Abstract

DUQUE HERNANDEZ, Jonathan Isael; RODRIGUEZ-CHAVEZ, Mario Humberto  and  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 Jan 31, 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.

Keywords : characterization of learning; algorithms; learning objects; cognitive level; model.

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