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 favored 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 74% of the datasets.
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Esmeraldo, G. et al. (2022). Using Genetic Programming and Linear Regression for Academic Performance Analysis. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_30
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DOI: https://doi.org/10.1007/978-3-031-11647-6_30
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