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Data were collected from a convenience sample of 605 students from a private university in Mexico with the eComplexity instrument. In this study, we consider the following data analyses: 1) predict students\u2019 gender based on their perception of complex thinking competency and sub-competencies from a 25 items questionnaire, 2) analyze models\u2019 performance during training and testing stages, and 3) study the models\u2019 prediction bias through a confusion matrix analysis. Our results confirm the hypothesis that the four machine learning models (Random Forest, Support Vector Machines, Multi-layer Perception, and One-Dimensional Convolutional Neural Network) can find sufficient differences in the eComplexity data to classify correctly up to 96.94% and 82.14% of the students\u2019 gender in the training and testing stage, respectively. The confusion matrix analysis revealed partiality in gender prediction among all machine learning models, even though we have applied an oversampling method to reduce the imbalance dataset. It showed that the most frequent error was to predict Male students as Female class. This paper provides empirical support for analyzing perception data through machine learning models in survey research. This work proposed a novel educational practice based on developing complex thinking competency and machine learning models to facilitate educational itineraries adapted to the training needs of each group to reduce social gaps existing due to gender.<\/jats:p>","DOI":"10.1007\/s10639-023-11831-4","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T06:02:41Z","timestamp":1686636161000},"page":"2721-2739","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Gender prediction based on University students\u2019 complex thinking competency: An analysis from machine learning approaches"],"prefix":"10.1007","volume":"29","author":[{"given":"Gerardo","family":"Ibarra-Vazquez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mar\u00eda Soledad","family":"Ram\u00ed\u00adrez-Montoya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hugo","family":"Terashima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,13]]},"reference":[{"key":"11831_CR1","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/s10758-019-09408-7","volume":"24","author":"A Abu Saa","year":"2019","unstructured":"Abu Saa, A., Al-Emran, M., & Shaalan, K. 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