2014
DOI: 10.1145/2557833.2557842
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Abstract: Educational data mining is a new discipline, which aims at extracting useful information and thus knowledge from huge data sets present at Educational Institutions. The main aim for such a discipline is to improve the quality of education by analyzing every parameter that is related to it. This is a Non-Linear Problem. Machine Learning provides various algorithms and approaches to deal with problems related to determining education quality. For the present study, a prediction model based on the Radial Basis Fu… Show more

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Cited by 16 publications
(4 citation statements)
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“…Quite a lot of attempts have been made to predict the results of introductory programming courses using student background data, high school grades (Costa et al, 2017;Khan et al, 2019;Sivasakthi, 2017), grades in related subjects (Arora et al, 2014), but predictions have also been made based on the programming process. Castro-Wunsch et al ( 2017) developed a model that predicts which students need help.…”
Section: Programming Process Analysismentioning
confidence: 99%
“…Quite a lot of attempts have been made to predict the results of introductory programming courses using student background data, high school grades (Costa et al, 2017;Khan et al, 2019;Sivasakthi, 2017), grades in related subjects (Arora et al, 2014), but predictions have also been made based on the programming process. Castro-Wunsch et al ( 2017) developed a model that predicts which students need help.…”
Section: Programming Process Analysismentioning
confidence: 99%
“…To carry out this prediction, the authors used an ANN having as inputs several variables such as the characteristics of the wheels and rails, train speed, yaw angle, etc. Other areas have also been targets of prediction studies using ANNs such as turbine operation [10], education [37], entertainment [38] and production [39].…”
Section: Related Workmentioning
confidence: 99%
“…The conventional statistical, as well as data mining approaches, usually lack a defined paradigm for enhancing performance prediction 9,10 . Statistical models 11 like logistic regression and linear regression, for example, include constraints for a priori regression 12 structures and data distribution. If the underlying premises of regression 13 ) methods are violated, 14 poor estimates and incorrect inferences will result, and it is challenging for end‐users to identify these kinds of breaches.…”
Section: Introductionmentioning
confidence: 99%