tukey depth
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2021 ◽  
Vol 409 ◽  
pp. 126409
Author(s):  
J. Orestes Cerdeira ◽  
Pedro C. Silva
Keyword(s):  

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2677
Author(s):  
Alicia Nieto-Reyes ◽  
Rafael Duque ◽  
Giacomo Francisci

The objective of this work is to present a methodology that automates the prediction of students’ academic performance at the end of the course using data recorded in the first tasks of the academic year. Analyzing early student records is helpful in predicting their later results; which is useful, for instance, for an early intervention. With this aim, we propose a methodology based on the random Tukey depth and a non-parametric kernel. This methodology allows teachers and evaluators to define the variables that they consider most appropriate to measure those aspects related to the academic performance of students. The methodology is applied to a real case study obtaining a success rate in the predictions of over the 80%. The case study was carried out in the field of Human-computer Interaction.The results indicate that the methodology could be of special interest to develop software systems that process the data generated by computer-supported learning systems and to warn the teacher of the need to adopt intervention mechanisms when low academic performance is predicted.


2021 ◽  
pp. 108339
Author(s):  
Hugo Lewi ◽  
Anis Yazidi ◽  
Håvard Rue

2018 ◽  
Vol 9 (12) ◽  
pp. 2349-2361 ◽  
Author(s):  
Jorge Orestes Cerdeira ◽  
Tiago Monteiro‐Henriques ◽  
Maria João Martins ◽  
Pedro C. Silva ◽  
Diogo Alagador ◽  
...  
Keyword(s):  

2016 ◽  
Vol 32 (4) ◽  
pp. 1395-1410 ◽  
Author(s):  
Xiaohui Liu

Statistics ◽  
2016 ◽  
Vol 50 (5) ◽  
pp. 1075-1085 ◽  
Author(s):  
Irène Gijbels ◽  
Stanislav Nagy
Keyword(s):  

2013 ◽  
Vol 46 (5) ◽  
pp. 566-573 ◽  
Author(s):  
Dan Chen ◽  
Pat Morin ◽  
Uli Wagner

2008 ◽  
Vol 78 (15) ◽  
pp. 2308-2313 ◽  
Author(s):  
Abdelhamid Hassairi ◽  
Ons Regaieg

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