Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/11328/3050
Título : Predicting students' performance using survey data
Autor : Felix, Catarina
Sobral, Sónia Rolland
Palabras clave : Student profiling
Student performance
Machine learning
Educational data mining
Fecha de publicación : 2020
Citación : Felix, C., & Sobral, S. R. (2020). Predicting students' performance using survey data. In Proceedings of the EDUCON2020 – IEEE Global Engineering Education Conference, Porto, Portugal, 27-30 April 2020. Disponível no Repositório UPT, http://hdl.handle.net/11328/3050
Resumen : The acquisition of competences for the development of computer programs is one of the main challenges faced by computer science students. As a result of not being able to develop the abilities needed (for example, abstraction), students drop out the subjects and sometimes even the course. There is a need to study the causes of student success (or failure) in introductory curricular units to check for behaviours or characteristics that may be determinant and thus try to prevent and change said causes. The students of one programming curricular unit were invited to answer four surveys. We use machine learning techniques to try to predict the students’ grades based on the answers obtained on the surveys. The results obtained enable us to plan the semester accordingly, by anticipating how many students might need extra support. We hope to increase the students’ motivation and, with this, increase their interest on the subject. This way we aim to accomplish our ultimate goal: reducing the drop out and increasing the overall average student performance.
URI : http://hdl.handle.net/11328/3050
Aparece en las colecciones: REMIT - Resumos em Livros de Atas / Abstracts in Proceedings

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