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Dilemas contemporáneos: educación, política y valores
versão On-line ISSN 2007-7890
Resumo
DUQUE HERNANDEZ, Jonathan Isael; RODRIGUEZ-CHAVEZ, Mario Humberto e POLANCO-MARTAGON, Said. Characterization of algorithm learning through data mining in the higher level. Dilemas contemp. educ. política valores [online]. 2021, vol.9, n.spe1, 00019. Epub 31-Jan-2022. ISSN 2007-7890. https://doi.org/10.46377/dilemas.v9i.2925.
This research focuses on the development of a learning characterization model for university students in the programming area, based on a cognitive level taxonomy of (Marzano, R. J., 2001). A compilation of questionnaire responses obtained from the students was carried out for data treatment and analysis; in this way, obtain the necessary information and characterize the student's learning, identifying their strengths and weaknesses. After performing tests, 5 groupings were obtained that represent the cognitive levels, where 29% represent the largest population in the first level and 13% are in the second level as the smallest population.
Palavras-chave : characterization of learning; algorithms; learning objects; cognitive level; model.