Using data mining to predict secondary school student performance

By Paulo Cortez, Alice Maria Gonçalves Silva. 2008

773 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Aaron Roth
2. Michael Kearns
3. Seth Neel
4. Zhiwei Steven Wu
5. Dimitris Papailiopoulos
6. Kangwook Lee
7. Michael Gira
8. Vitaly Feldman

Citations in the corpus

• An Empirical Study of Rich Subgroup Fairness for Machine Learning (2018, methodology): “knesses. 3.1 Datasets We ran experiments on 3 datasets from the UCI Machine Learning RepositoryDheeru and Karra Taniskidou [2017]: Communities and Crime [Redmond and Baveja,2002], Adult, and Student [Cortez and Silva, 2008], and the Law School dataset from the Law School Admission Council’s National”
• Optimal Generalized Decision Trees via Integer Programming (2016, background): “By “math scores”, we are referring to the Student Alcohol Consumption dataset, for predicting Portuguese secondary school students’ failure of a mathematics course [5].”
• Generalized Inverse Classification (2016, background): “Performance is initially represented by 45 features, including a unique identifier (discarded) and class variable y , which we define to be whether or not a student’s final grade was above a C ( y = 0) or, conversely, less than or equal to a C ( y = 1).”
• LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks (2022, methodology): “We also use four real datasets: Medical Insurance (Insurance) [41], Combined Cycle Power Plant (CCPP) [42], Servo [43], and Student Performance (Student) [44].”
• Towards Robust and Reliable Algorithmic Recourse (2021, background): “Our last dataset contains student performance records of 649 students from two Portuguese secondary schools, Gabriel Pereira (GP) and Mousinho da Silveira (MS) [8, 6], and captures geospatial shift.”