Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data

Márquez-Vera, Carlos; Cano, Alberto; Romero, Cristóbal; Ventura, Sebastián

doi:10.1007/s10489-012-0374-8

Cited by 176 publications

(82 citation statements)

References 20 publications

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“…Such methods are very important to clearly see which attributes lead to that behavior [Roberto and Adeodato 2012, Balaniuk et al 2011, Tamhane et al 2014, Márquez-Vera et al 2013, Lakkaraju et al 2015, Costa et al 2015, de Brito et al 2014, Maria et al 2016, Kantorski et al 2016, Pascoal et al 2016.…”

Section: Related Workmentioning

confidence: 99%

A machine learning approach to identify and prioritize college students at risk of dropping out

Barbosa

Santos

Pordeus

2017

Anais Do XXVIII Simpósio Brasileiro De Informática Na Educação (SBIE 2017)

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Abstract. In this paper, we present a student dropout prediction strategy based on the classification with reject option paradigm. In such strategy, our method classifies students into dropout prone or non-dropout prone classes and may also reject classifying students when the algorithm does not provide a reliable prediction. The rejected students are the ones that could be classified into either class, and so are probably the ones with more chances of success when subjected to personalized intervention activities. In the proposed method, the reject zone can be adjusted so that the number of rejected students can meet the available workforce of the educational institution. Our method was tested on a dataset collected from 892 undergraduate students from 2005 to 2016.

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Section: Related Workmentioning

confidence: 99%

A machine learning approach to identify and prioritize college students at risk of dropping out

Barbosa

Santos

Pordeus

2017

Anais Do XXVIII Simpósio Brasileiro De Informática Na Educação (SBIE 2017)

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“…All continuous features, except for final grades, were discretized to provide a more comprehensible view of the data with the help of the entropy-MDL algorithm (Márquez-Vera et al, 2013). Entropy-MDL is a class-aware discretization introduced by Fayyad and Irani (1992) that uses MDL and entropy to find the best cut-off points.…”

Section: Preprocessingmentioning

confidence: 99%

Modeling Students’ Academic Performance Based on Their Interactions with the Online Learning Environment

Akçapınar¹,

Altun²,

Aşkar³

2015

İlköğretim Online

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ABSTRACT. The aim of this study is to model students' academic performance based on their interactions in an online learning environment. The dataset includes 10 input attributes extracted from students' learning interaction data. As an output (class) variable, the final grades obtained from their Computer Hardware course were used. The modeling performance of three different classification algorithms were tested (naïve Bayes classifier, classification tree and CN2 rules) on the dataset. All analyses were performed using the Orange data mining tool, and the models were evaluated using ten-fold cross-validation. The results of analysis were presented as a confusion matrix, a decision tree, and if-then rules. The predictive performance of the algorithms was also tested and compared using the classification accuracy (CA), and area under the ROC Curve (AUC) metrics. The experimental results indicate that the naïve Bayes algorithm outperforms other classification algorithms when compared using the CA and AUC metrics. The naïve Bayes algorithm correctly classified 75.4% of the students according to their grade for the course (Fail, Pass, and Good). The classification model also accurately predicted 81.5% of the students who failed, and 91.8% of the students who passed the course. On the other hand, the classification tree and the CN2 algorithms generated models which can be used with confidence in decision making processes by non-expert data mining users.

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“…However, the biggest limitation of most of these emerging works is that they focus solely on discussion forum behavior or video lecture activity, but do not fuse and take them into account. Some of these works have grown out of research on predicting academic progress of students and identifying students those who are at dropout risk (Kotsiantis et al, 2003;Dekker et al, 2009;Pal, 2012;Márquez-Vera et al, 2013;Manhaes et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs

2014

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Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data

Cited by 176 publications

References 20 publications

A machine learning approach to identify and prioritize college students at risk of dropping out

A machine learning approach to identify and prioritize college students at risk of dropping out

Modeling Students’ Academic Performance Based on Their Interactions with the Online Learning Environment

Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs

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