Development of predictive model in education system

Data Mining for Student Outcome Prediction on Moodle: a systematic mapping

“…As EDM is still a new research domain, the great majority of the papers present data mining analysis of specific situations, experimenting with different types of methods [Hung et al 2016], [Zorrilla and Garcia-Saiz 2014], [Hu et al 2014], [Márquez-Vera et al 2013], [Romero et al 2013a], [Jovanovic et al 2012], [Kotsiantis 2012], [Minaei-Bidgoli et al 2003], [Thai-Nghe et al 2009], [Pardos et al 2012], [Romero et al 2008], [Sharma and Mavani 2011a] Neural Network [Gamulin et al 2016], [Cambruzzi et al 2015], [Shana and Abdulla 2015], [Sorour et al 2014], [Romero et al 2013a], [Kotsiantis 2012], [Sharma and Mavani 2011a], [Lykourentzou et al 2009b], [Lykourentzou et al 2009a] Bayesian Classification [Gamulin et al 2016], [Zorrilla and Garcia-Saiz 2014], [Romero et al 2013b], [Sharma and Mavani 2011b], [Sharma and Mavani 2011a], [Kotsiantis et al 2010], [Thai-Nghe et al 2009], [ Minaei-Bidgoli et al 2003] Support Vector Machines [Gamulin et al 2016], [Kotsiantis 2012], [Lykourentzou et al 2009b], [Thai-Nghe et al 2009] Genetic Algorithm [Márquez-Vera et al 2016], [Xing et al 2015], [Romero et al 2013a], [Zafra and Ventura 2012], [Zafra et al 2011], [Zafra and Ventura 2009],…”

“…Forums participation [Dascalu et al 2016], [Hung et al 2016], [Gasevic et al 2016], [Neto and Castro 2015], [Cambruzzi et al 2015], [Hu et al 2014], [Romero et al 2013b], [Romero et al 2013a], [Zafra and Ventura 2012], [López et al 2012], [Jovanovic et al 2012], [Mogus et al 2012], [Zafra et al 2011], [Obadi et al 2010], [Carmona et al 2010], [Zafra and Ventura 2009], Assessment data/grades [Kostopoulos et al 2015], ], [Lykourentzou et al 2009b], [Hu et al 2014], [Gasevic et al 2016], [You 2016], [Kotsiantis 2012], [Moradi et al 2014], [Jovanovic et al 2012], [Romero et al 2013a], [Černezel et al 2014], [Pardos et al 2012], [Carmona et al 2010], [Hung et al 2016] Interaction logs [Joksimović et al 2015], [Xing et al 2015], [Kotsiantis et al 2010], [Zacharis 2015], [You 2016], [Zorrilla and Garcia-Saiz 2014], [Cambruzzi et al 2015], [Gamulin et al 2016], [Sharma and Mavani 2011a], [Sorour et al 2014], [Romero et al 2008], [Sharma and Mavani 2011b] Quizzes data [Kato and Ishikawa 2013],…”

Evaluation metrics and dimensional reduction for binary classification algorithms: a case study on bankruptcy prediction

“…Those that have been widely used in bankruptcy prediction by different authors are compared in the study done by Olson et al (2012) and Barboza et al (2017). The final list of the algorithms that have been considered in this study are: gradient boosting (GB) which builds the model in a stepwise and generalizes the model by allowing arbitrary optimization of a differentiable loss function Zięba et al (2016), Gaussian Naive Bayes (NB) which is based on the Bayes theorem Eirola et al (2015) Sharma and Mavani (2011), decision tree classifier which is based on a decision tree, is a predictive model that maps observations about an item to conclusions about the target value of the item Foroghi et al (2011), random forest (RF) which consists on a combination of predictor trees such that each tree depends on the values of a random vector tested independently and with the same distribution for each predictor tree, K-nearest neighbors (KNNs) which seeks out the closest observations that are trying to be predicted and classifies the point of interest based on the most data surrounding it, Imandoust and Bolandraftar (2013) and support vector machine classifier (SVMC) which is based on the hyperplane concept Hsu et al (2003). To overcome the potential bias when training the algorithms, fivefold cross-validation was used.The cross-validation technique has made it possible to reduce the problems of overfitting and also evaluate the results of the analysis while ensuring that they are independent of the partitioning between training and test data.…”