Using Genetic Programming and Linear Regression for Academic Performance Analysis

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{esmeraldo:2022:AIEPLBR,

• author = "Guilherme Esmeraldo and Robson Feitosa and Cicero Samuel Mendes and Cicero Carlos Oliveira and Esdras {Bispo Junior} and Allan Carlos {de Sousa} and Gustavo Campos",
• title = "Using Genetic Programming and Linear Regression for Academic Performance Analysis",
• booktitle = "Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners' and Doctoral Consortium",
• year = "2022",
• editor = "Maria Mercedes Rodrigo and Noburu Matsuda and Alexandra I. Cristea and Vania Dimitrova",
• volume = "13356",
• series = "LNCS",
• pages = "174--179",
• address = "Durham, UK",
• month = jul # " 27-31",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Academic performance analysis, Linear regression",
• isbn13 = "978-3-031-11647-6",
• URL = "http://link.springer.com/chapter/10.1007/978-3-031-11647-6_30",
• DOI = "doi:10.1007/978-3-031-11647-6_30",
• abstract = "The academic evaluation process, even today, is the subject of much discussion. This process can use quantitative analysis to indicate the level of learning of students to support the decision about whether the student can attend the next curriculum phase. From this context, this paper analyzes the history of students grades in the 1st year of a technical course in informatics integrated to high school, for the years 2020 and 2021, through the linear regression method, supported by genetic programming, to find out the influence of the grades of the first two bimesters concerning the final grade. The main results show that the genetic programming algorithm favoured the search for linear regression models with a good fit to the datasets with students data. The resultant models proved accurate and explained more than 74percent of the datasets.",
• notes = "AIED 2022 https://aied2022.webspace.durham.ac.uk/",

}

Genetic Programming entries for Guilherme Esmeraldo Robson Feitosa Cicero Samuel Mendes Cicero Carlos Oliveira Esdras Bispo Junior Allan Carlos de Sousa Gustavo Campos

Citations