trees Classifier for Epileptic Seizure Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maurice%20Ntahobari">Maurice Ntahobari</a>, <a href="https://publications.waset.org/abstracts/search?q=Levin%20Kuhlmann"> Levin Kuhlmann</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20Boley"> Mario Boley</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhinoos%20Razavi%20Hesabi"> Zhinoos Razavi Hesabi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection. <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=seizure%20prediction" title=" seizure prediction"> seizure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=extra%20tree%20classifier" title=" extra tree classifier"> extra tree classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=SHAP" title=" SHAP"> SHAP</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a> </p> <a href="https://publications.waset.org/abstracts/155126/enhanced-extra-trees-classifier-for-epileptic-seizure-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155126.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">113</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">29975</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">128</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">29974</span> SEMCPRA-Sar-Esembled Model for Climate Prediction in Remote Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamalpreet%20Kaur">Kamalpreet Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Renu%20Dhir"> Renu Dhir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate prediction is an essential component of climate research, which helps evaluate possible effects on economies, communities, and ecosystems. Climate prediction involves short-term weather prediction, seasonal prediction, and long-term climate change prediction. Climate prediction can use the information gathered from satellites, ground-based stations, and ocean buoys, among other sources. The paper's four architectures, such as ResNet50, VGG19, Inception-v3, and Xception, have been combined using an ensemble approach for overall performance and robustness. An ensemble of different models makes a prediction, and the majority vote determines the final prediction. The various architectures such as ResNet50, VGG19, Inception-v3, and Xception efficiently classify the dataset RSI-CB256, which contains satellite images into cloudy and non-cloudy. The generated ensembled S-E model (Sar-ensembled model) provides an accuracy of 99.25%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate" title="climate">climate</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images"> satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/178864/semcpra-sar-esembled-model-for-climate-prediction-in-remote-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178864.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">75</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">29973</span> Dynamic vs. Static Bankruptcy Prediction Models: A Dynamic Performance Evaluation Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Mahdi%20Mousavi">Mohammad Mahdi Mousavi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bankruptcy prediction models have been implemented for continuous evaluation and monitoring of firms. With the huge number of bankruptcy models, an extensive number of studies have focused on answering the question that which of these models are superior in performance. In practice, one of the drawbacks of existing comparative studies is that the relative assessment of alternative bankruptcy models remains an exercise that is mono-criterion in nature. Further, a very restricted number of criteria and measure have been applied to compare the performance of competing bankruptcy prediction models. In this research, we overcome these methodological gaps through implementing an extensive range of criteria and measures for comparison between dynamic and static bankruptcy models, and through proposing a multi-criteria framework to compare the relative performance of bankruptcy models in forecasting firm distress for UK firms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bankruptcy%20prediction" title="bankruptcy prediction">bankruptcy prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20envelopment%20analysis" title=" data envelopment analysis"> data envelopment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20criteria" title=" performance criteria"> performance criteria</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20measures" title=" performance measures"> performance measures</a> </p> <a href="https://publications.waset.org/abstracts/52050/dynamic-vs-static-bankruptcy-prediction-models-a-dynamic-performance-evaluation-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52050.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">249</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">29972</span> Performance Evaluation of Arrival Time Prediction Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bin%20Li">Bin Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Mei%20Liu"> Mei Liu </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Arrival time information is a crucial component of advanced public transport system (APTS). The advertisement of arrival time at stops can help reduce the waiting time and anxiety of passengers, and improve the quality of service. In this research, an experiment was conducted to compare the performance on prediction accuracy and precision between the link-based and the path-based historical travel time based model with the automatic vehicle location (AVL) data collected from an actual bus route. The research results show that the path-based model is superior to the link-based model, and achieves the best improvement on peak hours. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bus%20transit" title="bus transit">bus transit</a>, <a href="https://publications.waset.org/abstracts/search?q=arrival%20time%20prediction" title=" arrival time prediction"> arrival time prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=link-based" title=" link-based"> link-based</a>, <a href="https://publications.waset.org/abstracts/search?q=path-based" title=" path-based"> path-based</a> </p> <a href="https://publications.waset.org/abstracts/2389/performance-evaluation-of-arrival-time-prediction-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2389.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">359</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">29971</span> A Machine Learning Model for Predicting Students’ Academic Performance in Higher Institutions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Osaze%20Oshoiribhor">Emmanuel Osaze Oshoiribhor</a>, <a href="https://publications.waset.org/abstracts/search?q=Adetokunbo%20MacGregor%20John-Otumu"> Adetokunbo MacGregor John-Otumu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better. <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=ML" title=" ML"> ML</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/151317/a-machine-learning-model-for-predicting-students-academic-performance-in-higher-institutions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151317.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">109</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">29970</span> Machine Learning Approach for Predicting Students’ Academic Performance and Study Strategies Based on Their Motivation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fidelia%20A.%20Orji">Fidelia A. Orji</a>, <a href="https://publications.waset.org/abstracts/search?q=Julita%20Vassileva"> Julita Vassileva</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification%20models" title="classification models">classification models</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20strategy" title=" learning strategy"> learning strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling" title=" predictive modeling"> predictive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20models" title=" regression models"> regression models</a>, <a href="https://publications.waset.org/abstracts/search?q=student%20academic%20performance" title=" student academic performance"> student academic performance</a>, <a href="https://publications.waset.org/abstracts/search?q=student%20motivation" title=" student motivation"> student motivation</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20machine%20learning" title=" supervised machine learning"> supervised machine learning</a> </p> <a href="https://publications.waset.org/abstracts/150715/machine-learning-approach-for-predicting-students-academic-performance-and-study-strategies-based-on-their-motivation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150715.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">129</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">29969</span> In Search of High Growth: Mapping out Academic Spin-Off´s Performance in Catalonia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Guspi">F. Guspi</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Garc%C3%ADa"> E. García</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This exploratory study gives an overview of the evolution of the main financial and performance indicators of the Academic Spin-Off’s and High Growth Academic Spin-Off’s in year 3 and year 6 after its creation in the region of Catalonia in Spain. The study compares and evaluates results of these different measures of performance and the degree of success of these companies for each University. We found that the average Catalonian Academic Spin-Off is small and have not achieved the sustainability stage at year 6. On the contrary, a small group of High Growth Academic Spin-Off’s exhibit robust performance with high profits in year 6. Our results support the need to increase selectivity and support for these companies especially near year 3, because are the ones that will bring wealth and employment. University role as an investor has rigid norms and habits that impede an efficient economic return from their ASO investment. Universities with high performance on sales and employment in year 3 not always could sustain this growth in year 6 because their ASO’s are not profitable. On the contrary, profitable ASO exhibit superior performance in all measurement indicators in year 6. We advocate the need of a balanced growth (with profits) as a way to obtain subsequent continuous growth. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Academic%20Spin-Off%20%28ASO%29" title="Academic Spin-Off (ASO)">Academic Spin-Off (ASO)</a>, <a href="https://publications.waset.org/abstracts/search?q=university%20entrepreneurship" title=" university entrepreneurship"> university entrepreneurship</a>, <a href="https://publications.waset.org/abstracts/search?q=entrepreneurial%20university" title=" entrepreneurial university"> entrepreneurial university</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20growth" title=" high growth"> high growth</a>, <a href="https://publications.waset.org/abstracts/search?q=New%20Technology%20Based%20Companies%20%28NTBC%29" title=" New Technology Based Companies (NTBC)"> New Technology Based Companies (NTBC)</a>, <a href="https://publications.waset.org/abstracts/search?q=University%20Spin-Off" title=" University Spin-Off "> University Spin-Off </a> </p> <a href="https://publications.waset.org/abstracts/18172/in-search-of-high-growth-mapping-out-academic-spin-offs-performance-in-catalonia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18172.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">458</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">29968</span> Effects of Family Socioeconomic Status and Parental Involvement on Elementary School Students’ Academic Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qingli%20Lei">Qingli Lei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigates the impact of family socioeconomic status and parental involvement on the academic performance of elementary school students, specifically focusing on migrant students in China. The findings reveal that gender has a stronger influence on academic performance compared to local status and parental tutoring time. Female students tend to achieve higher scores than males. Parental education level does not significantly predict academic performance, while parent tutoring time does have a significant impact. Furthermore, there is a significant interaction between local status and parental education level, indicating that migrant students with lower-educated parents perform better than their local counterparts, while local children excel when their parents' education levels are higher. These results emphasize the importance of parental involvement, particularly for immigrant students, and highlight the need for interventions that enhance parental engagement in education to improve academic outcomes. <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=family%20socioeconomic%20status" title=" family socioeconomic status"> family socioeconomic status</a>, <a href="https://publications.waset.org/abstracts/search?q=migrant%20students" title=" migrant students"> migrant students</a>, <a href="https://publications.waset.org/abstracts/search?q=parental%20involvement" title=" parental involvement"> parental involvement</a> </p> <a href="https://publications.waset.org/abstracts/166948/effects-of-family-socioeconomic-status-and-parental-involvement-on-elementary-school-students-academic-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166948.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">101</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">29967</span> The Impact of Technological Advancement on Academic Performance of Mathematics Students in Tertiary Institutions in Ekiti State, Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Odunayo%20E.%20Popoola">Odunayo E. Popoola</a>, <a href="https://publications.waset.org/abstracts/search?q=Charles%20A.%20Aladesaye"> Charles A. Aladesaye</a>, <a href="https://publications.waset.org/abstracts/search?q=Sunday%20O.%20Gbenro"> Sunday O. Gbenro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study investigated the impact of technological advancement on the academic performance of Mathematics students in tertiary institutions in Ekiti State, Nigeria. The quasi-experimental research design was adopted for the study. The population for the study consisted of all the 100 level undergraduates and all Mathematics lecturers in the Department of Mathematics in all the five tertiary institutions in the State. The sample of this study was made of one hundred (100) students and fifty (50) lecturers randomly selected using stratified sampling technique. Hypotheses were postulated to find out whether (i) advancement in technology influences the academic performance of students in Mathematics (ii) teaching method and gender disparity influences the academic performance of students in Mathematics. The study revealed that teaching method, gender, and technology influence academic performance of students in Mathematics. Based on the findings, it is recommended that curriculum and assessment in school Mathematics should explicitly require that all undergraduate become proficient in using digital technologies for mathematical purposes so as to enhance the better performance of students in Mathematics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mathematics" title="mathematics">mathematics</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=tertiary%20institutions" title=" tertiary institutions"> tertiary institutions</a>, <a href="https://publications.waset.org/abstracts/search?q=technology" title=" technology "> technology </a> </p> <a href="https://publications.waset.org/abstracts/92793/the-impact-of-technological-advancement-on-academic-performance-of-mathematics-students-in-tertiary-institutions-in-ekiti-state-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92793.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">184</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=academic%20performance%20prediction%20system&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=academic%20performance%20prediction%20system&page=3">3</a></li> <li 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